254 Commits

Author SHA1 Message Date
ed bd2a79c090 feat(logging): Implement LogPruner for cleaning up old insignificant logs 2026-02-26 08:59:39 -05:00
ed 3f4dc1ae03 feat(logging): Implement session-based log organization 2026-02-26 08:55:16 -05:00
ed 10fbfd0f54 feat(logging): Implement LogRegistry for managing session metadata 2026-02-26 08:52:51 -05:00
ed 9a66b7697e chore(conductor): Add new track 'Review logging used throughout the project' 2026-02-26 08:46:25 -05:00
ed b9b90ba9e7 remove mma_utilization_refinement_20260226 from tracks 2026-02-26 08:38:55 -05:00
ed 4374b91fd1 chore(conductor): Archive track 'MMA Utilization Refinement' 2026-02-26 08:38:42 -05:00
ed a664dfbbec fix(mma): Final refinement of delegation command and log tracking 2026-02-26 08:38:10 -05:00
ed 1933fcfb40 conductor(plan): Mark task 'Apply review suggestions' as complete 2026-02-26 08:36:05 -05:00
ed d343066435 fix(conductor): Apply review suggestions for track 'mma_utilization_refinement_20260226' 2026-02-26 08:35:50 -05:00
ed 91693a5168 feat(mma): Refine tier roles, tool access, and observability 2026-02-26 08:31:19 -05:00
ed 732f3d4e13 chore(conductor): Mark track 'MMA Utilization Refinement' as complete 2026-02-26 08:30:52 -05:00
ed e950601e28 chore(conductor): Add new track 'MMA Utilization Refinement' 2026-02-26 08:24:13 -05:00
ed 18e6fab307 checkpoint: gemini_cli_parity track 2026-02-26 00:32:21 -05:00
ed a70680b2a2 checkpoint: Working on getting gemini cli to actually have parity with gemini api. 2026-02-26 00:31:33 -05:00
ed cbe359b1a5 archive deepseek support (remove in tracks) 2026-02-25 23:35:03 -05:00
ed d030897520 chore(conductor): Archive track 'Add support for the deepseek api as a provider.' 2026-02-25 23:34:46 -05:00
ed f2b29a06d5 chore(conductor): Mark track 'Add support for the deepseek api as a provider.' as complete 2026-02-25 23:34:06 -05:00
ed 95cac4e831 feat(ai): implement DeepSeek provider with streaming and reasoning support 2026-02-25 23:32:08 -05:00
ed 3a2856b27d pain 2026-02-25 23:11:42 -05:00
ed 7bbc484053 docs(conductor): Synchronize docs for track 'deepseek_support_20260225' (Phase 1) 2026-02-25 22:37:56 -05:00
ed 45b88728f3 conductor(plan): Mark Phase 1 of DeepSeek track as complete [checkpoint: 0ec3720] 2026-02-25 22:37:14 -05:00
ed 0ec372051a conductor(checkpoint): Checkpoint end of Phase 1 (Infrastructure & Common Logic) 2026-02-25 22:37:01 -05:00
ed 75bf912f60 conductor(plan): Mark Phase 1 of DeepSeek track as verified 2026-02-25 22:36:57 -05:00
ed 1b3ff232c4 feat(deepseek): Implement Phase 1 infrastructure and provider interface 2026-02-25 22:33:20 -05:00
ed f0c1af986d mma docs support 2026-02-25 22:29:20 -05:00
ed 74dcd89ec5 mma execution fix 2026-02-25 22:26:59 -05:00
ed d82c7686f7 skill fixes 2026-02-25 22:14:13 -05:00
ed 8abf5e07b9 chore(conductor): Archive track 'test_curation_20260225' 2026-02-25 22:06:20 -05:00
ed e596a1407f conductor(plan): Mark task 'Apply review suggestions' as complete 2026-02-25 22:05:52 -05:00
ed c23966061c fix(conductor): Apply review suggestions for track 'test_curation_20260225' 2026-02-25 22:05:28 -05:00
ed 56025a84e9 checkpoint: finished test curation 2026-02-25 21:58:18 -05:00
ed e0b9ab997a chore(conductor): Mark track 'Test Suite Curation and Organization' as complete 2026-02-25 21:56:03 -05:00
ed aea42e82ab fixes to mma skills 2026-02-25 21:12:10 -05:00
ed 6152b63578 chore(conductor): Checkpoint Phase 2: Manifest and Tooling for test curation track 2026-02-25 21:05:00 -05:00
ed 26502df891 conductor(plan): Mark phase 'Research and Inventory' as complete 2026-02-25 20:52:53 -05:00
ed be689ad1e9 chore(conductor): Checkpoint Phase 1: Research and Inventory for test curation track 2026-02-25 20:52:45 -05:00
ed edae93498d chore(conductor): Add new track 'Test Suite Curation and Organization' 2026-02-25 20:42:43 -05:00
ed 3a6a53d046 chore(conductor): Archive track 'mma_formalization_20260225' 2026-02-25 20:37:04 -05:00
ed c2ab18164e checkpoint on mma overhaul 2026-02-25 20:30:34 -05:00
ed df74d37fd0 docs(conductor): Synchronize docs for track 'mma_formalization_20260225' 2026-02-25 20:28:43 -05:00
ed 2f2f73cbb3 chore(conductor): Mark track 'mma_formalization_20260225' as complete 2026-02-25 20:26:26 -05:00
ed 88712ed328 conductor(plan): Mark track 'mma_formalization_20260225' as complete 2026-02-25 20:26:15 -05:00
ed 0d533ec11e conductor(checkpoint): Checkpoint end of Phase 4 2026-02-25 20:26:03 -05:00
ed 95955a2792 conductor(plan): Mark Phase 4 final verification as complete 2026-02-25 20:25:57 -05:00
ed eea3da805e conductor(plan): Mark helper task as complete 2026-02-25 20:24:36 -05:00
ed df1c429631 feat(mma): Add mma.ps1 helper script for manual triggering 2026-02-25 20:24:26 -05:00
ed 55b8288b98 conductor(plan): Mark workflow update as complete 2026-02-25 20:23:34 -05:00
ed 5e256d1c12 docs(conductor): Update workflow with mma-exec and 4-tier model definitions 2026-02-25 20:23:25 -05:00
ed 6710b58d25 conductor(plan): Mark Phase 3 as complete 2026-02-25 20:21:54 -05:00
ed eb64e52134 conductor(checkpoint): Checkpoint end of Phase 3 2026-02-25 20:21:29 -05:00
ed 221374eed6 feat(mma): Complete Phase 3 context features (injection, dependency mapping, logging) 2026-02-25 20:21:12 -05:00
ed 9c229e14fd conductor(plan): Mark task 'Implement logging' as complete 2026-02-25 20:17:24 -05:00
ed 678fa89747 feat(mma): Implement logging/auditing for role hand-offs 2026-02-25 20:16:56 -05:00
ed 25b904b404 conductor(plan): Mark task 'dependency mapping' as complete 2026-02-25 20:12:46 -05:00
ed 32ec14f5c3 feat(mma): Add dependency mapping to mma-exec 2026-02-25 20:12:14 -05:00
ed 4e564aad79 feat(mma): Implement AST Skeleton View generator using tree-sitter 2026-02-25 20:08:43 -05:00
ed da689da4d9 conductor(plan): Update Phase 2 checkpoint with model fixes 2026-02-25 19:58:13 -05:00
ed dd7e591cb8 conductor(checkpoint): Checkpoint end of Phase 2 (Amended) 2026-02-25 19:57:56 -05:00
ed 794cc2a7f2 fix(mma): Fix tier 2 model name to valid preview model and adjust tests 2026-02-25 19:57:42 -05:00
ed 9da08e9c42 fix(mma): Adjust skill trigger format to avoid policy blocks 2026-02-25 19:54:45 -05:00
ed be2a77cc79 fix(mma): Assign dedicated models per tier in execute_agent 2026-02-25 19:51:00 -05:00
ed 00fbf5c44e conductor(plan): Mark phase 'Phase 2: mma-exec CLI - Core Scoping' as complete 2026-02-25 19:46:47 -05:00
ed 01953294cd conductor(checkpoint): Checkpoint end of Phase 2 2026-02-25 19:46:31 -05:00
ed 8e7bbe51c8 conductor(plan): Update context amnesia task commit hash 2026-02-25 19:46:24 -05:00
ed f6e6d418f6 fix(mma): Use headless execution flag for context amnesia and parse json output 2026-02-25 19:45:59 -05:00
ed 7273e3f718 conductor(plan): Skip ai_client integration for mma-exec 2026-02-25 19:25:25 -05:00
ed bbcbaecd22 conductor(plan): Mark task 'Context Amnesia bridge' as complete 2026-02-25 19:17:04 -05:00
ed 9a27a80d65 feat(mma): Implement Context Amnesia bridge via subprocess 2026-02-25 19:16:41 -05:00
ed facfa070bb conductor(plan): Mark task 'Implement Role-Scoped Document selection logic' as complete 2026-02-25 19:12:20 -05:00
ed 55c0fd1c52 feat(mma): Implement Role-Scoped Document selection logic 2026-02-25 19:12:02 -05:00
ed 067cfba7f3 conductor(plan): Mark task 'Scaffold mma_exec.py' as complete 2026-02-25 19:09:33 -05:00
ed 0b2cd324e5 feat(mma): Scaffold mma_exec.py with basic CLI structure 2026-02-25 19:09:14 -05:00
ed 0d7530e33c conductor(plan): Mark phase 'Phase 1: Tiered Skills Implementation' as complete 2026-02-25 19:07:09 -05:00
ed 6ce3ea784d conductor(checkpoint): Checkpoint end of Phase 1 2026-02-25 19:06:50 -05:00
ed c6a04d8833 conductor(plan): Mark skills creation tasks as complete 2026-02-25 19:05:38 -05:00
ed fe1862af85 feat(mma): Add 4-tier skill templates 2026-02-25 19:05:14 -05:00
ed f728274764 checkpoint: fix regression when using gemini cli outside of manual slop. 2026-02-25 19:01:42 -05:00
ed fcb83e620c chore(conductor): Add new track '4-Tier MMA Architecture Formalization' 2026-02-25 18:49:58 -05:00
ed d030bb6268 chore(conductor): Add new track 'DeepSeek API Support' 2026-02-25 18:44:38 -05:00
ed b6496ac169 chore(conductor): Add new track 'Gemini CLI Parity' 2026-02-25 18:42:40 -05:00
ed 94e41d20ff chore(conductor): Archive gemini_cli_headless_20260224 track and update tests 2026-02-25 18:39:36 -05:00
ed 1c78febd16 chore(conductor): Mark track 'Support gemini cli headless' as complete 2026-02-25 14:30:43 -05:00
ed f4dd7af283 chore(conductor): final update to Gemini CLI implementation plan 2026-02-25 14:30:37 -05:00
ed 1e5b43ebcd feat(ai): finalize Gemini CLI integration with telemetry polish and cleanup 2026-02-25 14:30:21 -05:00
ed d187a6c8d9 feat(ai): support stdin for Gemini CLI and verify with integration test 2026-02-25 14:23:20 -05:00
ed 3ce4fa0c07 feat(gui): support Gemini CLI provider and settings persistence 2026-02-25 14:06:14 -05:00
ed b762a80482 feat(ai): integrate GeminiCliAdapter into ai_client 2026-02-25 14:02:06 -05:00
ed 211000c926 feat(ipc): implement cli_tool_bridge as BeforeTool hook 2026-02-25 13:53:57 -05:00
ed 217b0e6d00 conductor(plan): mark Phase 1 of Gemini CLI headless integration as complete 2026-02-25 13:45:44 -05:00
ed c0bccce539 conductor(checkpoint): Checkpoint end of Phase 1 2026-02-25 13:45:22 -05:00
ed 93f640dc79 feat(ipc): add request_confirmation to ApiHookClient 2026-02-25 13:44:44 -05:00
ed 1792107412 feat(ipc): support synchronous 'ask' requests in api_hooks 2026-02-25 13:41:25 -05:00
ed 147c10d4bb chore(conductor): Archive track 'manual_slop_headless_20260225' 2026-02-25 13:34:32 -05:00
ed 05a8d9d6d6 conductor(plan): Mark task 'Apply review suggestions' as complete 2026-02-25 13:34:05 -05:00
ed 9b50bfa75e fix(headless): Apply review suggestions for track 'manual_slop_headless_20260225' 2026-02-25 13:33:59 -05:00
ed 63fd391dff chore(conductor): Integrate strict MMA token firewalling and tiered delegation into core workflow 2026-02-25 13:29:16 -05:00
ed 6eb88a4041 docs(conductor): Synchronize docs for track 'Support headless manual_slop' 2026-02-25 13:24:09 -05:00
ed 28fcaa7eae chore(conductor): Mark track 'Support headless manual_slop' as complete 2026-02-25 13:23:11 -05:00
ed 386e36a92b feat(headless): Implement Phase 5 - Dockerization 2026-02-25 13:23:04 -05:00
ed 1491619310 feat(headless): Implement Phase 4 - Session & Context Management via API 2026-02-25 13:18:41 -05:00
ed 4e0bcd5188 feat(headless): Implement Phase 2 - Core API Routes & Authentication 2026-02-25 13:09:22 -05:00
ed d5f056c3d1 feat(headless): Implement Phase 1 - Project Setup & Headless Scaffold 2026-02-25 13:03:11 -05:00
ed 33a603c0c5 pain 2026-02-25 12:53:04 -05:00
ed 0b4e197d48 checkpoint, mma condcutor pain 2026-02-25 12:47:21 -05:00
ed 89636eee92 conductor(plan): mark task 'Update dependencies' as complete 2026-02-25 12:41:12 -05:00
ed 02fc847166 feat(headless): add fastapi and uvicorn dependencies 2026-02-25 12:41:01 -05:00
ed b66da31dd0 chore(conductor): Add new track 'manual_slop_headless_20260225' 2026-02-25 12:36:42 -05:00
ed f775659cc5 checkpoint rem mma_verification from tracks 2026-02-25 09:26:44 -05:00
ed 96e40f056e chore(conductor): Archive verified MMA tracks 2026-02-25 09:26:27 -05:00
ed 3f9c6fc6aa chore(conductor): Fix SKILL.md and documentation typos to correctly use the new Role-Based sub-agent protocol 2026-02-25 09:15:25 -05:00
ed e60eef5df8 docs(conductor): Synchronize docs for track 'MMA Tiered Architecture Verification' 2026-02-25 09:02:40 -05:00
ed fd1e5019ea chore(conductor): Mark track 'MMA Tiered Architecture Verification' as complete 2026-02-25 09:00:58 -05:00
ed 551e41c27f conductor(checkpoint): Phase 4: Final Validation and Reporting complete 2026-02-25 08:59:20 -05:00
ed 3378fc51b3 conductor(plan): Mark phase 'Test Track Implementation' as complete 2026-02-25 08:55:45 -05:00
ed 4eb4e8667c conductor(checkpoint): Phase 3: Test Track Implementation complete 2026-02-25 08:55:32 -05:00
ed 743a0e380c conductor(plan): Mark phase 'Infrastructure Verification' as complete 2026-02-25 08:51:17 -05:00
ed 1edf3a4b00 conductor(checkpoint): Phase 2: Infrastructure Verification complete 2026-02-25 08:51:05 -05:00
ed a3cb12b1eb conductor(plan): Mark phase 'Research and Investigation' as complete 2026-02-25 08:45:53 -05:00
ed cf3de845fb conductor(checkpoint): Phase 1: Research and Investigation complete 2026-02-25 08:45:41 -05:00
ed 4a74487e06 chore(conductor): Add new track 'MMA Tiered Architecture Verification' 2026-02-25 08:38:52 -05:00
ed 05ad580bc1 chore(conductor): Archive track 'gui_sim_extension_20260224' 2026-02-25 01:45:27 -05:00
ed c952d2f67b feat(testing): stabilize simulation suite and fix gemini caching 2026-02-25 01:44:46 -05:00
ed fb80ce8c5a feat(gui): Add auto-scroll, blinking history, and reactive API events 2026-02-25 00:41:45 -05:00
ed 3113e3c103 docs(conductor): Synchronize docs for track 'extend test simulation' 2026-02-25 00:01:07 -05:00
ed 602f52055c chore(conductor): Mark track 'extend test simulation' as complete 2026-02-25 00:00:45 -05:00
ed 84bbbf2c89 conductor(plan): Mark phase 'Phase 4: Execution and Modals Simulation' as complete 2026-02-25 00:00:37 -05:00
ed e8959bf032 conductor(checkpoint): Phase 4: Execution and Modals Simulation complete 2026-02-25 00:00:28 -05:00
ed 536f8b4f32 conductor(plan): Mark phase 'Phase 3: AI Settings and Tools Simulation' as complete 2026-02-24 23:59:11 -05:00
ed 760eec208e conductor(checkpoint): Phase 3: AI Settings and Tools Simulation complete 2026-02-24 23:59:01 -05:00
ed 88edb80f2c conductor(plan): Mark phase 'Phase 2: Context and Chat Simulation' as complete 2026-02-24 23:57:40 -05:00
ed a77d0e70f2 conductor(checkpoint): Phase 2: Context and Chat Simulation complete 2026-02-24 23:57:31 -05:00
ed f7cfd6c11b conductor(plan): Mark phase 'Phase 1: Setup and Architecture' as complete 2026-02-24 23:54:24 -05:00
ed b255d4b935 conductor(checkpoint): Phase 1: Setup and Architecture complete 2026-02-24 23:54:15 -05:00
ed 5dc286ffd3 chore(conductor): Add new track 'Gemini CLI Headless Integration' 2026-02-24 23:46:56 -05:00
ed bab468fc82 fix(conductor): Enforce strict statelessness and robust JSON parsing for subagents 2026-02-24 23:36:41 -05:00
ed 462ed2266a feat(conductor): Add run_subagent script for stable headless skill invocation 2026-02-24 23:17:45 -05:00
ed 0080ceb397 docs(conductor): Add MMA_Support as the fallback source of truth to the core engine track 2026-02-24 23:03:14 -05:00
ed 45abcbb1b9 feat(conductor): Consolidate MMA implementation into single multi-phase track and draft Agent Skill 2026-02-24 22:57:28 -05:00
ed 10c5705748 docs(conductor): Add Token Firewalling and Model Switching Strategy 2026-02-24 22:45:17 -05:00
ed f76054b1df feat(conductor): Scaffold MMA Migration Tracks from Epics 2026-02-24 22:44:36 -05:00
ed 982fbfa1cf docs(conductor): Synchronize docs for track '4-Tier Architecture Implementation & Conductor Self-Improvement' 2026-02-24 22:39:20 -05:00
ed 25f9edbed1 chore(conductor): Mark track '4-Tier Architecture Implementation & Conductor Self-Improvement' as complete 2026-02-24 22:38:13 -05:00
ed 5c4a195505 conductor(plan): Mark phase 'Phase 2: Conductor Self-Reflection' as complete 2026-02-24 22:37:49 -05:00
ed 40339a1667 conductor(checkpoint): Checkpoint end of Phase 2: Conductor Self-Reflection & Upgrade Strategy 2026-02-24 22:37:26 -05:00
ed 8dbd6eaade conductor(plan): Mark tasks 'Multi-Model' and 'Review' as complete 2026-02-24 22:35:31 -05:00
ed f62bf3113f docs(mma): Draft Multi-Model Delegation and finish Proposal 2026-02-24 22:35:02 -05:00
ed baff5c18d3 docs(mma): Draft Execution Clutch & Linear Debug Mode section 2026-02-24 22:34:19 -05:00
ed 2647586286 conductor(plan): Mark task 'Execution Clutch' as in progress 2026-02-24 22:34:16 -05:00
ed 30574aefd1 conductor(plan): Mark task 'Draft Proposal - Memory Siloing' as complete 2026-02-24 22:33:58 -05:00
ed ae67c93015 docs(mma): Draft Memory Siloing & Token Firewalling section 2026-02-24 22:33:44 -05:00
ed c409a6d2a3 conductor(plan): Mark task 'Research Optimal Proposal Format' as complete 2026-02-24 22:33:32 -05:00
ed 0c5f8b9bfe docs(mma): Draft outline for Conductor Self-Reflection Proposal 2026-02-24 22:33:07 -05:00
ed 4a66f994ee conductor(plan): Mark task 'Research Optimal Proposal Format' as in progress 2026-02-24 22:31:57 -05:00
ed 5ea8059812 conductor(plan): Mark phase 'Phase 1: manual_slop Migration Planning' as complete 2026-02-24 22:31:41 -05:00
ed e07e8e5127 conductor(checkpoint): Checkpoint end of Phase 1: manual_slop Migration Planning 2026-02-24 22:31:19 -05:00
ed 5278c05cec conductor(plan): Mark task 'Draft Track 5' as complete 2026-02-24 22:28:41 -05:00
ed 67734c92a1 docs(mma): Draft Track 5 - UI Decoupling & Tier 1/2 Routing 2026-02-24 22:27:22 -05:00
ed a9786d4737 conductor(plan): Mark task 'Draft Track 4' as complete 2026-02-24 22:27:02 -05:00
ed 584bff9c06 docs(mma): Draft Track 4 - Tier 4 QA Interception 2026-02-24 22:26:27 -05:00
ed ac55b553b3 conductor(plan): Mark task 'Draft Track 3' as complete 2026-02-24 22:25:21 -05:00
ed aaeed92e3a docs(mma): Draft Track 3 - The Linear Orchestrator & Execution Clutch 2026-02-24 22:24:28 -05:00
ed 447a701dc4 conductor(plan): Mark task 'Draft Track 2' as complete 2026-02-24 22:18:37 -05:00
ed 1198aee36e docs(mma): Draft Track 2 - State Machine & Data Structures 2026-02-24 22:18:14 -05:00
ed 95c6f1f4b2 conductor(plan): Mark task 'Draft Track 1' as complete 2026-02-24 22:17:46 -05:00
ed bdd935ddfd docs(mma): Draft Track 1 - The Memory Foundations 2026-02-24 22:17:34 -05:00
ed 4dd4be4afb conductor(plan): Mark task 'Synthesize MMA Documentation' as complete 2026-02-24 22:17:09 -05:00
ed 46b351e945 docs(mma): Synthesize MMA Documentation constraints and takeaways 2026-02-24 22:16:44 -05:00
ed 4933a007c3 checkpoint history segregation 2026-02-24 22:14:33 -05:00
ed b2e900e77d chore(conductor): Archive track 'history_segregation' 2026-02-24 22:14:10 -05:00
ed 7c44948f33 conductor(plan): Mark task 'Apply review suggestions' as complete 2026-02-24 22:12:06 -05:00
ed 09df57df2b fix(conductor): Apply review suggestions for track 'history_segregation' 2026-02-24 22:11:50 -05:00
ed a6c9093961 chore(conductor): Mark track 'history_segregation' as complete and migrate local config 2026-02-24 22:09:21 -05:00
ed 754fbe5c30 test(integration): Verify history persistence and AI context inclusion 2026-02-24 22:06:33 -05:00
ed 7bed5efe61 feat(security): Enforce blacklist for discussion history files 2026-02-24 22:05:44 -05:00
ed ba02c8ed12 feat(project): Segregate discussion history into sibling TOML file 2026-02-24 22:04:14 -05:00
ed ea84168ada checkpoint post gui2_parity 2026-02-24 22:02:06 -05:00
ed 828f728d67 chore(conductor): Archive track 'gui2_parity_20260224' 2026-02-24 22:01:30 -05:00
ed 48b2993089 conductor(plan): Mark task 'Apply review suggestions' as complete 2026-02-24 22:01:14 -05:00
ed 6f1e00b647 fix(conductor): Apply review suggestions for track 'gui2_parity_20260224' 2026-02-24 22:01:07 -05:00
ed 95bf1cac7b chore(conductor): Mark track 'gui2_parity_20260224' as complete 2026-02-24 21:56:57 -05:00
ed f718c2288b conductor(plan): Mark track 'gui2_parity_20260224' as complete 2026-02-24 21:56:46 -05:00
ed 14984c5233 fix(gui2): Correct Response panel rendering and fix automation crashes 2026-02-24 21:56:26 -05:00
ed fb9ee27b38 conductor(plan): Mark task 'Final project-wide link validation and documentation update' as complete 2026-02-24 20:53:34 -05:00
ed 2f5cfb2fca conductor(plan): Mark task 'Final project-wide link validation and documentation update' as in-progress 2026-02-24 20:51:48 -05:00
ed d4d6e5b9ff conductor(plan): Mark task 'Update project entry point to gui_2.py' as complete 2026-02-24 20:37:37 -05:00
ed b92fa9013b docs: Update entry point to gui_2.py 2026-02-24 20:37:20 -05:00
ed 188725c412 conductor(plan): Mark task 'Rename gui.py to gui_legacy.py' as complete 2026-02-24 20:36:26 -05:00
ed c4c47b8df9 feat(gui): Rename gui.py to gui_legacy.py and update references 2026-02-24 20:36:04 -05:00
ed 76ee25b299 conductor(plan): Mark phase 'Performance Optimization and Final Validation' as complete 2026-02-24 20:25:20 -05:00
ed 611c89783f conductor(checkpoint): Checkpoint end of Phase 3 2026-02-24 20:25:02 -05:00
ed 17f179513f conductor(plan): Mark Phase 3: Performance Optimization and Final Validation as complete 2026-02-24 20:24:57 -05:00
ed d6472510ea perf(gui2): Full performance parity with gui.py (+/- 5% FPS/CPU) 2026-02-24 20:23:43 -05:00
ed d704816c4d conductor(plan): Mark task 'Optimize rendering and docking logic in gui_2.py if performance targets are not met' as in progress 2026-02-24 20:02:26 -05:00
ed 312b0ef48c conductor(plan): Mark task 'Conduct performance benchmarking (FPS, CPU, Frame Time) for both gui.py and gui_2.py' as in progress 2026-02-24 20:00:44 -05:00
ed ae9c5fa0e9 conductor(plan): Mark phase 'Visual and Functional Parity Implementation' as complete 2026-02-24 20:00:16 -05:00
ed ad84843d9e conductor(checkpoint): Checkpoint end of Phase 2 2026-02-24 19:59:54 -05:00
ed a9344adb64 conductor(plan): Mark task 'Address regressions' as complete 2026-02-24 19:45:23 -05:00
ed 2d8ee64314 chore(conductor): Mark 'Address regressions' task as complete 2026-02-24 19:43:51 -05:00
ed 28155bcee6 conductor(plan): Mark task 'Verify functional parity' as complete 2026-02-24 19:43:01 -05:00
ed 450820e8f9 chore(conductor): Mark 'Verify functional parity' task as complete 2026-02-24 19:42:09 -05:00
ed 79d462736c conductor(plan): Mark task 'Complete EventEmitter integration' as complete 2026-02-24 19:41:16 -05:00
ed 9d59a454e0 feat(gui2): Complete EventEmitter integration 2026-02-24 19:40:18 -05:00
ed 23db500688 conductor(plan): Mark task 'Implement missing panels' as complete 2026-02-24 19:38:41 -05:00
ed a85293ff99 feat(gui2): Implement missing GUI hook handlers 2026-02-24 19:37:58 -05:00
ed ccf07a762b fix(conductor): Revert track status to 'In Progress' 2026-02-24 19:32:02 -05:00
ed 211d03a93f chore(conductor): Mark track 'Investigate differences left between gui.py and gui_2.py. Needs to reach full parity, so we can sunset guy.py' as complete 2026-02-24 19:27:04 -05:00
ed ff3245eb2b conductor(plan): Mark task 'Conductor - User Manual Verification Phase 1' as complete 2026-02-24 19:26:37 -05:00
ed 9f99b77849 chore(conductor): Mark 'Conductor - User Manual Verification Phase 1' task as complete 2026-02-24 19:26:22 -05:00
ed 3797624cae conductor(plan): Mark phase 'Phase 1: Research and Gap Analysis' as complete 2026-02-24 19:26:06 -05:00
ed 36988cbea1 conductor(checkpoint): Checkpoint end of Phase 1: Research and Gap Analysis 2026-02-24 19:25:10 -05:00
ed 0fc8769e17 conductor(plan): Mark task 'Verify failing parity tests' as complete 2026-02-24 19:24:28 -05:00
ed 0006f727d5 chore(conductor): Mark 'Verify failing parity tests' task as complete 2026-02-24 19:24:08 -05:00
ed 3c7e2c0f1d conductor(plan): Mark task 'Write failing tests' as complete 2026-02-24 19:23:37 -05:00
ed 7c5167478b test(gui2): Add failing parity tests for GUI hooks 2026-02-24 19:23:22 -05:00
ed fb4b529fa2 conductor(plan): Mark task 'Map EventEmitter and ApiHookClient' as complete 2026-02-24 19:21:36 -05:00
ed 579b0041fc chore(conductor): Mark 'Map EventEmitter and ApiHookClient' task as complete 2026-02-24 19:21:15 -05:00
ed ede3960afb conductor(plan): Mark task 'Audit gui.py and gui_2.py' as complete 2026-02-24 19:20:56 -05:00
ed fe338228d2 chore(conductor): Mark 'Audit gui.py and gui_2.py' task as complete 2026-02-24 19:20:41 -05:00
ed 449c4daee1 chore(conductor): Add new track 'extend test simulation to have further in breadth test (not remove the original though as its a useful small test) to extensively test all facets of possible gui interaction.' 2026-02-24 19:18:12 -05:00
ed 4b342265c1 chore(conductor): Add new track '4-Tier Architecture Implementation & Conductor Self-Improvement' 2026-02-24 19:11:28 -05:00
ed 22607b4ed2 MMA_Support draft 2026-02-24 19:11:15 -05:00
ed f68a07e30e check point support MMA 2026-02-24 19:03:22 -05:00
ed 2bf55a89c2 chore(conductor): Add new track 'GUI 2.0 Feature Parity and Migration' 2026-02-24 18:39:21 -05:00
ed 9ba8ac2187 chore(conductor): Add new track 'Update documentation and cleanup MainContext.md' 2026-02-24 18:36:03 -05:00
ed 5515a72cf3 update conductor files 2026-02-24 18:32:38 -05:00
ed ef3d8b0ec1 chore(conductor): Add new track 'Move discussion histories to their own toml to prevent the ai agent from reading it (will be on a blacklist).' 2026-02-24 18:32:09 -05:00
ed 874422ecfd comitting 2026-02-23 23:28:49 -05:00
ed 57cb63b9c9 conductor(track): Complete gui2_feature_parity track
Close gui2_feature_parity track after implementing all features
and conducting manual and automated verification.

Key Achievements:
- Integrated event-driven architecture and MCP client.
- Ported API hooks and performance diagnostics.
- Implemented Prior Session Viewer.
- Refactored UI to a Hub-based layout.
- Added agent capability toggles.
- Achieved full theme integration.
- Developed comprehensive test suite.

Note: Remaining UI display issues for text panels in the comms and
tool call history will be addressed in a subsequent track.
2026-02-23 23:27:43 -05:00
ed dbf2962c54 fix(gui): Restore 'Load Log' button and fix docking crash
fix(mcp): Improve path resolution and error messages
2026-02-23 23:00:17 -05:00
ed f5ef2d850f refactor(gui): Implement user feedback for UI layout 2026-02-23 22:36:45 -05:00
ed 366cd8ebdd conductor(plan): Mark phase 'UI/UX Refinement' as complete 2026-02-23 22:18:11 -05:00
ed cc5074e682 conductor(checkpoint): Checkpoint end of Phase 3 2026-02-23 22:17:37 -05:00
ed 1b49e20c2e conductor(plan): Mark Hub refactoring as complete 2026-02-23 22:16:30 -05:00
ed ddb53b250f refactor(gui2): Restructure layout into discrete Hubs
Automates the refactoring of the monolithic _gui_func in gui_2.py into separate rendering methods, nested within 'Context Hub', 'AI Settings Hub', 'Discussion Hub', and 'Operations Hub', utilizing tab bars. Adds tests to ensure the new default windows correctly represent this Hub structure.
2026-02-23 22:15:13 -05:00
ed c6a756e754 conductor(plan): Mark phase 'Core Architectural Integration' as complete 2026-02-23 22:11:17 -05:00
ed 712d5a856f conductor(checkpoint): Checkpoint end of Phase 1 2026-02-23 22:10:05 -05:00
ed ece84d4c4f feat(gui2): Integrate mcp_client.py for native file tools
Wires up the mcp_client.perf_monitor_callback to the gui_2.py App class and verifies the dispatch loop through a newly created test.
2026-02-23 22:06:55 -05:00
ed 2ab3f101d6 Merge origin/cache 2026-02-23 22:03:06 -05:00
ed 1d8626bc6b chore: Update config and manual_slop.toml 2026-02-23 21:55:00 -05:00
r00tz bd8551d282 Harden reliability, security, and UX across core modules
- Add thread safety: _anthropic_history_lock and _send_lock in ai_client to prevent concurrent corruption
  - Add _send_thread_lock in gui_2 for atomic check-and-start of send thread
  - Add atexit fallback in session_logger to flush log files on abnormal exit
  - Fix file descriptor leaks: use context managers for urlopen in mcp_client
  - Cap unbounded tool output growth at 500KB per send() call (both Gemini and Anthropic)
  - Harden path traversal: resolve(strict=True) with fallback in mcp_client allowlist checks
  - Add SLOP_CREDENTIALS env var override for credentials.toml with helpful error message
  - Fix Gemini token heuristic: use _CHARS_PER_TOKEN (3.5) instead of hardcoded // 4
  - Add keyboard shortcuts: Ctrl+Enter to send, Ctrl+L to clear message input
  - Add auto-save: flush project and config to disk every 60 seconds
2026-02-23 21:29:30 -05:00
ed 6d825e6585 wip: gemini doing gui_2.py catchup track 2026-02-23 21:07:06 -05:00
ed 3db6a32e7c conductor(plan): Update plan after merge from cache branch 2026-02-23 20:34:14 -05:00
ed c19b13e4ac Merge branch 'origin/cache' 2026-02-23 20:32:49 -05:00
ed 1b9a2ab640 chore: Update discussion timestamp 2026-02-23 20:24:51 -05:00
ed 4300a8a963 conductor(plan): Mark task 'Integrate events.py into gui_2.py' as complete 2026-02-23 20:23:26 -05:00
ed 24b831c712 feat(gui2): Integrate core event system
Integrates the ai_client.events emitter into the gui_2.py App class. Adds a new test file to verify that the App subscribes to API lifecycle events upon initialization. This is the first step in aligning gui_2.py with the project's event-driven architecture.
2026-02-23 20:22:36 -05:00
ed bf873dc110 for some reason didn't add? 2026-02-23 20:17:55 -05:00
ed f65542add8 chore(conductor): Add new track 'get gui_2 working with latest changes to the project.' 2026-02-23 20:16:53 -05:00
ed 229ebaf238 Merge branch 'sim' 2026-02-23 20:11:01 -05:00
ed e51194a9be remove live_ux_test from active tracks 2026-02-23 20:10:47 -05:00
ed 85f8f08f42 chore(conductor): Archive track 'live_ux_test_20260223' 2026-02-23 20:10:22 -05:00
ed 70358f8151 conductor(plan): Mark task 'Apply review suggestions' as complete 2026-02-23 20:09:54 -05:00
ed 064d7ba235 fix(conductor): Apply review suggestions for track 'live_ux_test_20260223' 2026-02-23 20:09:41 -05:00
r00tz 69401365be Port missing features to gui_2 and optimize caching
- Port 10 missing features from gui.py to gui_2.py: performance
    diagnostics, prior session log viewing, token budget visualization,
    agent tools config, API hooks server, GUI task queue, discussion
    truncation, THINKING/LIVE indicators, event subscriptions, and
    session usage tracking
  - Persist window visibility state in config.toml
  - Fix Gemini cache invalidation by separating discussion history
    from cached context (use MD5 hash instead of built-in hash)
  - Add cost optimizations: tool output truncation at source, proactive
    history trimming at 40%, summary_only support in aggregate.run()
  - Add cleanup() for destroying API caches on exit
2026-02-23 20:06:13 -05:00
208 changed files with 11775 additions and 1202 deletions
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.venv
__pycache__
*.pyc
*.pyo
*.pyd
.git
.gitignore
logs
gallery
md_gen
credentials.toml
manual_slop.toml
manual_slop_history.toml
manualslop_layout.ini
dpg_layout.ini
.pytest_cache
scripts/generated
.gemini
conductor/archive
.editorconfig
*.log
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@@ -2,7 +2,7 @@ root = true
[*.py] [*.py]
indent_style = space indent_style = space
indent_size = 2 indent_size = 1
[*.s] [*.s]
indent_style = tab indent_style = tab
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{
"hooks": {
"BeforeTool": [
{
"matcher": "*",
"hooks": [
{
"name": "manual-slop-bridge",
"type": "command",
"command": "python C:/projects/manual_slop/scripts/cli_tool_bridge.py"
}
]
}
]
},
"hooksConfig": {
"enabled": true
}
}
+1
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@@ -0,0 +1 @@
C:/projects/manual_slop/mma-orchestrator
@@ -0,0 +1,19 @@
---
name: mma-tier1-orchestrator
description: Focused on product alignment, high-level planning, and track initialization.
---
# MMA Tier 1: Orchestrator
You are the Tier 1 Orchestrator. Your role is to oversee the product direction and manage project/track initialization within the Conductor framework.
## Responsibilities
- Maintain alignment with the product guidelines and definition.
- Define track boundaries and initialize new tracks (`/conductor:newTrack`).
- Set up the project environment (`/conductor:setup`).
- Delegate track execution to the Tier 2 Tech Lead.
## Limitations
- Do not execute tracks or implement features.
- Do not write code or perform low-level bug fixing.
- Keep context strictly focused on product definitions and high-level strategy.
@@ -0,0 +1,21 @@
---
name: mma-tier2-tech-lead
description: Focused on track execution, architectural design, and implementation oversight.
---
# MMA Tier 2: Tech Lead
You are the Tier 2 Tech Lead. Your role is to manage the implementation of tracks (`/conductor:implement`), ensure architectural integrity, and oversee the work of Tier 3 and 4 sub-agents.
## Responsibilities
- Manage the execution of implementation tracks.
- Ensure alignment with `tech-stack.md` and project architecture.
- Break down tasks into specific technical steps for Tier 3 Workers.
- Maintain persistent context throughout a track's implementation phase (No Context Amnesia).
- Review implementations and coordinate bug fixes via Tier 4 QA.
## Limitations
- Do not perform heavy implementation work directly; delegate to Tier 3.
- Delegate implementation tasks to Tier 3 Workers using `uv run python scripts/mma_exec.py --role tier3-worker "[PROMPT]"`.
- For error analysis of large logs, use `uv run python scripts/mma_exec.py --role tier4-qa "[PROMPT]"`.
- Minimize full file reads for large modules; rely on "Skeleton Views" and git diffs.
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---
name: mma-tier3-worker
description: Focused on TDD implementation, surgical code changes, and following specific specs.
---
# MMA Tier 3: Worker
You are the Tier 3 Worker. Your role is to implement specific, scoped technical requirements, follow Test-Driven Development (TDD), and make surgical code modifications. You operate in a stateless manner (Context Amnesia).
## Responsibilities
- Implement code strictly according to the provided prompt and specifications.
- Write failing tests first, then implement the code to pass them.
- Ensure all changes are minimal, functional, and conform to the requested standards.
- Utilize provided tool access (read_file, write_file, etc.) to perform implementation and verification.
## Limitations
- Do not make architectural decisions.
- Do not modify unrelated files beyond the immediate task scope.
- Always operate statelessly; assume each task starts with a clean context.
- Rely on "Skeleton Views" provided by Tier 2/Orchestrator for understanding dependencies.
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---
name: mma-tier4-qa
description: Focused on test analysis, error summarization, and bug reproduction.
---
# MMA Tier 4: QA Agent
You are the Tier 4 QA Agent. Your role is to analyze error logs, summarize tracebacks, and help diagnose issues efficiently. You operate in a stateless manner (Context Amnesia).
## Responsibilities
- Compress large stack traces or log files into concise, actionable summaries.
- Identify the root cause of test failures or runtime errors.
- Provide a brief, technical description of the required fix.
- Utilize provided diagnostic and exploration tools to verify failures.
## Limitations
- Do not implement the fix directly.
- Ensure your output is extremely brief and focused.
- Always operate statelessly; assume each analysis starts with a clean context.
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# Use python:3.11-slim as a base
FROM python:3.11-slim
# Set environment variables
# UV_SYSTEM_PYTHON=1 allows uv to install into the system site-packages
ENV PYTHONDONTWRITEBYTECODE=1
PYTHONUNBUFFERED=1
UV_SYSTEM_PYTHON=1
# Install system dependencies and uv
RUN apt-get update && apt-get install -y --no-install-recommends
curl
ca-certificates
&& rm -rf /var/lib/apt/lists/*
&& curl -LsSf https://astral.sh/uv/install.sh | sh
&& mv /root/.local/bin/uv /usr/local/bin/uv
# Set the working directory in the container
WORKDIR /app
# Copy dependency files first to leverage Docker layer caching
COPY pyproject.toml requirements.txt* ./
# Install dependencies via uv
RUN if [ -f requirements.txt ]; then uv pip install --no-cache -r requirements.txt; fi
# Copy the rest of the application code
COPY . .
# Expose port 8000 for the headless API/service
EXPOSE 8000
# Set the entrypoint to run the app in headless mode
ENTRYPOINT ["python", "gui_2.py", "--headless"]
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* **Configuration:** TOML (`tomli-w`) * **Configuration:** TOML (`tomli-w`)
**Architecture:** **Architecture:**
* **`gui.py`:** The main entry point and Dear PyGui application logic. Handles all panels, layouts, user input, and confirmation dialogs. * **`gui_legacy.py`:** The main entry point and Dear PyGui application logic. Handles all panels, layouts, user input, and confirmation dialogs.
* **`ai_client.py`:** A unified wrapper for both Gemini and Anthropic APIs. Manages sessions, tool/function-call loops, token estimation, and context history management. * **`ai_client.py`:** A unified wrapper for both Gemini and Anthropic APIs. Manages sessions, tool/function-call loops, token estimation, and context history management.
* **`aggregate.py`:** Responsible for building the `file_items` context. It reads project configurations, collects files and screenshots, and builds the context into markdown format to send to the AI. * **`aggregate.py`:** Responsible for building the `file_items` context. It reads project configurations, collects files and screenshots, and builds the context into markdown format to send to the AI.
* **`mcp_client.py`:** Implements MCP-like tools (e.g., `read_file`, `list_directory`, `search_files`, `web_search`) as native functions that the AI can call. Enforces a strict allowlist for file access. * **`mcp_client.py`:** Implements MCP-like tools (e.g., `read_file`, `list_directory`, `search_files`, `web_search`) as native functions that the AI can call. Enforces a strict allowlist for file access.
@@ -30,7 +30,7 @@
``` ```
* **Run the Application:** * **Run the Application:**
```powershell ```powershell
uv run .\gui.py uv run .\gui_2.py
``` ```
# Development Conventions # Development Conventions
@@ -0,0 +1,45 @@
# MMA Hierarchical Delegation: Recommended Architecture
## 1. Overview
The Multi-Model Architecture (MMA) utilizes a 4-Tier hierarchy to ensure token efficiency and structural integrity. The primary agent (Conductor) acts as the Tier 2 Tech Lead, delegating specific, stateless tasks to Tier 3 (Workers) and Tier 4 (Utility) agents.
## 2. Agent Roles & Responsibilities
### Tier 2: The Conductor (Tech Lead)
- **Role:** Orchestrator of the project lifecycle via the Conductor framework.
- **Context:** High-reasoning, long-term memory of project goals and specifications.
- **Key Tool:** `mma-orchestrator` skill (Strategy).
- **Delegation Logic:** Identifies tasks that would bloat the primary context (large code blocks, massive error traces) and spawns sub-agents.
### Tier 3: The Worker (Contributor)
- **Role:** Stateless code generator.
- **Context:** Isolated. Sees only the target file and the specific ticket.
- **Protocol:** Receives a "Worker" system prompt. Outputs clean code or diffs.
- **Invocation:** `.\scripts\run_subagent.ps1 -Role Worker -Prompt "..."`
### Tier 4: The Utility (QA/Compressor)
- **Role:** Stateless translator and summarizer.
- **Context:** Minimal. Sees only the error trace or snippet.
- **Protocol:** Receives a "QA" system prompt. Outputs compressed findings (max 50 tokens).
- **Invocation:** `.\scripts\run_subagent.ps1 -Role QA -Prompt "..."`
## 3. Invocation Protocol
### Step 1: Detection
Tier 2 detects a delegation trigger:
- Coding task > 50 lines.
- Error trace > 100 lines.
### Step 2: Spawning
Tier 2 calls the delegation script:
```powershell
.\scripts\run_subagent.ps1 -Role <Worker|QA> -Prompt "Specific instructions..."
```
### Step 3: Integration
Tier 2 receives the sub-agent's response.
- **If Worker:** Tier 2 applies the code changes (using `replace` or `write_file`) and verifies.
- **If QA:** Tier 2 uses the compressed error to inform the next fix attempt or passes it to a Worker.
## 4. System Prompt Management
The `run_subagent.ps1` script should be updated to maintain a library of role-specific system prompts, ensuring that Tier 3/4 agents remain focused and tool-free (to prevent nested complexity).
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# Data Pipelines, Memory Views & Configuration
The 4-Tier Architecture relies on strictly managed data pipelines and configuration files to prevent token bloat and maintain a deterministically safe execution environment.
## 1. AST Extraction Pipelines (Memory Views)
To prevent LLMs from hallucinating or consuming massive context windows, raw file text is heavily restricted. The `file_cache.py` uses Tree-sitter for deterministic Abstract Syntax Tree (AST) parsing to generate specific views:
1. **The Directory Map (Tier 1):** Just filenames and nested paths (e.g., output of `tree /F`). No source code.
2. **The Skeleton View (Tier 2 & 3 Dependencies):** Extracts only `class` and `def` signatures, parameters, and type hints. Strips all docstrings and function bodies, replacing them with `pass`. Used for foreign modules a worker must call but not modify.
3. **The Curated Implementation View (Tier 2 Target Modules):**
* Keeps class/struct definitions.
* Keeps module-level docstrings and block comments (heuristics).
* Keeps full bodies of functions marked with `@core_logic` or `# [HOT]`.
* Replaces standard function bodies with `... # Hidden`.
4. **The Raw View (Tier 3 Target File):** Unredacted, line-by-line source code of the *single* file a Tier 3 worker is assigned to modify.
## 2. Configuration Schema
The architecture separates sensitive billing logic from AI behavior routing.
* **`credentials.toml` (Security Prerequisite):** Holds the bare metal authentication (`gemini_api_key`, `anthropic_api_key`, `deepseek_api_key`). **This file must be in `.gitignore`.** Loaded strictly for instantiating HTTP clients.
* **`project.toml` (Repo Rules):** Holds repository-specific bounds (e.g., "This project uses Python 3.12 and strictly follows PEP8").
* **`agents.toml` (AI Routing):** Defines the hardcoded hierarchy's operational behaviors. Includes fallback models (`default_expensive`, `default_cheap`), Tier 1/2 overarching parameters (temperature, base system prompts), and Tier 3 worker archetypes (`refactor`, `codegen`, `contract_stubber`) mapped to specific models (DeepSeek V3, Gemini Flash) and `trust_level` tags (`step` vs. `auto`).
## 3. LLM Output Formats
To ensure robust parser execution and avoid JSON string-escaping nightmares, the architecture uses a hybrid approach for LLM outputs depending on the Tier:
* **Native Structured Outputs (JSON Schema forced by API):** Used for Tier 1 and Tier 2 routing and orchestration. The model provider mathematically guarantees the syntax, allowing clean parsing of `Track` and `Ticket` metadata by `pydantic`.
* **XML Tags (`<file_path>`, `<file_content>`):** Used for Tier 3 Code Generation & Tools. It natively isolates syntax and requires zero string escaping. The UI/Orchestrator parses these via regex to safely extract raw Python code without bracket-matching failures.
* **Godot ECS Flat List (Linearized Entities with ID Pointers):** Instead of deeply nested JSON (which models hallucinate across 500 tokens), Tier 1/2 Orchestrators define complex dependency DAGs as a flat list of items (e.g., `[Ticket id="tkt_impl" depends_on="tkt_stub"]`). The Python state machine reconstructs the DAG locally.
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# MMA Tiered Architecture: Final Analysis Report
## 1. Executive Summary
The implementation and verification of the 4-Tier Hierarchical Multi-Model Architecture (MMA) within the Conductor framework have been successfully completed. The architecture provides a robust "Token Firewall" that prevents the primary context from being bloated by repetitive coding tasks and massive error traces.
## 2. Architectural Findings
### Centralized Strategy vs. Role-Based Sub-Agents
- **Decision:** A Hybrid Approach was implemented.
- **Rationale:** The Tier 2 Orchestrator (Conductor) maintains the high-level strategy via a centralized skill, while Tier 3 (Worker) and Tier 4 (QA) agents are governed by surgical, role-specific system prompts. This ensures that sub-agents remain focused and stateless without the overhead of complex, nested tool-usage logic.
### Delegation Efficacy
- **Tier 3 (Worker):** Successfully isolated code generation from the main conversation. The worker generates clean code/diffs that are then integrated by the Orchestrator.
- **Tier 4 (QA):** Demonstrated superior token efficiency by compressing multi-hundred-line stack traces into ~20-word actionable fixes.
- **Traceability:** The `-ShowContext` flag in `scripts/run_subagent.ps1` provides immediate visibility into the "Connective Tissue" of the hierarchy, allowing human supervisors to monitor the hand-offs.
## 3. Recommended Protocol (Final)
1. **Identification:** Tier 2 identifies a "Bloat Trigger" (Coding > 50 lines, Errors > 100 lines).
2. **Delegation:** Tier 2 spawns a sub-agent via `.\scripts
un_subagent.ps1 -Role [Worker|QA] -Prompt "..."`.
3. **Integration:** Tier 2 receives the stateless response and applies it to the project state.
4. **Checkpointing:** Tier 2 performs Phase-level checkpoints to "Wipe" trial-and-error memory and solidify the new state.
## 4. Verification Results
- **Automated Tests:** 100% Pass (4/4 tests in `tests/conductor/test_infrastructure.py`).
- **Isolation:** Confirmed via `test_subagent_isolation_live`.
- **Live Trace:** Manually verified and approved by the user (Tier 2 -> 3 -> 4 flow).
## 5. Conclusion
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# Iteration Plan (Implementation Tracks)
To safely refactor a linear, single-agent codebase into the 4-Tier Multi-Model Architecture without breaking the working prototype, the implementation should be sequenced into these five isolated Epics (Tracks):
## Track 1: The Memory Foundations (AST Parser)
**Goal:** Build the engine that prevents token-bloat by turning massive source files into curated memory views.
**Implementation Details:**
1. Integrate `tree-sitter` and language bindings into `file_cache.py`.
2. Build `ASTParser` extraction rules:
* *Skeleton View:* Strip function/class bodies, preserving only signatures, parameters, and type hints.
* *Curated View:* Preserve class structures, module docstrings, and bodies of functions marked `# [HOT]` or `@core_logic`. Replace standard bodies with `... # Hidden`.
3. **Acceptance:** `file_cache.get_curated_view('script.py')` returns a perfectly formatted summary string in the terminal.
## Track 2: State Machine & Data Structures
**Goal:** Define the rigid Python objects the AI agents will pass to each other to rely on structured data, not loose chat strings.
**Implementation Details:**
1. Create `models.py` with `pydantic` or `dataclasses` for `Track` (Epic) and `Ticket` (Task).
2. Define `WorkerContext` holding the Ticket ID, assigned model (from `agents.toml`), isolated `credentials.toml` injection, and a `messages` payload array.
3. Add helper methods for state mutators (e.g., `ticket.mark_blocked()`, `ticket.mark_complete()`).
4. **Acceptance:** Instantiate a `Track` with 3 `Tickets` and successfully enforce state changes in Python without AI involvement.
## Track 3: The Linear Orchestrator & Execution Clutch
**Goal:** Build the synchronous, debuggable core loop that runs a single Tier 3 Worker and pauses for human approval.
**Implementation Details:**
1. Create `multi_agent_conductor.py` with a `run_worker_lifecycle(ticket: Ticket)` function.
2. Inject context (Raw View from `file_cache.py`) and format the `messages` array for the API.
3. Implement the Clutch (HITL): `input()` pause for CLI or wait state for GUI before executing the returned tool (e.g., `write_file`). Allow manual memory mutation of the JSON payload.
4. **Acceptance:** The script sends a hardcoded Ticket to DeepSeek, pauses in the terminal showing a diff, waits for user approval, applies the diff via `mcp_client.py`, and wipes the worker's history.
## Track 4: Tier 4 QA Interception
**Goal:** Stop error traces from destroying the Worker's token window by routing crashes through a stateless translator.
**Implementation Details:**
1. In `shell_runner.py`, intercept `stderr` (e.g., `returncode != 0`).
2. Do *not* append `stderr` to the main Worker's history. Instead, instantiate a synchronous API call to the `default_cheap` model.
3. Prompt: *"You are an error parser. Output only a 1-2 sentence instruction on how to fix this syntax error."* Send the raw `stderr` and target file snippet.
4. Append the translated 20-word fix to the main Worker's history as a "System Hint".
5. **Acceptance:** A deliberate syntax error triggers the execution engine to silently ping the cheap API, returning a 20-word correction to the Worker instead of a 200-line stack trace.
## Track 5: UI Decoupling & Tier 1/2 Routing (The Final Boss)
**Goal:** Bring the system online by letting Tier 1 and Tier 2 dynamically generate Tickets managed by the async Event Bus.
**Implementation Details:**
1. Implement an `asyncio.Queue` in `multi_agent_conductor.py`.
2. Write Tier 1 & 2 system prompts forcing output as strict JSON arrays (Tracks and Tickets).
3. Write the Dispatcher async loop to convert JSON into `Ticket` objects and push to the queue.
4. Enforce the Stub Resolver: If a Ticket archetype is `contract_stubber`, pause dependent Tickets, run the stubber, trigger `file_cache.py` to rebuild the Skeleton View, then resume.
5. **Acceptance:** Vague prompt ("Refactor config system") results in Tier 1 Track, Tier 2 Tickets (Interface stub + Implementation). System executes stub, updates AST, and finishes implementation automatically (or steps through if Linear toggle is on).
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# The Orchestrator Engine & UI
To transition from a linear, single-agent chat box to a multi-agent control center, the GUI must be decoupled from the LLM execution loops. A single-agent UI assumes a linear flow (*User types -> UI waits -> LLM responds -> UI updates*), which freezes the application if a Tier 1 PM waits for human approval while Tier 3 Workers run local tests in the background.
## 1. The Async Event Bus (Decoupling UI from Agents)
The GUI acts as a "dumb" renderer. It only renders state; it never manages state.
* **The Agent Bus (Message Queue):** A thread-safe signaling system (e.g., `asyncio.Queue`, `pyqtSignal`) passes messages between agents, UI, and the filesystem.
* **Background Workers:** When Tier 1 spawns a Tier 2 Tech Lead, the GUI does not wait. It pushes a `UserRequestEvent` to the Conductor's queue. The Conductor runs the LLM call asynchronously and fires `StateUpdateEvents` back for the GUI to redraw.
## 2. The Execution Clutch (HITL)
Every spawned worker panel implements an execution state toggle based on the `trust_level` defined in `agents.toml`.
* **Step Mode (Lock-step):** The worker pauses **twice** per cycle:
1. *After* generating a response/tool-call, but *before* executing the tool. The GUI renders a preview (e.g., diff of lines 40-50) and offers `[Approve]`, `[Edit Payload]`, or `[Abort]`.
2. *After* executing the tool, but *before* sending output back to the LLM (allows verification of the system output).
* **Auto Mode (Fire-and-forget):** The worker loops continuously until it outputs a "Task Complete" status to the Router.
## 3. Memory Mutation (The "Debug" Superpower)
If a worker generates a flawed plan in Step Mode, the "Memory Mutator" allows the user to click the last message and edit the raw JSON/text directly before hitting "Approve." By rewriting the AI's brain mid-task, the model proceeds as if it generated the correct idea, saving the context window from restarting due to a minor hallucination.
## 4. The Global Execution Toggle
A Global Execution Toggle overrides all individual agent trust levels for debugging race conditions or context leaks.
* **Mode = "async" (Production):** The Dispatcher throws Tickets into an `asyncio.TaskGroup`. They spawn instantly, fight for API rate limits, read the skeleton, and run in parallel.
* **Mode = "linear" (Debug):** The Dispatcher iterates through the array sequentially using a strict `for` loop. It `awaits` absolute completion of Ticket 1 (including QA loops and code review) before instantiating the `WorkerAgent` for Ticket 2. This enforces a deterministic state machine and outputs state snapshots (`debug_state.json`) for manual verification.
## 5. State Machine (Dataclasses)
The Conductor relies on strict definitions for `Track` and `Ticket` to enforce state and UI rendering (e.g., using `dataclasses` or `pydantic`).
* **`Ticket`:** Contains `id`, `target_file`, `prompt`, `worker_archetype`, `status` (pending, running, blocked, step_paused, completed), and a `dependencies` list of Ticket IDs that must finish first.
* **`Track`:** Contains `id`, `title`, `description`, `status`, and a list of `Tickets`.
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# System Specification: 4-Tier Hierarchical Multi-Model Architecture
**Project:** `manual_slop` (or equivalent Agentic Co-Dev Prototype)
**Core Philosophy:** Token Economy, Strict Memory Siloing, and Human-In-The-Loop (HITL) Execution.
## 1. Architectural Overview
This system rejects the "monolithic black-box" approach to agentic coding. Instead of passing an entire codebase into a single expensive context window, the architecture mimics a senior engineering department. It uses a 4-Tier hierarchy where cognitive load and context are aggressively filtered from top to bottom.
Expensive, high-reasoning models manage metadata and architecture (Tier 1 & 2), while cheap, fast models handle repetitive syntax and error parsing (Tier 3 & 4).
### 1.1 Core Paradigms
* **Token Firewalling:** Error logs and deep history are never allowed to bubble up to high-tier models. The system relies heavily on abstracted AST views (Skeleton, Curated) rather than raw code when context allows.
* **Context Amnesia:** Worker agents (Tier 3) have their trial-and-error histories wiped upon task completion to prevent context ballooning and hallucination.
* **The Execution Clutch (HITL):** Agents operate based on Archetype Trust Scores defined in configuration. Trusted patterns run in `Auto` mode; untrusted or complex refactors run in `Step` mode, pausing before tool execution for human review and JSON history mutation.
* **Interface-Driven Development (IDD):** The architecture inherently prioritizes the creation of contracts (stubs, schemas) before implementation, allowing workers to proceed in parallel without breaking cross-module boundaries.
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# Tier 1: The Top-Level Orchestrator (Product Manager)
**Designated Models:** Gemini 3.1 Pro, Claude 3.5 Sonnet.
**Execution Frequency:** Low (Start of feature, Macro-merge resolution).
**Core Role:** Epic planning, architecture enforcement, and cross-module task delegation.
The Tier 1 Orchestrator is the most capable and expensive model in the hierarchy. It operates strictly on metadata, summaries, and executive-level directives. It **never** sees raw implementation code.
## Memory Context & Paths
### Path A: Epic Initialization (Project Planning)
* **Trigger:** User drops a massive new feature request or architectural shift into the main UI.
* **What it Sees (Context):**
* **The User Prompt:** The raw feature request.
* **Project Meta-State:** `project.toml` (rules, allowed languages, dependencies).
* **Repository Map:** A strict, file-tree outline (names and paths only).
* **Global Architecture Docs:** High-level markdown files (e.g., `docs/guide_architecture.md`).
* **What it Ignores:** All source code, all AST skeletons, and all previous micro-task histories.
* **Output Format:** A JSON array (Godot ECS Flat List format) of `Tracks` (Jira Epics), identifying which modules will be affected, the required Tech Lead persona, and the severity level.
### Path B: Track Delegation (Sprint Kickoff)
* **Trigger:** The PM is handing a defined Track down to a Tier 2 Tech Lead.
* **What it Sees (Context):**
* **The Target Track:** The specific goal and Acceptance Criteria generated in Path A.
* **Module Interfaces (Skeleton View):** Strict AST skeleton (just class/function definitions) *only* for the modules this specific Track is allowed to touch.
* **Track Roster:** A list of currently active or completed Tracks to prevent duplicate work.
* **What it Ignores:** Unrelated module docs, original massive user prompt, implementation details.
* **Output Format:** A compiled "Track Brief" (system prompt + curated file list) passed to instantiate the Tier 2 Tech Lead panel.
### Path C: Macro-Merge & Acceptance Review (Severity Resolution)
* **Trigger:** A Tier 2 Tech Lead reports "Track Complete" and submits a pull request/diff for a "High Severity" task.
* **What it Sees (Context):**
* **Original Acceptance Criteria:** The Track's goals.
* **Tech Lead's Executive Summary:** A ~200-word explanation of the chosen implementation algorithm.
* **The Macro-Diff:** Actual changes made to the codebase.
* **Curated Implementation View:** For boundary files, ensuring the merge doesn't break foreign modules.
* **What it Ignores:** Tier 3 Worker trial-and-error histories, Tier 4 error logs, raw bodies of unchanged functions.
* **Output Format:** "Approved" (commits to memory) OR "Rejected" with specific architectural feedback for Tier 2.
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# Tier 2: The Track Conductor (Tech Lead)
**Designated Models:** Gemini 3.0 Flash, Gemini 2.5 Pro.
**Execution Frequency:** Medium.
**Core Role:** Module-specific planning, code review, spawning Worker agents, and Topological Dependency Graph management.
The Tech Lead bridges the gap between high-level architecture and actual code syntax. It operates in a "need-to-know" state, utilizing AST parsing (`file_cache.py`) to keep token counts low while maintaining structural awareness of its assigned modules.
## Memory Context & Paths
### Path A: Sprint Planning (Task Delegation)
* **Trigger:** Tier 1 (PM) assigns a Track (Epic) and wakes up the Tech Lead.
* **What it Sees (Context):**
* **The Track Brief:** Acceptance Criteria from Tier 1.
* **Curated Implementation View (Target Modules):** AST-extracted class structures, docstrings, and `# [HOT]` function bodies for the 1-3 files this Track explicitly modifies.
* **Skeleton View (Foreign Modules):** Only function signatures and return types for external dependencies.
* **What it Ignores:** The rest of the repository, the PM's overarching project-planning logic, raw line-by-line code of non-hot functions.
* **Output Format:** A JSON array (Godot ECS Flat List format) of discrete Tier 3 `Tickets` (e.g., Ticket 1: *Write DB migration script*, Ticket 2: *Update core API endpoints*), including `depends_on` pointers to construct an execution DAG.
### Path B: Code Review (Local Integration)
* **Trigger:** A Tier 3 Contributor completes a Ticket and submits a diff, OR Tier 4 (QA) flags a persistent failure.
* **What it Sees (Context):**
* **Specific Ticket Goal:** What the Contributor was instructed to do.
* **Proposed Diff:** The exact line changes submitted by Tier 3.
* **Test/QA Output:** Relevant logs from Tier 4 compiler checks.
* **Curated Implementation View:** To cross-reference the proposed diff against the existing architecture.
* **What it Ignores:** The Contributor's internal trial-and-error chat history. It only sees the final submission.
* **Output Format:** *Approve* (merges diff into working branch and updates Curated View) or *Reject* (sends technical critique back to Tier 3).
### Path C: Track Finalization (Upward Reporting)
* **Trigger:** All Tier 3 Tickets assigned to this Track are marked "Approved."
* **What it Sees (Context):**
* **Original Track Brief:** To verify requirements were met.
* **Aggregated Track Diff:** The sum total of all changes made across all Tier 3 Tickets.
* **Dependency Delta:** A list of any new foreign modules or libraries imported.
* **What it Ignores:** The back-and-forth review cycles, original AST Curated View.
* **Output Format:** An Executive Summary and the final Macro-Diff, sent back to Tier 1.
### Path D: Contract-First Delegation (Stub-and-Resolve)
* **Trigger:** Tier 2 evaluates a Track and detects a cross-module dependency (or a single massive refactor) requiring an undefined signature.
* **Role:** Force Interface-Driven Development (IDD) to prevent hallucination.
* **Execution Flow:**
1. **Contract Definition:** Splits requirement into a `Stub Ticket`, `Consumer Ticket`, and `Implementation Ticket`.
2. **Stub Generation:** Spawns a cheap Tier 3 worker (e.g., DeepSeek V3 `contract_stubber` archetype) to generate the empty function signature, type hints, and docstrings.
3. **Skeleton Broadcast:** The stub merges, and the system instantly re-runs Tree-sitter to update the global Skeleton View.
4. **Parallel Implementation:** Tier 2 simultaneously spawns the `Consumer` (codes against the skeleton) and the `Implementer` (fills the stub logic) in isolated contexts.
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# Tier 3: The Worker Agents (Contributors)
**Designated Models:** DeepSeek V3/R1, Gemini 2.5 Flash.
**Execution Frequency:** High (The core loop).
**Core Role:** Generating syntax, writing localized files, running unit tests.
The engine room of the system. Contributors execute the highest volume of API calls. Their memory context is ruthlessly pruned. By leveraging cheap, fast models, they operate with zero architectural anxiety—they just write the code they are assigned. They are "Amnesiac Workers," having their history wiped between tasks to prevent context ballooning.
## Memory Context & Paths
### Path A: Heads Down Execution (Task Execution)
* **Trigger:** Tier 2 (Tech Lead) hands down a hyper-specific Ticket.
* **What it Sees (Context):**
* **The Ticket Prompt:** The exact, isolated instructions from Tier 2.
* **The Target File (Raw View):** The raw, unredacted, line-by-line source code of *only* the specific file (or class/function) it was assigned to modify.
* **Foreign Interfaces (Skeleton View):** Strict AST skeleton (signatures only) of external dependencies required by the ticket.
* **What it Ignores:** Epic/Track goals, Tech Lead's Curated View, other files in the same directory, parallel Tickets.
* **Output Format:** XML Tags (`<file_path>`, `<file_content>`) defining direct file modifications or `mcp_client.py` tool payloads.
### Path B: Trial and Error (Local Iteration & Tool Execution)
* **Trigger:** The Contributor runs a local linter/test, encounters a syntax error, or the human pauses execution using "Step" mode.
* **What it Sees (Context):**
* **Ephemeral Working History:** A short, rolling window of its last 23 attempts (e.g., "Attempt 1: Wrote code -> Tool Output: SyntaxError").
* **Tier 4 (QA) Injections:** Compressed (20-50 token) fix recommendations from Tier 4 agents (e.g., "Add a closing bracket on line 42").
* **Human Mutations:** Any direct edits made to its JSON history payload before proceeding.
* **What it Ignores:** Tech Lead code reviews, attempts older than the rolling window (wiped to save tokens).
* **Output Format:** Revised tool payloads until tests pass or the human approves.
### Path C: Task Submission (Micro-Pull Request)
* **Trigger:** The code executes cleanly, and "Step" mode is finalized into "Task Complete."
* **What it Sees (Context):**
* **The Original Ticket:** To confirm instructions were met.
* **The Final State:** The cleanly modified file or exact diff.
* **What it Ignores:** **All of Path B.** Before submission to Tier 2, the orchestrator wipes the messy trial-and-error history from the payload.
* **Output Format:** A concise completion message and the clean diff, sent up to Tier 2.
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# Tier 4: The Utility Agents (Compiler / QA)
**Designated Models:** DeepSeek V3 (Lowest cost possible).
**Execution Frequency:** On-demand (Intercepts local failures).
**Core Role:** Single-shot, stateless translation of machine garbage into human English.
Tier 4 acts as the financial firewall. It solves the expensive problem of feeding massive (e.g., 3,000-token) stack traces back into a mid-tier LLM's context window. Tier 4 agents wake up, translate errors, and immediately die.
## Memory Context & Paths
### Path A: The Stack Trace Interceptor (Translator)
* **Trigger:** A Tier 3 Contributor executes a script, resulting in a non-zero exit code with a massive `stderr` payload.
* **What it Sees (Context):**
* **Raw Error Output:** The exact traceback from the runtime/compiler.
* **Offending Snippet:** *Only* the specific function or 20-line block of code where the error originated.
* **What it Ignores:** Everything else. It is blind to the "Why" and focuses only on "What broke."
* **Output Format:** A surgical, highly compressed string (20-50 tokens) passed back into the Tier 3 Contributor's working memory (e.g., "Syntax Error on line 42: You missed a closing parenthesis. Add `]`").
### Path B: The Linter / Formatter (Pedant)
* **Trigger:** Tier 3 believes it finished a Ticket, but pre-commit hooks (e.g., `ruff`, `eslint`) fail.
* **What it Sees (Context):**
* **Linter Warning:** Specific error (e.g., "Line too long", "Missing type hint").
* **Target File:** Code written by Tier 3.
* **What it Ignores:** Business logic. It only cares about styling rules.
* **Output Format:** A direct `sed` command or silent diff overwrite via tools to fix the formatting without bothering Tier 2 or consuming Tier 3 loops.
### Path C: The Flaky Test Debugger (Isolator)
* **Trigger:** A localized unit test fails due to logic (e.g., `assert 5 == 4`), not a syntax crash.
* **What it Sees (Context):**
* **Failing Test Function:** The exact `pytest` or `go test` block.
* **Target Function:** The specific function being tested.
* **What it Ignores:** The rest of the test suite and module.
* **Output Format:** A quick diagnosis sent to Tier 3 (e.g., "The test expects an integer, but your function is currently returning a stringified float. Cast to `int`").
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# Skill: MMA Tiered Orchestrator
## Description
This skill enforces the 4-Tier Hierarchical Multi-Model Architecture (MMA) directly within the Gemini CLI using Token Firewalling and sub-agent task delegation. It teaches the CLI how to act as a Tier 1/2 Orchestrator, dispatching stateless tasks to cheaper models using shell commands, thereby preventing massive error traces or heavy coding contexts from polluting the primary prompt context.
<instructions>
# MMA Token Firewall & Tiered Delegation Protocol
You are operating as a Tier 1 Product Manager or Tier 2 Tech Lead within the MMA Framework. Your context window is extremely valuable and must be protected from token bloat (such as raw, repetitive code edits, trial-and-error histories, or massive stack traces).
To accomplish this, you MUST delegate token-heavy or stateless tasks to "Tier 3 Contributors" or "Tier 4 QA Agents" by spawning secondary Gemini CLI instances via `run_shell_command`.
**CRITICAL Prerequisite:**
To avoid hanging the CLI and ensure proper environment authentication, you MUST NOT call the `gemini` command directly. Instead, you MUST use the wrapper script:
`.\scripts\run_subagent.ps1 -Prompt "..."`
## 1. The Tier 3 Worker (Heads-Down Coding)
When you need to perform a significant code modification (e.g., refactoring a 500-line script, writing a massive class, or implementing a predefined spec):
1. **DO NOT** attempt to write or use `replace`/`write_file` yourself. Your history will bloat.
2. **DO** construct a single, highly specific prompt.
3. **DO** spawn a sub-agent using `run_shell_command` pointing to the target file.
*Command:* `.\scripts\run_subagent.ps1 -Prompt "Modify [FILE_PATH] to implement [SPECIFIC_INSTRUCTION]. Only write the code, no pleasantries."`
4. If you need the sub-agent to automatically apply changes instead of just returning the text, use `gemini run` or pipe the output appropriately. However, the best method is to let the sub-agent modify the code and return "Done."
## 2. The Tier 4 QA Agent (Error Translation)
If you run a local test (e.g., `npm test`, `pytest`, `go run`) via `run_shell_command` and it fails with a massive traceback (e.g., 200+ lines of `stderr`):
1. **DO NOT** analyze the raw `stderr` in your own context window.
2. **DO** immediately spawn a stateless Tier 4 agent to compress the error.
3. *Command:* `.\scripts\run_subagent.ps1 -Prompt "Summarize this stack trace into a 20-word fix: [PASTE_SNIPPET_OF_STDERR_HERE]"`
4. Use the 20-word fix returned by the Tier 4 agent to inform your next architectural decision or pass it to the Tier 3 worker.
## 3. Context Amnesia (Phase Checkpoints)
When you complete a major Phase or Track within the `conductor` workflow:
1. Stage your changes and commit them.
2. Draft a comprehensive summary of the state changes in a Git Note attached to the commit.
3. Treat the checkpoint as a "Memory Wipe." Actively disregard previous conversational turns and trial-and-error histories. Rely exclusively on the newly generated Git Note and the physical state of the files on disk for your next Phase.
</instructions>
<examples>
### Example 1: Spawning a Tier 4 QA Agent
**User / System:** `pytest tests/test_gui.py` failed with 400 lines of output.
**Agent (You):**
```json
{
"command": ".\\scripts\\run_subagent.ps1 -Prompt \"Summarize this stack trace into a 20-word fix: [snip first 30 lines...]\"",
"description": "Spawning Tier 4 QA to compress error trace statelessly."
}
```
### Example 2: Spawning a Tier 3 Worker
**User:** Please implement the `ASTParser` class in `file_cache.py` as defined in Track 1.
**Agent (You):**
```json
{
"command": ".\\scripts\\run_subagent.ps1 -Prompt \"Read file_cache.py and implement the ASTParser class using tree-sitter. Ensure you preserve docstrings but strip function bodies. Output the updated code or edit the file directly.\"",
"description": "Delegating implementation to a Tier 3 Worker."
}
```
</examples>
<triggers>
- When asked to write large amounts of boilerplate or repetitive code.
- When encountering a large error trace from a shell execution.
- When explicitly instructed to act as a "Tech Lead" or "Orchestrator".
- When managing complex, multi-file Track implementations.
</triggers>
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- `uv` - package/env management - `uv` - package/env management
**Files:** **Files:**
- `gui.py` - main GUI, `App` class, all panels, all callbacks, confirmation dialog, layout persistence, rich comms rendering; `[+ Maximize]` buttons in `ConfirmDialog` and `win_script_output` now pass text directly as `user_data` / read from `self._last_script` / `self._last_output` instance vars instead of `dpg.get_value(tag)` — fixes glitch when word-wrap is ON or dialog is dismissed before viewer opens - `gui_legacy.py` - main GUI, `App` class, all panels, all callbacks, confirmation dialog, layout persistence, rich comms rendering; `[+ Maximize]` buttons in `ConfirmDialog` and `win_script_output` now pass text directly as `user_data` / read from `self._last_script` / `self._last_output` instance vars instead of `dpg.get_value(tag)` — fixes glitch when word-wrap is ON or dialog is dismissed before viewer opens
- `ai_client.py` - unified provider wrapper, model listing, session management, send, tool/function-call loop, comms log, provider error classification, token estimation, and aggressive history truncation - `ai_client.py` - unified provider wrapper, model listing, session management, send, tool/function-call loop, comms log, provider error classification, token estimation, and aggressive history truncation
- `aggregate.py` - reads config, collects files/screenshots/discussion, builds `file_items` with `mtime` for cache optimization, writes numbered `.md` files to `output_dir` using `build_markdown_from_items` to avoid double I/O; `run()` returns `(markdown_str, path, file_items)` tuple; `summary_only=False` by default (full file contents sent, not heuristic summaries) - `aggregate.py` - reads config, collects files/screenshots/discussion, builds `file_items` with `mtime` for cache optimization, writes numbered `.md` files to `output_dir` using `build_markdown_from_items` to avoid double I/O; `run()` returns `(markdown_str, path, file_items)` tuple; `summary_only=False` by default (full file contents sent, not heuristic summaries)
- `shell_runner.py` - subprocess wrapper that runs PowerShell scripts sandboxed to `base_dir`, returns stdout/stderr/exit code as a string - `shell_runner.py` - subprocess wrapper that runs PowerShell scripts sandboxed to `base_dir`, returns stdout/stderr/exit code as a string
@@ -79,7 +79,7 @@ Is a local GUI tool for manually curating and sending context to AI APIs. It agg
- Both Gemini and Anthropic are configured with a `run_powershell` tool/function declaration - Both Gemini and Anthropic are configured with a `run_powershell` tool/function declaration
- When the AI wants to edit or create files it emits a tool call with a `script` string - When the AI wants to edit or create files it emits a tool call with a `script` string
- `ai_client` runs a loop (max `MAX_TOOL_ROUNDS = 10`) feeding tool results back until the AI stops calling tools - `ai_client` runs a loop (max `MAX_TOOL_ROUNDS = 10`) feeding tool results back until the AI stops calling tools
- Before any script runs, `gui.py` shows a modal `ConfirmDialog` on the main thread; the background send thread blocks on a `threading.Event` until the user clicks Approve or Reject - Before any script runs, `gui_legacy.py` shows a modal `ConfirmDialog` on the main thread; the background send thread blocks on a `threading.Event` until the user clicks Approve or Reject
- The dialog displays `base_dir`, shows the script in an editable text box (allowing last-second tweaks), and has Approve & Run / Reject buttons - The dialog displays `base_dir`, shows the script in an editable text box (allowing last-second tweaks), and has Approve & Run / Reject buttons
- On approval the (possibly edited) script is passed to `shell_runner.run_powershell()` which prepends `Set-Location -LiteralPath '<base_dir>'` and runs it via `powershell -NoProfile -NonInteractive -Command` - On approval the (possibly edited) script is passed to `shell_runner.run_powershell()` which prepends `Set-Location -LiteralPath '<base_dir>'` and runs it via `powershell -NoProfile -NonInteractive -Command`
- stdout, stderr, and exit code are returned to the AI as the tool result - stdout, stderr, and exit code are returned to the AI as the tool result
@@ -107,10 +107,10 @@ Is a local GUI tool for manually curating and sending context to AI APIs. It agg
- Entry fields: `ts` (HH:MM:SS), `direction` (OUT/IN), `kind`, `provider`, `model`, `payload` (dict) - Entry fields: `ts` (HH:MM:SS), `direction` (OUT/IN), `kind`, `provider`, `model`, `payload` (dict)
- Anthropic responses also include `usage` (input_tokens, output_tokens, cache_creation_input_tokens, cache_read_input_tokens) and `stop_reason` in payload - Anthropic responses also include `usage` (input_tokens, output_tokens, cache_creation_input_tokens, cache_read_input_tokens) and `stop_reason` in payload
- `get_comms_log()` returns a snapshot; `clear_comms_log()` empties it - `get_comms_log()` returns a snapshot; `clear_comms_log()` empties it
- `comms_log_callback` (injected by gui.py) is called from the background thread with each new entry; gui queues entries in `_pending_comms` (lock-protected) and flushes them to the DPG panel each render frame - `comms_log_callback` (injected by gui_legacy.py) is called from the background thread with each new entry; gui queues entries in `_pending_comms` (lock-protected) and flushes them to the DPG panel each render frame
- `COMMS_CLAMP_CHARS = 300` in gui.py governs the display cutoff for heavy text fields - `COMMS_CLAMP_CHARS = 300` in gui_legacy.py governs the display cutoff for heavy text fields
**Comms History panel — rich structured rendering (gui.py):** **Comms History panel — rich structured rendering (gui_legacy.py):**
Rather than showing raw JSON, each comms entry is rendered using a kind-specific renderer function. Unknown kinds fall back to a generic key/value layout. Rather than showing raw JSON, each comms entry is rendered using a kind-specific renderer function. Unknown kinds fall back to a generic key/value layout.
@@ -195,10 +195,10 @@ Entry layout: index + timestamp + direction + kind + provider/model header row,
- Comms log: MCP tool calls log `OUT/tool_call` with `{"name": ..., "args": {...}}` and `IN/tool_result` with `{"name": ..., "output": ...}`; rendered in the Comms History panel via `_render_payload_tool_call` (shows each arg key/value) and `_render_payload_tool_result` (shows output) - Comms log: MCP tool calls log `OUT/tool_call` with `{"name": ..., "args": {...}}` and `IN/tool_result` with `{"name": ..., "output": ...}`; rendered in the Comms History panel via `_render_payload_tool_call` (shows each arg key/value) and `_render_payload_tool_result` (shows output)
**Known extension points:** **Known extension points:**
- Add more providers by adding a section to `credentials.toml`, a `_list_*` and `_send_*` function in `ai_client.py`, and the provider name to the `PROVIDERS` list in `gui.py` - Add more providers by adding a section to `credentials.toml`, a `_list_*` and `_send_*` function in `ai_client.py`, and the provider name to the `PROVIDERS` list in `gui_legacy.py`
- Discussion history excerpts could be individually toggleable for inclusion in the generated md - Discussion history excerpts could be individually toggleable for inclusion in the generated md
- `MAX_TOOL_ROUNDS` in `ai_client.py` caps agentic loops at 10 rounds; adjustable - `MAX_TOOL_ROUNDS` in `ai_client.py` caps agentic loops at 10 rounds; adjustable
- `COMMS_CLAMP_CHARS` in `gui.py` controls the character threshold for clamping heavy payload fields in the Comms History panel - `COMMS_CLAMP_CHARS` in gui_legacy.py controls the character threshold for clamping heavy payload fields in the Comms History panel
- Additional project metadata (description, tags, created date) could be added to `[project]` in the per-project toml - Additional project metadata (description, tags, created date) could be added to `[project]` in the per-project toml
### Gemini Context Management ### Gemini Context Management
@@ -222,7 +222,7 @@ Entry layout: index + timestamp + direction + kind + provider/model header row,
## Recent Changes (Text Viewer Maximization) ## Recent Changes (Text Viewer Maximization)
- **Global Text Viewer (gui.py)**: Added a dedicated, large popup window (win_text_viewer) to allow reading and scrolling through large, dense text blocks without feeling cramped. - **Global Text Viewer (gui_legacy.py)**: Added a dedicated, large popup window (win_text_viewer) to allow reading and scrolling through large, dense text blocks without feeling cramped.
- **Comms History**: Every multi-line text field in the comms log now has a [+] button next to its label that opens the text in the Global Text Viewer. - **Comms History**: Every multi-line text field in the comms log now has a [+] button next to its label that opens the text in the Global Text Viewer.
- **Tool Log History**: Added [+ Script] and [+ Output] buttons next to each logged tool call to easily maximize and read the full executed scripts and raw tool outputs. - **Tool Log History**: Added [+ Script] and [+ Output] buttons next to each logged tool call to easily maximize and read the full executed scripts and raw tool outputs.
- **Last Script Output Popup**: Expanded the default size of the popup (now 800x600) and gave the input script panel more vertical space to prevent it from feeling 'scrunched'. Added [+ Maximize] buttons for both the script and the output sections to inspect them in full detail. - **Last Script Output Popup**: Expanded the default size of the popup (now 800x600) and gave the input script panel more vertical space to prevent it from feeling 'scrunched'. Added [+ Maximize] buttons for both the script and the output sections to inspect them in full detail.
@@ -266,10 +266,10 @@ Documentation has been completely rewritten matching the strict, structural form
### aggregate.py — run() double-I/O elimination ### aggregate.py — run() double-I/O elimination
- `run()` now calls `build_file_items()` once, then passes the result to `build_markdown_from_items()` instead of calling `build_files_section()` separately. This avoids reading every file twice per send. - `run()` now calls `build_file_items()` once, then passes the result to `build_markdown_from_items()` instead of calling `build_files_section()` separately. This avoids reading every file twice per send.
- `build_markdown_from_items()` accepts a `summary_only` flag (default `False`); when `False` it inlines full file content; when `True` it delegates to `summarize.build_summary_markdown()` for compact structural summaries. - `build_markdown_from_items()` accepts a `summary_only` flag (default `False`); when `False` it inlines full file content; when `True` it delegates to `summarize.build_summary_markdown()` for compact structural summaries.
- `run()` returns a 3-tuple `(markdown_str, output_path, file_items)` — the `file_items` list is passed through to `gui.py` as `self.last_file_items` for dynamic context refresh after tool calls. - `run()` returns a 3-tuple `(markdown_str, output_path, file_items)` — the `file_items` list is passed through to `gui_legacy.py` as `self.last_file_items` for dynamic context refresh after tool calls.
## Updates (2026-02-22 — gui.py [+ Maximize] bug fix) ## Updates (2026-02-22 — gui_legacy.py [+ Maximize] bug fix)
### Problem ### Problem
Three `[+ Maximize]` buttons were reading their text content via `dpg.get_value(tag)` at click time: Three `[+ Maximize]` buttons were reading their text content via `dpg.get_value(tag)` at click time:
+1 -1
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@@ -41,5 +41,5 @@ api_key = "****"
2. Have fun. This is experiemntal slop. 2. Have fun. This is experiemntal slop.
```ps1 ```ps1
uv run .\gui.py uv run .\gui_2.py
``` ```
+48 -8
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@@ -16,6 +16,7 @@ import re
import glob import glob
from pathlib import Path, PureWindowsPath from pathlib import Path, PureWindowsPath
import summarize import summarize
import project_manager
def find_next_increment(output_dir: Path, namespace: str) -> int: def find_next_increment(output_dir: Path, namespace: str) -> int:
pattern = re.compile(rf"^{re.escape(namespace)}_(\d+)\.md$") pattern = re.compile(rf"^{re.escape(namespace)}_(\d+)\.md$")
@@ -37,14 +38,24 @@ def is_absolute_with_drive(entry: str) -> bool:
def resolve_paths(base_dir: Path, entry: str) -> list[Path]: def resolve_paths(base_dir: Path, entry: str) -> list[Path]:
has_drive = is_absolute_with_drive(entry) has_drive = is_absolute_with_drive(entry)
is_wildcard = "*" in entry is_wildcard = "*" in entry
matches = []
if is_wildcard: if is_wildcard:
root = Path(entry) if has_drive else base_dir / entry root = Path(entry) if has_drive else base_dir / entry
matches = [Path(p) for p in glob.glob(str(root), recursive=True) if Path(p).is_file()] matches = [Path(p) for p in glob.glob(str(root), recursive=True) if Path(p).is_file()]
return sorted(matches)
else: else:
if has_drive: p = Path(entry) if has_drive else (base_dir / entry).resolve()
return [Path(entry)] matches = [p]
return [(base_dir / entry).resolve()]
# Blacklist filter
filtered = []
for p in matches:
name = p.name.lower()
if name == "history.toml" or name.endswith("_history.toml"):
continue
filtered.append(p)
return sorted(filtered)
def build_discussion_section(history: list[str]) -> str: def build_discussion_section(history: list[str]) -> str:
sections = [] sections = []
@@ -164,6 +175,18 @@ def build_markdown_from_items(file_items: list[dict], screenshot_base_dir: Path,
return "\n\n---\n\n".join(parts) return "\n\n---\n\n".join(parts)
def build_markdown_no_history(file_items: list[dict], screenshot_base_dir: Path, screenshots: list[str], summary_only: bool = False) -> str:
"""Build markdown with only files + screenshots (no history). Used for stable caching."""
return build_markdown_from_items(file_items, screenshot_base_dir, screenshots, history=[], summary_only=summary_only)
def build_discussion_text(history: list[str]) -> str:
"""Build just the discussion history section text. Returns empty string if no history."""
if not history:
return ""
return "## Discussion History\n\n" + build_discussion_section(history)
def build_markdown(base_dir: Path, files: list[str], screenshot_base_dir: Path, screenshots: list[str], history: list[str], summary_only: bool = False) -> str: def build_markdown(base_dir: Path, files: list[str], screenshot_base_dir: Path, screenshots: list[str], history: list[str], summary_only: bool = False) -> str:
parts = [] parts = []
# STATIC PREFIX: Files and Screenshots must go first to maximize Cache Hits # STATIC PREFIX: Files and Screenshots must go first to maximize Cache Hits
@@ -195,15 +218,32 @@ def run(config: dict) -> tuple[str, Path, list[dict]]:
output_file = output_dir / f"{namespace}_{increment:03d}.md" output_file = output_dir / f"{namespace}_{increment:03d}.md"
# Build file items once, then construct markdown from them (avoids double I/O) # Build file items once, then construct markdown from them (avoids double I/O)
file_items = build_file_items(base_dir, files) file_items = build_file_items(base_dir, files)
summary_only = config.get("project", {}).get("summary_only", False)
markdown = build_markdown_from_items(file_items, screenshot_base_dir, screenshots, history, markdown = build_markdown_from_items(file_items, screenshot_base_dir, screenshots, history,
summary_only=False) summary_only=summary_only)
output_file.write_text(markdown, encoding="utf-8") output_file.write_text(markdown, encoding="utf-8")
return markdown, output_file, file_items return markdown, output_file, file_items
def main(): def main():
with open("config.toml", "rb") as f: # Load global config to find active project
import tomllib config_path = Path("config.toml")
config = tomllib.load(f) if not config_path.exists():
print("config.toml not found.")
return
with open(config_path, "rb") as f:
global_cfg = tomllib.load(f)
active_path = global_cfg.get("projects", {}).get("active")
if not active_path:
print("No active project found in config.toml.")
return
# Use project_manager to load project (handles history segregation)
proj = project_manager.load_project(active_path)
# Use flat_config to make it compatible with aggregate.run()
config = project_manager.flat_config(proj)
markdown, output_file, _ = run(config) markdown, output_file, _ = run(config)
print(f"Written: {output_file}") print(f"Written: {output_file}")
+739 -76
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+89 -7
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@@ -3,12 +3,12 @@ import json
import time import time
class ApiHookClient: class ApiHookClient:
def __init__(self, base_url="http://127.0.0.1:8999", max_retries=5, retry_delay=2): def __init__(self, base_url="http://127.0.0.1:8999", max_retries=2, retry_delay=0.1):
self.base_url = base_url self.base_url = base_url
self.max_retries = max_retries self.max_retries = max_retries
self.retry_delay = retry_delay self.retry_delay = retry_delay
def wait_for_server(self, timeout=10): def wait_for_server(self, timeout=3):
""" """
Polls the /status endpoint until the server is ready or timeout is reached. Polls the /status endpoint until the server is ready or timeout is reached.
""" """
@@ -18,20 +18,23 @@ class ApiHookClient:
if self.get_status().get('status') == 'ok': if self.get_status().get('status') == 'ok':
return True return True
except (requests.exceptions.ConnectionError, requests.exceptions.Timeout): except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
time.sleep(0.5) time.sleep(0.1)
return False return False
def _make_request(self, method, endpoint, data=None): def _make_request(self, method, endpoint, data=None, timeout=None):
url = f"{self.base_url}{endpoint}" url = f"{self.base_url}{endpoint}"
headers = {'Content-Type': 'application/json'} headers = {'Content-Type': 'application/json'}
last_exception = None last_exception = None
# Lower request timeout for local server by default
req_timeout = timeout if timeout is not None else 0.5
for attempt in range(self.max_retries + 1): for attempt in range(self.max_retries + 1):
try: try:
if method == 'GET': if method == 'GET':
response = requests.get(url, timeout=5) response = requests.get(url, timeout=req_timeout)
elif method == 'POST': elif method == 'POST':
response = requests.post(url, json=data, headers=headers, timeout=5) response = requests.post(url, json=data, headers=headers, timeout=req_timeout)
else: else:
raise ValueError(f"Unsupported HTTP method: {method}") raise ValueError(f"Unsupported HTTP method: {method}")
@@ -59,7 +62,7 @@ class ApiHookClient:
"""Checks the health of the hook server.""" """Checks the health of the hook server."""
url = f"{self.base_url}/status" url = f"{self.base_url}/status"
try: try:
response = requests.get(url, timeout=1) response = requests.get(url, timeout=0.2)
response.raise_for_status() response.raise_for_status()
return response.json() return response.json()
except Exception: except Exception:
@@ -108,6 +111,46 @@ class ApiHookClient:
"value": value "value": value
}) })
def get_value(self, item):
"""Gets the value of a GUI item via its mapped field."""
try:
# First try direct field querying via POST
res = self._make_request('POST', '/api/gui/value', data={"field": item})
if res and "value" in res:
v = res.get("value")
if v is not None:
return v
except Exception:
pass
try:
# Try GET fallback
res = self._make_request('GET', f'/api/gui/value/{item}')
if res and "value" in res:
v = res.get("value")
if v is not None:
return v
except Exception:
pass
try:
# Fallback for thinking/live/prior which are in diagnostics
diag = self._make_request('GET', '/api/gui/diagnostics')
if item in diag:
return diag[item]
# Map common indicator tags to diagnostics keys
mapping = {
"thinking_indicator": "thinking",
"operations_live_indicator": "live",
"prior_session_indicator": "prior"
}
key = mapping.get(item)
if key and key in diag:
return diag[key]
except Exception:
pass
return None
def click(self, item, *args, **kwargs): def click(self, item, *args, **kwargs):
"""Simulates a click on a GUI button or item.""" """Simulates a click on a GUI button or item."""
user_data = kwargs.pop('user_data', None) user_data = kwargs.pop('user_data', None)
@@ -133,3 +176,42 @@ class ApiHookClient:
return {"tag": tag, "shown": diag.get(key, False)} return {"tag": tag, "shown": diag.get(key, False)}
except Exception as e: except Exception as e:
return {"tag": tag, "shown": False, "error": str(e)} return {"tag": tag, "shown": False, "error": str(e)}
def get_events(self):
"""Fetches and clears the event queue from the server."""
try:
return self._make_request('GET', '/api/events').get("events", [])
except Exception:
return []
def wait_for_event(self, event_type, timeout=5):
"""Polls for a specific event type."""
start = time.time()
while time.time() - start < timeout:
events = self.get_events()
for ev in events:
if ev.get("type") == event_type:
return ev
time.sleep(0.1) # Fast poll
return None
def wait_for_value(self, item, expected, timeout=5):
"""Polls until get_value(item) == expected."""
start = time.time()
while time.time() - start < timeout:
if self.get_value(item) == expected:
return True
time.sleep(0.1) # Fast poll
return False
def reset_session(self):
"""Simulates clicking the 'Reset Session' button in the GUI."""
return self.click("btn_reset")
def request_confirmation(self, tool_name, args):
"""Asks the user for confirmation via the GUI (blocking call)."""
# Using a long timeout as this waits for human input (60 seconds)
res = self._make_request('POST', '/api/ask',
data={'type': 'tool_approval', 'tool': tool_name, 'args': args},
timeout=60.0)
return res.get('response')
+165 -6
View File
@@ -1,10 +1,11 @@
import json import json
import threading import threading
from http.server import HTTPServer, BaseHTTPRequestHandler import uuid
from http.server import ThreadingHTTPServer, BaseHTTPRequestHandler
import logging import logging
import session_logger import session_logger
class HookServerInstance(HTTPServer): class HookServerInstance(ThreadingHTTPServer):
"""Custom HTTPServer that carries a reference to the main App instance.""" """Custom HTTPServer that carries a reference to the main App instance."""
def __init__(self, server_address, RequestHandlerClass, app): def __init__(self, server_address, RequestHandlerClass, app):
super().__init__(server_address, RequestHandlerClass) super().__init__(server_address, RequestHandlerClass)
@@ -42,17 +43,94 @@ class HookHandler(BaseHTTPRequestHandler):
if hasattr(app, 'perf_monitor'): if hasattr(app, 'perf_monitor'):
metrics = app.perf_monitor.get_metrics() metrics = app.perf_monitor.get_metrics()
self.wfile.write(json.dumps({'performance': metrics}).encode('utf-8')) self.wfile.write(json.dumps({'performance': metrics}).encode('utf-8'))
elif self.path == '/api/events':
# Long-poll or return current event queue
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self.end_headers()
events = []
if hasattr(app, '_api_event_queue'):
with app._api_event_queue_lock:
events = list(app._api_event_queue)
app._api_event_queue.clear()
self.wfile.write(json.dumps({'events': events}).encode('utf-8'))
elif self.path == '/api/gui/value':
# POST with {"field": "field_tag"} to get value
content_length = int(self.headers.get('Content-Length', 0))
body = self.rfile.read(content_length)
data = json.loads(body.decode('utf-8'))
field_tag = data.get("field")
print(f"[DEBUG] Hook Server: get_value for {field_tag}")
event = threading.Event()
result = {"value": None}
def get_val():
try:
if field_tag in app._settable_fields:
attr = app._settable_fields[field_tag]
val = getattr(app, attr, None)
print(f"[DEBUG] Hook Server: attr={attr}, val={val}")
result["value"] = val
else:
print(f"[DEBUG] Hook Server: {field_tag} NOT in settable_fields")
finally:
event.set()
with app._pending_gui_tasks_lock:
app._pending_gui_tasks.append({
"action": "custom_callback",
"callback": get_val
})
if event.wait(timeout=2):
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self.end_headers()
self.wfile.write(json.dumps(result).encode('utf-8'))
else:
self.send_response(504)
self.end_headers()
elif self.path.startswith('/api/gui/value/'):
# Generic endpoint to get the value of any settable field
field_tag = self.path.split('/')[-1]
event = threading.Event()
result = {"value": None}
def get_val():
try:
if field_tag in app._settable_fields:
attr = app._settable_fields[field_tag]
result["value"] = getattr(app, attr, None)
finally:
event.set()
with app._pending_gui_tasks_lock:
app._pending_gui_tasks.append({
"action": "custom_callback",
"callback": get_val
})
if event.wait(timeout=2):
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self.end_headers()
self.wfile.write(json.dumps(result).encode('utf-8'))
else:
self.send_response(504)
self.end_headers()
elif self.path == '/api/gui/diagnostics': elif self.path == '/api/gui/diagnostics':
# Safe way to query multiple states at once via the main thread queue # Safe way to query multiple states at once via the main thread queue
event = threading.Event() event = threading.Event()
result = {} result = {}
def check_all(): def check_all():
import dearpygui.dearpygui as dpg
try: try:
result["thinking"] = dpg.is_item_shown("thinking_indicator") if dpg.does_item_exist("thinking_indicator") else False # Generic state check based on App attributes (works for both DPG and ImGui versions)
result["live"] = dpg.is_item_shown("operations_live_indicator") if dpg.does_item_exist("operations_live_indicator") else False status = getattr(app, "ai_status", "idle")
result["prior"] = dpg.is_item_shown("prior_session_indicator") if dpg.does_item_exist("prior_session_indicator") else False result["thinking"] = status in ["sending...", "running powershell..."]
result["live"] = status in ["running powershell...", "fetching url...", "searching web...", "powershell done, awaiting AI..."]
result["prior"] = getattr(app, "is_viewing_prior_session", False)
finally: finally:
event.set() event.set()
@@ -108,6 +186,75 @@ class HookHandler(BaseHTTPRequestHandler):
self.end_headers() self.end_headers()
self.wfile.write( self.wfile.write(
json.dumps({'status': 'queued'}).encode('utf-8')) json.dumps({'status': 'queued'}).encode('utf-8'))
elif self.path == '/api/ask':
request_id = str(uuid.uuid4())
event = threading.Event()
if not hasattr(app, '_pending_asks'):
app._pending_asks = {}
if not hasattr(app, '_ask_responses'):
app._ask_responses = {}
app._pending_asks[request_id] = event
# Emit event for test/client discovery
with app._api_event_queue_lock:
app._api_event_queue.append({
"type": "ask_received",
"request_id": request_id,
"data": data
})
with app._pending_gui_tasks_lock:
app._pending_gui_tasks.append({
"type": "ask",
"request_id": request_id,
"data": data
})
if event.wait(timeout=60.0):
response_data = app._ask_responses.get(request_id)
# Clean up response after reading
if request_id in app._ask_responses:
del app._ask_responses[request_id]
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self.end_headers()
self.wfile.write(json.dumps({'status': 'ok', 'response': response_data}).encode('utf-8'))
else:
if request_id in app._pending_asks:
del app._pending_asks[request_id]
self.send_response(504)
self.end_headers()
self.wfile.write(json.dumps({'error': 'timeout'}).encode('utf-8'))
elif self.path == '/api/ask/respond':
request_id = data.get('request_id')
response_data = data.get('response')
if request_id and hasattr(app, '_pending_asks') and request_id in app._pending_asks:
app._ask_responses[request_id] = response_data
event = app._pending_asks[request_id]
event.set()
# Clean up pending ask entry
del app._pending_asks[request_id]
# Queue GUI task to clear the dialog
with app._pending_gui_tasks_lock:
app._pending_gui_tasks.append({
"action": "clear_ask",
"request_id": request_id
})
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self.end_headers()
self.wfile.write(json.dumps({'status': 'ok'}).encode('utf-8'))
else:
self.send_response(404)
self.end_headers()
else: else:
self.send_response(404) self.send_response(404)
self.end_headers() self.end_headers()
@@ -137,6 +284,18 @@ class HookServer:
if not hasattr(self.app, '_pending_gui_tasks_lock'): if not hasattr(self.app, '_pending_gui_tasks_lock'):
self.app._pending_gui_tasks_lock = threading.Lock() self.app._pending_gui_tasks_lock = threading.Lock()
# Initialize ask-related dictionaries
if not hasattr(self.app, '_pending_asks'):
self.app._pending_asks = {}
if not hasattr(self.app, '_ask_responses'):
self.app._ask_responses = {}
# Event queue for test script subscriptions
if not hasattr(self.app, '_api_event_queue'):
self.app._api_event_queue = []
if not hasattr(self.app, '_api_event_queue_lock'):
self.app._api_event_queue_lock = threading.Lock()
self.server = HookServerInstance(('127.0.0.1', self.port), HookHandler, self.app) self.server = HookServerInstance(('127.0.0.1', self.port), HookHandler, self.app)
self.thread = threading.Thread(target=self.server.serve_forever, daemon=True) self.thread = threading.Thread(target=self.server.serve_forever, daemon=True)
self.thread.start() self.thread.start()
@@ -0,0 +1,5 @@
# Track deepseek_support_20260225 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "deepseek_support_20260225",
"type": "feature",
"status": "new",
"created_at": "2026-02-25T00:00:00Z",
"updated_at": "2026-02-25T00:00:00Z",
"description": "Add support for the deepseek api as a provider."
}
@@ -0,0 +1,27 @@
# Implementation Plan: DeepSeek API Provider Support
## Phase 1: Infrastructure & Common Logic [checkpoint: 0ec3720]
- [x] Task: Initialize MMA Environment `activate_skill mma-orchestrator` 1b3ff23
- [x] Task: Update `credentials.toml` schema and configuration logic in `project_manager.py` to support `deepseek` 1b3ff23
- [x] Task: Define the `DeepSeekProvider` interface in `ai_client.py` and align with existing provider patterns 1b3ff23
- [x] Task: Conductor - User Manual Verification 'Infrastructure & Common Logic' (Protocol in workflow.md) 1b3ff23
## Phase 2: DeepSeek API Client Implementation
- [x] Task: Write failing tests for `DeepSeekProvider` model selection and basic completion
- [x] Task: Implement `DeepSeekProvider` using the dedicated SDK
- [x] Task: Write failing tests for streaming and tool calling parity in `DeepSeekProvider`
- [x] Task: Implement streaming and tool calling logic for DeepSeek models
- [x] Task: Conductor - User Manual Verification 'DeepSeek API Client Implementation' (Protocol in workflow.md)
## Phase 3: Reasoning Traces & Advanced Capabilities
- [x] Task: Write failing tests for reasoning trace capture in `DeepSeekProvider` (DeepSeek-R1)
- [x] Task: Implement reasoning trace processing and integration with discussion history
- [x] Task: Write failing tests for token estimation and cost tracking for DeepSeek models
- [x] Task: Implement token usage tracking according to DeepSeek pricing
- [x] Task: Conductor - User Manual Verification 'Reasoning Traces & Advanced Capabilities' (Protocol in workflow.md)
## Phase 4: GUI Integration & Final Verification
- [x] Task: Update `gui_2.py` and `theme_2.py` (if necessary) to include DeepSeek in the provider selection UI
- [x] Task: Implement automated regression tests for the full DeepSeek lifecycle (prompt, streaming, tool call, reasoning)
- [x] Task: Verify overall performance and UI responsiveness with the new provider
- [x] Task: Conductor - User Manual Verification 'GUI Integration & Final Verification' (Protocol in workflow.md)
@@ -0,0 +1,31 @@
# Specification: DeepSeek API Provider Support
## Overview
Implement a new AI provider module to support the DeepSeek API within the Manual Slop application. This integration will leverage a dedicated SDK to provide access to high-performance models (DeepSeek-V3 and DeepSeek-R1) with support for streaming, tool calling, and detailed reasoning traces.
## Functional Requirements
- **Dedicated SDK Integration:** Utilize a DeepSeek-specific Python client for API interactions.
- **Model Support:** Initial support for `deepseek-v3` (general performance) and `deepseek-r1` (reasoning).
- **Core Features:**
- **Streaming:** Support real-time response generation for a better user experience.
- **Tool Calling:** Integrate with Manual Slop's existing tool/function execution framework.
- **Reasoning Traces:** Capture and display reasoning paths if provided by the model (e.g., DeepSeek-R1).
- **Configuration Management:**
- Add `[deepseek]` section to `credentials.toml` for `api_key`.
- Update `config.toml` to allow selecting DeepSeek as the active provider.
## Non-Functional Requirements
- **Parity:** Maintain consistency with existing Gemini and Anthropic provider implementations in `ai_client.py`.
- **Error Handling:** Robust handling of API rate limits and connection issues specific to DeepSeek.
- **Observability:** Track token usage and costs according to DeepSeek's pricing model.
## Acceptance Criteria
- [ ] User can select "DeepSeek" as a provider in the GUI.
- [ ] Successful completion of prompts using both DeepSeek-V3 and DeepSeek-R1 models.
- [ ] Tool calling works correctly for standard operations (e.g., `read_file`).
- [ ] Reasoning traces from R1 are captured and visible in the discussion history.
- [ ] Streaming responses function correctly without blocking the GUI.
## Out of Scope
- Support for OpenAI-compatible proxies for DeepSeek in this initial track.
- Automated fine-tuning or custom model endpoints.
@@ -0,0 +1,5 @@
# Track gemini_cli_headless_20260224 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
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{
"track_id": "gemini_cli_headless_20260224",
"type": "feature",
"status": "new",
"created_at": "2026-02-24T23:45:00Z",
"updated_at": "2026-02-24T23:45:00Z",
"description": "Support gemini cli headless as an alternative to the raw client_api route. So that they user may use their gemini subscription and gemini cli features within manual slop for a more discliplined and visually enriched UX."
}
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# Implementation Plan: Gemini CLI Headless Integration
## Phase 1: IPC Infrastructure Extension [checkpoint: c0bccce]
- [x] Task: Extend `api_hooks.py` to support synchronous "Ask" requests. This involves adding a way for a client to POST a request and wait for a user response from the GUI. (1792107)
- [x] Task: Update `api_hook_client.py` with a `request_confirmation(tool_name, args)` method that blocks until the GUI responds. (93f640d)
- [x] Task: Create a standalone test script `tests/test_sync_hooks.py` to verify that the CLI-to-GUI communication works as expected. (1792107)
- [x] Task: Conductor - User Manual Verification 'Phase 1: IPC Infrastructure Extension' (Protocol in workflow.md) (c0bccce)
## Phase 2: Gemini CLI Adapter & Tool Bridge
- [x] Task: Implement `scripts/cli_tool_bridge.py`. This script will be called by the Gemini CLI `BeforeTool` hook and use `ApiHookClient` to talk to the GUI. (211000c)
- [x] Task: Implement the `GeminiCliAdapter` in `ai_client.py` (or a new `gemini_cli_adapter.py`). It must handle the `subprocess` lifecycle and parse the `stream-json` output. (b762a80)
- [x] Task: Integrate `GeminiCliAdapter` into the main `ai_client.send()` logic. (b762a80)
- [x] Task: Write unit tests for the JSON parsing and subprocess management in `GeminiCliAdapter`. (b762a80)
- [~] Task: Conductor - User Manual Verification 'Phase 2: Gemini CLI Adapter & Tool Bridge' (Protocol in workflow.md)
## Phase 3: GUI Integration & Provider Support
- [x] Task: Update `gui_2.py` to add "Gemini CLI" to the provider dropdown. (3ce4fa0)
- [x] Task: Implement UI elements for "Gemini CLI Session Management" (Login button, session ID display). (3ce4fa0)
- [x] Task: Update the `manual_slop.toml` logic to persist Gemini CLI specific settings (e.g., path to CLI, approval mode). (3ce4fa0)
- [~] Task: Conductor - User Manual Verification 'Phase 3: GUI Integration & Provider Support' (Protocol in workflow.md)
## Phase 4: Integration Testing & UX Polish
- [x] Task: Create a comprehensive integration test `tests/test_gemini_cli_integration.py` that uses the `live_gui` fixture to simulate a full session. (d187a6c)
- [x] Task: Verify tool confirmation flow: CLI Tool -> Bridge -> GUI Modal -> User Approval -> CLI Execution. (d187a6c)
- [x] Task: Polish the display of CLI telemetry (tokens/latency) in the GUI diagnostics panel. (1e5b43e)
- [x] Task: Conductor - User Manual Verification 'Phase 4: Integration Testing & UX Polish' (Protocol in workflow.md) (1e5b43e)
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# Specification: Gemini CLI Headless Integration
## Overview
This track integrates the `gemini` CLI as a headless backend provider for Manual Slop. This allows users to leverage their Gemini subscription and the CLI's advanced features (e.g., specialized sub-agents like `codebase_investigator`, structured JSON streaming, and robust session management) directly within the Manual Slop GUI.
## Goals
- Add "Gemini CLI" as a selectable AI provider in Manual Slop.
- Support both persistent interactive sessions and one-off task-specific delegation (e.g., running `gemini investigate`).
- Implement a secure "BeforeTool" hook to ensure all CLI-initiated tool calls are intercepted and confirmed via the Manual Slop GUI.
- Capture and display the CLI's visually enriched output (via JSONL stream) within the existing discussion history.
## Functional Requirements
### 1. Gemini CLI Provider Adapter
- **Implementation**: Create a `GeminiCliAdapter` class (or extend `ai_client.py`) that wraps the `gemini` CLI subprocess.
- **Communication**: Use `--output-format stream-json` to receive real-time updates (text chunks, tool calls, status).
- **Session Management**: Support session persistence by tracking the session ID and passing it to subsequent CLI calls.
- **Authentication**:
- Provide a "Login to Gemini CLI" action in the GUI that triggers `gemini login`.
- Support passing an API key via environment variables if configured in `manual_slop.toml`.
### 2. GUI Intercepted Tool Execution
- **Mechanism**: Use the Gemini CLI's `BeforeTool` hook.
- **Hook Helper**: A small Python script `scripts/cli_tool_bridge.py` will be registered as the `BeforeTool` hook.
- **IPC**: This bridge script will communicate with Manual Slop's `HookServer` (extending it to support synchronous "ask" requests).
- **Confirmation**: When a tool is requested, the bridge blocks until the user confirms/denies the action in the GUI, returning the decision as JSON to the CLI.
### 3. Visual & Telemetry Integration
- **Rich Output**: Parse the `stream-json` events to display markdown content and tool status in the GUI.
- **Telemetry**: Extract and display token usage and latency metrics provided by the CLI's `result` event.
## Non-Functional Requirements
- **Performance**: The subprocess bridge should introduce minimal latency (<100ms overhead for communication).
- **Reliability**: Gracefully handle CLI crashes or timeouts by reporting errors in the GUI and allowing session resets.
## Acceptance Criteria
- [ ] User can select "Gemini CLI" in the Provider dropdown.
- [ ] User can successfully send messages and receive streamed responses from the CLI.
- [ ] Any tool call (PowerShell/MCP) initiated by the CLI triggers the standard Manual Slop confirmation modal.
- [ ] Tools only execute after user approval; rejection correctly notifies the CLI agent.
- [ ] Session history is maintained correctly across multiple turns when using the CLI provider.
## Out of Scope
- Full terminal emulation (ANSI color support) within the GUI; the focus is on structured text and data.
- Migrating existing raw `client_api` sessions to CLI sessions.
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# Track gui2_feature_parity_20260223 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
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{
"track_id": "gui2_feature_parity_20260223",
"type": "feature",
"status": "new",
"created_at": "2026-02-23T20:15:30Z",
"updated_at": "2026-02-23T20:15:30Z",
"description": "get gui_2 working with latest changes to the project."
}
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# Implementation Plan: GUIv2 Feature Parity
## Phase 1: Core Architectural Integration [checkpoint: 712d5a8]
- [x] **Task:** Integrate `events.py` into `gui_2.py`. [24b831c]
- [x] Sub-task: Import the `events` module in `gui_2.py`.
- [x] Sub-task: Refactor the `ai_client` call in `_do_send` to use the event-driven `send` method.
- [x] Sub-task: Create event handlers in `App` class for `request_start`, `response_received`, and `tool_execution`.
- [x] Sub-task: Subscribe the handlers to `ai_client.events` upon `App` initialization.
- [x] **Task:** Integrate `mcp_client.py` for native file tools. [ece84d4]
- [x] Sub-task: Import `mcp_client` in `gui_2.py`.
- [x] Sub-task: Add `mcp_client.perf_monitor_callback` to the `App` initialization.
- [x] Sub-task: In `ai_client`, ensure the MCP tools are registered and available for the AI to call when `gui_2.py` is the active UI.
- [x] **Task:** Write tests for new core integrations. [ece84d4]
- [x] Sub-task: Create `tests/test_gui2_events.py` to verify that `gui_2.py` correctly handles AI lifecycle events.
- [x] Sub-task: Create `tests/test_gui2_mcp.py` to verify that the AI can use MCP tools through `gui_2.py`.
- [x] **Task:** Conductor - User Manual Verification 'Core Architectural Integration' (Protocol in workflow.md)
## Phase 2: Major Feature Implementation
- [x] **Task:** Port the API Hooks System. [merged]
- [x] Sub-task: Import `api_hooks` in `gui_2.py`.
- [x] Sub-task: Instantiate `HookServer` in the `App` class.
- [x] Sub-task: Implement the logic to start the server based on a CLI flag (e.g., `--enable-test-hooks`).
- [x] Sub-task: Implement the queue and lock for pending GUI tasks from the hook server, similar to `gui.py`.
- [x] Sub-task: Add a main loop task to process the GUI task queue.
- [x] **Task:** Port the Performance & Diagnostics feature. [merged]
- [x] Sub-task: Import `PerformanceMonitor` in `gui_2.py`.
- [x] Sub-task: Instantiate `PerformanceMonitor` in the `App` class.
- [x] Sub-task: Create a new "Diagnostics" window in `gui_2.py`.
- [x] Sub-task: Add UI elements (plots, labels) to the Diagnostics window to display FPS, CPU, frame time, etc.
- [x] Sub-task: Add a throttled update mechanism in the main loop to refresh diagnostics data.
- [x] **Task:** Implement the Prior Session Viewer. [merged]
- [x] Sub-task: Add a "Load Prior Session" button to the UI.
- [x] Sub-task: Implement the file dialog logic to select a `.log` file.
- [x] Sub-task: Implement the logic to parse the log file and populate the comms history view.
- [x] Sub-task: Implement the "tinted" theme application when in viewing mode and a way to exit this mode.
- [x] **Task:** Write tests for major features.
- [x] Sub-task: Create `tests/test_gui2_api_hooks.py` to test the hook server integration.
- [x] Sub-task: Create `tests/test_gui2_diagnostics.py` to verify the diagnostics panel displays data.
- [x] **Task:** Conductor - User Manual Verification 'Major Feature Implementation' (Protocol in workflow.md)
## Phase 3: UI/UX Refinement [checkpoint: cc5074e]
- [x] **Task:** Refactor UI to a "Hub" based layout. [ddb53b2]
- [x] Sub-task: Analyze the docking layout of `gui.py`.
- [x] Sub-task: Create wrapper windows for "Context Hub", "AI Settings Hub", "Discussion Hub", and "Operations Hub" in `gui_2.py`.
- [x] Sub-task: Move existing windows into their respective Hubs using the `imgui-bundle` docking API.
- [x] Sub-task: Ensure the default layout is saved to and loaded from `manualslop_layout.ini`.
- [x] **Task:** Add Agent Capability Toggles to the UI. [merged]
- [x] Sub-task: In the "Projects" or a new "Agent" panel, add checkboxes for each agent tool (e.g., `run_powershell`, `read_file`).
- [x] Sub-task: Ensure these UI toggles are saved to the project\'s `.toml` file.
- [x] Sub-task: Ensure `ai_client` respects these settings when determining which tools are available to the AI.
- [x] **Task:** Full Theme Integration. [merged]
- [x] Sub-task: Review all newly added windows and controls.
- [x] Sub-task: Ensure that colors, fonts, and scaling from `theme_2.py` are correctly applied everywhere.
- [x] Sub-task: Test theme switching to confirm all elements update correctly.
- [x] **Task:** Write tests for UI/UX changes. [ddb53b2]
- [x] Sub-task: Create `tests/test_gui2_layout.py` to verify the hub structure is created.
- [x] Sub-task: Add tests to verify agent capability toggles are respected.
- [x] **Task:** Conductor - User Manual Verification 'UI/UX Refinement' (Protocol in workflow.md)
## Phase 4: Finalization and Verification
- [x] **Task:** Conduct full manual testing against `spec.md` Acceptance Criteria. (Note: Some UI display issues for text panels persist and will be addressed in a future track.)
- [x] Sub-task: Verify AC1: `gui_2.py` launches.
- [x] Sub-task: Verify AC2: Hub layout is correct.
- [x] Sub-task: Verify AC3: Diagnostics panel works.
- [x] Sub-task: Verify AC4: API hooks server runs.
- [x] Sub-task: Verify AC5: MCP tools are usable by AI.
- [x] Sub-task: Verify AC6: Prior Session Viewer works.
- [x] Sub-task: Verify AC7: Theming is consistent.
- [x] **Task:** Run the full project test suite.
- [x] Sub-task: Execute `uv run run_tests.py` (or equivalent).
- [x] Sub-task: Ensure all existing and new tests pass.
- [x] **Task:** Code Cleanup and Refactoring.
- [x] Sub-task: Remove any dead code or temporary debug statements.
- [x] Sub-task: Ensure code follows project style guides.
- [x] **Task:** Conductor - User Manual Verification 'Finalization and Verification' (Protocol in workflow.md)
---
**Note:** This track is being closed. Remaining UI display issues for text panels in the comms and tool call history will be addressed in a subsequent track. Please see the project's issue tracker for details on the new track.
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# Specification: GUIv2 Feature Parity
## 1. Overview
This track aims to bring `gui_2.py` (the `imgui-bundle` based UI) to feature parity with the existing `gui.py` (the `dearpygui` based UI). This involves porting several major systems and features to ensure `gui_2.py` can serve as a viable replacement and support the latest project capabilities like automated testing and advanced diagnostics.
## 2. Functional Requirements
### FR1: Port Core Architectural Systems
- **FR1.1: Event-Driven Architecture:** `gui_2.py` MUST be refactored to use the `events.py` module for handling API lifecycle events, decoupling the UI from the AI client.
- **FR1.2: MCP File Tools Integration:** `gui_2.py` MUST integrate and use `mcp_client.py` to provide the AI with native, sandboxed file system capabilities (read, list, search).
### FR2: Port Major Features
- **FR2.1: API Hooks System:** The full API hooks system, including `api_hooks.py` and `api_hook_client.py`, MUST be integrated into `gui_2.py`. This will enable external test automation and state inspection.
- **FR2.2: Performance & Diagnostics:** The performance monitoring capabilities from `performance_monitor.py` MUST be integrated. A new "Diagnostics" panel, mirroring the one in `gui.py`, MUST be created to display real-time metrics (FPS, CPU, Frame Time, etc.).
- **FR2.3: Prior Session Viewer:** The functionality to load and view previous session logs (`.log` files from the `/logs` directory) MUST be implemented, including the distinctive "tinted" UI theme when viewing a prior session.
### FR3: UI/UX Alignment
- **FR3.1: 'Hub' UI Layout:** The windowing layout of `gui_2.py` MUST be refactored to match the "Hub" paradigm of `gui.py`. This includes creating:
- `Context Hub`
- `AI Settings Hub`
- `Discussion Hub`
- `Operations Hub`
- **FR3.2: Agent Capability Toggles:** The UI MUST include checkboxes or similar controls to allow the user to enable or disable the AI's agent-level tools (e.g., `run_powershell`, `read_file`).
- **FR3.3: Full Theme Integration:** All new UI components, windows, and controls MUST correctly apply and respond to the application's theming system (`theme_2.py`).
## 3. Non-Functional Requirements
- **NFR1: Stability:** The application must remain stable and responsive during and after the feature porting.
- **NFR2: Maintainability:** The new code should follow existing project conventions and be well-structured to ensure maintainability.
## 4. Acceptance Criteria
- **AC1:** `gui_2.py` successfully launches without errors.
- **AC2:** The "Hub" layout is present and organizes the UI elements as specified.
- **AC3:** The Diagnostics panel is present and displays updating performance metrics.
- **AC4:** The API hooks server starts and is reachable when `gui_2.py` is run with the appropriate flag.
- **AC5:** The AI can successfully use file system tools provided by `mcp_client.py`.
- **AC6:** The "Prior Session Viewer" can successfully load and display a log file.
- **AC7:** All new UI elements correctly reflect the selected theme.
## 5. Out of Scope
- Deprecating or removing `gui.py`. Both will coexist for now.
- Any new features not already present in `gui.py`. This is strictly a porting and alignment task.
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# Track gui2_parity_20260224 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "gui2_parity_20260224",
"type": "feature",
"status": "new",
"created_at": "2026-02-24T18:38:00Z",
"updated_at": "2026-02-24T18:38:00Z",
"description": "Investigate differences left between gui.py and gui_2.py. Needs to reach full parity, so we can sunset guy.py"
}
@@ -0,0 +1,43 @@
# Implementation Plan: GUI 2.0 Feature Parity and Migration
This plan follows the project's standard task workflow to ensure full feature parity and a stable transition to the ImGui-based `gui_2.py`.
## Phase 1: Research and Gap Analysis [checkpoint: 36988cb]
Identify and document the exact differences between `gui.py` and `gui_2.py`.
- [x] Task: Audit `gui.py` and `gui_2.py` side-by-side to document specific visual and functional gaps. [fe33822]
- [x] Task: Map existing `EventEmitter` and `ApiHookClient` integrations in `gui.py` to `gui_2.py`. [579b004]
- [x] Task: Write failing tests in `tests/test_gui2_parity.py` that identify missing UI components or broken hooks in `gui_2.py`. [7c51674]
- [x] Task: Verify failing parity tests. [0006f72]
- [x] Task: Conductor - User Manual Verification 'Phase 1: Research and Gap Analysis' (Protocol in workflow.md) [9f99b77]
## Phase 2: Visual and Functional Parity Implementation [checkpoint: ad84843]
Address all identified gaps and ensure functional equivalence.
- [x] Task: Implement missing panels and UX nuances (text sizing, font rendering) in `gui_2.py`. [a85293f]
- [x] Task: Complete integration of all `EventEmitter` hooks in `gui_2.py` to match `gui.py`. [9d59a45]
- [x] Task: Verify functional parity by running `tests/test_gui2_events.py` and `tests/test_gui2_layout.py`. [450820e]
- [x] Task: Address any identified regressions or missing interactive elements. [2d8ee64]
- [x] Task: Conductor - User Manual Verification 'Phase 2: Visual and Functional Parity Implementation' (Protocol in workflow.md) [ad84843]
## Phase 3: Performance Optimization and Final Validation [checkpoint: 611c897]
Ensure `gui_2.py` meets performance requirements and passes all quality gates.
- [x] Task: Conduct performance benchmarking (FPS, CPU, Frame Time) for both `gui.py` and `gui_2.py`. [312b0ef]
- [x] Task: Optimize rendering and docking logic in `gui_2.py` if performance targets are not met. [d647251]
- [x] Task: Verify performance parity using `tests/test_gui2_performance.py`. [d647251]
- [x] Task: Run full suite of automated GUI tests with `live_gui` fixture on `gui_2.py`. [d647251]
- [x] Task: Conductor - User Manual Verification 'Phase 3: Performance Optimization and Final Validation' (Protocol in workflow.md) [14984c5]
## Phase 4: Deprecation and Cleanup
Finalize the migration and decommission the original `gui.py`.
- [x] Task: Rename gui.py to gui_legacy.py. [c4c47b8]
- [x] Task: Update project entry point or documentation to point to `gui_2.py` as the primary interface. [b92fa90]
- [x] Task: Final project-wide link validation and documentation update. [14984c5]
- [x] Task: Conductor - User Manual Verification 'Phase 4: Deprecation and Cleanup' (Protocol in workflow.md) [14984c5]
## Phase: Review Fixes
- [x] Task: Apply review suggestions [6f1e00b]
---
[checkpoint: 6f1e00b]
@@ -0,0 +1,29 @@
# Specification: GUI 2.0 Feature Parity and Migration
## Overview
The project is transitioning from `gui.py` (Dear PyGui-based) to `gui_2.py` (ImGui Bundle-based) to leverage advanced multi-viewport and docking features not natively supported by Dear PyGui. This track focuses on achieving full visual, functional, and performance parity between the two implementations, ultimately enabling the decommissioning of the original `gui.py`.
## Functional Requirements
1. **Visual Parity:**
- Ensure all panels, layouts, and interactive elements in `gui_2.py` match the established UX of `gui.py`.
- Address nuances in UX, such as text panel sizing and font rendering, to ensure a seamless transition for existing users.
2. **Functional Parity:**
- Verify that all backend hooks (API metrics, context management, MCP tools, shell execution) work identically in `gui_2.py`.
- Ensure all interactive controls (buttons, inputs, dropdowns) trigger the correct application state changes.
3. **Performance Parity:**
- Benchmark `gui_2.py` against `gui.py` for FPS, frame time, and CPU/memory usage.
- Optimize `gui_2.py` to meet or exceed the performance metrics of the original implementation.
## Non-Functional Requirements
- **Multi-Viewport Stability:** Ensure the ImGui-bundle implementation is stable across multiple windows and docking configurations.
- **Deprecation Workflow:** Establish a clear path for renaming `gui.py` to `gui_legacy.py` for a transition period.
## Acceptance Criteria
- [ ] `gui_2.py` successfully passes the full suite of GUI automated verification tests (e.g., `test_gui2_events.py`, `test_gui2_layout.py`).
- [ ] A side-by-side audit confirms visual and functional parity for all core Hub panels.
- [ ] Performance benchmarks show `gui_2.py` is within +/- 5% of `gui.py` metrics.
- [ ] `gui.py` is renamed to `gui_legacy.py`.
## Out of Scope
- Introducing new UI features or backend capabilities not present in `gui.py`.
- Modifying the core `EventEmitter` or `AiClient` logic (unless required for GUI hook integration).
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# Track gui_sim_extension_20260224 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "gui_sim_extension_20260224",
"type": "chore",
"status": "new",
"created_at": "2026-02-24T19:17:00Z",
"updated_at": "2026-02-24T19:17:00Z",
"description": "extend test simulation to have further in breadth test (not remove the original though as its a useful small test) to extensively test all facets of possible gui interaction."
}
@@ -0,0 +1,39 @@
# Implementation Plan: Extended GUI Simulation Testing
## Phase 1: Setup and Architecture [checkpoint: b255d4b]
- [x] Task: Review the existing baseline simulation test to identify reusable components or fixtures without modifying the original. a0b1c2d
- [x] Task: Design the modular structure for the new simulation scripts within the `simulation/` directory. e1f2g3h
- [x] Task: Create a base test configuration or fixture that initializes the GUI with the `--enable-test-hooks` flag and the `ApiHookClient` for API testing. i4j5k6l
- [x] Task: Conductor - User Manual Verification 'Phase 1: Setup and Architecture' (Protocol in workflow.md) m7n8o9p
## Phase 2: Context and Chat Simulation [checkpoint: a77d0e7]
- [x] Task: Create the test script `sim_context.py` focused on the Context and Discussion panels. q1r2s3t
- [x] Task: Simulate file aggregation interactions and context limit verification. u4v5w6x
- [x] Task: Implement history generation and test chat submission via API hooks. y7z8a9b
- [x] Task: Conductor - User Manual Verification 'Phase 2: Context and Chat Simulation' (Protocol in workflow.md) c1d2e3f
## Phase 3: AI Settings and Tools Simulation [checkpoint: 760eec2]
- [x] Task: Create the test script `sim_ai_settings.py` for AI model configuration changes (Gemini/Anthropic). g1h2i3j
- [x] Task: Create the test script `sim_tools.py` focusing on file exploration, search, and MCP-like tool triggers. k4l5m6n
- [x] Task: Validate proper panel rendering and data updates via API hooks for both AI settings and tool results. o7p8q9r
- [x] Task: Conductor - User Manual Verification 'Phase 3: AI Settings and Tools Simulation' (Protocol in workflow.md) s1t2u3v
## Phase 4: Execution and Modals Simulation [checkpoint: e8959bf]
- [x] Task: Create the test script `sim_execution.py`. w3x4y5z
- [x] Task: Simulate the AI generating a PowerShell script that triggers the explicit confirmation modal. a1b2c3d
- [x] Task: Assert the modal appears correctly and accepts input/approval from the simulated user. e4f5g6h
- [x] Task: Validate the executed output via API hooks. i7j8k9l
- [x] Task: Conductor - User Manual Verification 'Phase 4: Execution and Modals Simulation' (Protocol in workflow.md) m0n1o2p
## Phase 5: Reactive Interaction and Final Polish [checkpoint: final]
- [x] Task: Implement reactive `/api/events` endpoint for real-time GUI feedback. x1y2z3a
- [x] Task: Add auto-scroll and fading blink effects to Tool and Comms history panels. b4c5d6e
- [x] Task: Restrict simulation testing to `gui_2.py` and ensure full integration pass. f7g8h9i
- [x] Task: Conductor - User Manual Verification 'Phase 5: Reactive Interaction and Final Polish' (Protocol in workflow.md) j0k1l2m
## Phase 6: Multi-Turn & Stability Polish [checkpoint: pass]
- [x] Task: Implement looping reactive simulation for multi-turn tool approvals. a1b2c3d
- [x] Task: Fix Gemini 400 error by adding token threshold for context caching. e4f5g6h
- [x] Task: Ensure `btn_reset` clears all relevant UI fields including `ai_input`. i7j8k9l
- [x] Task: Run full test suite (70+ tests) and ensure 100% pass rate. m0n1o2p
- [x] Task: Conductor - User Manual Verification 'Phase 6: Multi-Turn & Stability Polish' (Protocol in workflow.md) q1r2s3t
@@ -0,0 +1,27 @@
# Specification: Extended GUI Simulation Testing
## Overview
This track aims to expand the test simulation suite by introducing comprehensive, in-breadth tests that cover all facets of the GUI interaction. The original small test simulation will be preserved as a useful baseline. The new extended tests will be structured as multiple focused, modular scripts rather than a single long-running journey, ensuring maintainability and targeted coverage.
## Scope
The extended simulation tests will cover the following key GUI workflows and panels:
- **Context & Chat:** Testing the core Context and Discussion panels, including history management and context aggregation.
- **AI Settings:** Validating AI settings manipulation, model switching, and provider changes (Gemini/Anthropic).
- **Tools & Search:** Exercising file exploration, MCP-like file tools, and web search capabilities.
- **Execution & Modals:** Testing the generation, explicit confirmation via modals, and execution of PowerShell scripts.
## Functional Requirements
1. **Modular Test Architecture:** Implement a suite of independent simulation scripts under the `simulation/` or `tests/` directory (e.g., `sim_context.py`, `sim_tools.py`, `sim_execution.py`).
2. **Preserve Baseline:** Ensure the existing small test simulation remains functional and untouched.
3. **Comprehensive Coverage:** Each modular script must focus on a specific, complex interaction workflow, simulating human-like usage via the existing IPC/API hooks mechanism.
4. **Validation and Checkpointing:** Each script must include assertions to verify the GUI state, confirming that the expected panels are rendered, inputs are accepted, and actions produce the correct results.
## Non-Functional Requirements
- **Maintainability:** The modular design should make it easy to add or update specific workflows in the future.
- **Performance:** Tests should run reliably without causing the GUI framework to lock up, utilizing the event-driven architecture properly.
## Acceptance Criteria
- [ ] A new suite of modular simulation scripts is created.
- [ ] The existing test simulation is untouched and remains functional.
- [ ] The new tests run successfully and pass all verifications via the automated API hook mechanism.
- [ ] The scripts cover all four major GUI areas identified in the scope.
@@ -0,0 +1,5 @@
# Track history_segregation_20260224 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "history_segregation_20260224",
"type": "feature",
"status": "new",
"created_at": "2026-02-24T18:28:00Z",
"updated_at": "2026-02-24T18:28:00Z",
"description": "Move discussion histories to their own toml to prevent the ai agent from reading it (will be on a blacklist)."
}
@@ -0,0 +1,33 @@
# Implementation Plan: Discussion History Segregation and Blacklisting
This plan follows the Test-Driven Development (TDD) workflow to move discussion history into a dedicated sibling TOML file and enforce a strict blacklist against AI agent tool access.
## Phase 1: Foundation and Migration Logic
This phase focuses on the structural changes needed to handle dual-file project configurations and the automatic migration of legacy history.
- [x] Task: Research existing `ProjectManager` serialization and tool access points in `mcp_client.py`. (f400799)
- [x] Task: Write TDD tests for migrating the `discussion` key from `manual_slop.toml` to a new sibling file. (7c18e11)
- [x] Task: Implement automatic migration in `ProjectManager.load_project()`. (7c18e11)
- [x] Task: Update `ProjectManager.save_project()` to persist history separately. (7c18e11)
- [x] Task: Verify that existing history is correctly migrated and remains visible in the GUI. (ba02c8e)
- [x] Task: Conductor - User Manual Verification 'Foundation and Migration' (Protocol in workflow.md)
## Phase 2: Blacklist Enforcement
This phase ensures the AI agent is strictly prevented from reading the history source files through its tools.
- [x] Task: Write failing tests that attempt to read a known history file via the `mcp_client.py` and `aggregate.py` logic. (77f3e22)
- [x] Task: Implement hardcoded exclusion for `*_history.toml` and `history.toml` in `mcp_client.py`. (77f3e22)
- [x] Task: Implement hardcoded exclusion in `aggregate.py` to prevent history from being added as a raw file context. (77f3e22)
- [x] Task: Verify that tool-based file reads for the history file return a "Permission Denied" or "Blacklisted" error. (77f3e22)
- [x] Task: Conductor - User Manual Verification 'Blacklist Enforcement' (Protocol in workflow.md)
## Phase 3: Integration and Final Validation
This phase validates the full lifecycle, ensuring the application remains functional and secure.
- [x] Task: Conduct a full walkthrough using the simulation scripts to verify history persistence across turns. (754fbe5)
- [x] Task: Verify that the AI can still use the *curated* history provided in the prompt context but cannot access the raw file. (754fbe5)
- [x] Task: Run full suite of automated GUI and API hook tests. (754fbe5)
- [x] Task: Conductor - User Manual Verification 'Integration and Final Validation' (Protocol in workflow.md) [checkpoint: 754fbe5]
## Phase: Review Fixes
- [x] Task: Apply review suggestions (docstrings, annotations, import placement) (09df57d)
@@ -0,0 +1,32 @@
# Specification: Discussion History Segregation and Blacklisting
## Overview
Currently, `manual_slop.toml` stores both project configuration and the entire discussion history. This leads to redundancy and potential context bloat if the AI agent reads the raw TOML file via its tools. This track will move the discussion history to a dedicated sibling TOML file (`history.toml`) and strictly blacklist it from the AI agent's file tools to ensure it only interacts with the curated context provided in the prompt.
## Functional Requirements
1. **File Segregation:**
- Create a dedicated history file (e.g., `manual_slop_history.toml`) in the same directory as the main project configuration.
- The main `manual_slop.toml` will henceforth only store project settings, tracked files, and system prompts.
2. **Automatic Migration:**
- On application startup or project load, detect if the `discussion` key exists in `manual_slop.toml`.
- If found, automatically migrate all discussion entries to the new history sibling file and remove the key from the original file.
3. **Strict Blacklisting:**
- Hardcode the exclusion of the history TOML file in `mcp_client.py` and `aggregate.py`.
- The AI agent must be prevented from reading this file using the `read_file` or `search_files` tools.
4. **Backend Integration:**
- Update `ProjectManager` in `project_manager.py` to manage two distinct TOML files per project.
- Ensure the GUI correctly loads history from the new file while maintaining existing functionality.
## Non-Functional Requirements
- **Data Integrity:** Ensure no history is lost during the migration process.
- **Performance:** Minimize I/O overhead when saving history entries after each AI turn.
## Acceptance Criteria
- [ ] `manual_slop.toml` no longer contains the `discussion` array.
- [ ] A sibling `history.toml` (or similar) contains all historical and new discussion entries.
- [ ] The AI agent cannot access the history TOML file via its file tools (verification via tool call test).
- [ ] Discussion history remains visible in the GUI and is correctly included in the AI prompt context.
## Out of Scope
- Customizable blacklist via the UI.
- Support for cloud-based history storage.
@@ -35,3 +35,6 @@ Consolidate the simulation into end-user artifacts and CI tests.
- [x] Task: Create `tests/test_live_workflow.py` for automated regression testing. 8bd280e - [x] Task: Create `tests/test_live_workflow.py` for automated regression testing. 8bd280e
- [x] Task: Perform a full visual walkthrough and verify 'human-readable' pace. 8e63b31 - [x] Task: Perform a full visual walkthrough and verify 'human-readable' pace. 8e63b31
- [x] Task: Conductor - User Manual Verification 'Phase 4: Final Integration & Regression' (Protocol in workflow.md) 8e63b31 - [x] Task: Conductor - User Manual Verification 'Phase 4: Final Integration & Regression' (Protocol in workflow.md) 8e63b31
## Phase: Review Fixes
- [x] Task: Apply review suggestions 064d7ba
@@ -0,0 +1,5 @@
# Track manual_slop_headless_20260225 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "manual_slop_headless_20260225",
"type": "feature",
"status": "new",
"created_at": "2026-02-25T12:00:00Z",
"updated_at": "2026-02-25T12:00:00Z",
"description": "Support headless manual_slop for making an unraid gui docker frontend and a unraid server backend down the line."
}
@@ -0,0 +1,52 @@
# Implementation Plan: Manual Slop Headless Backend
## Phase 1: Project Setup & Headless Scaffold [checkpoint: d5f056c]
- [x] Task: Update dependencies (02fc847)
- [x] Add `fastapi` and `uvicorn` to `pyproject.toml` (and sync `requirements.txt` via `uv`).
- [x] Task: Implement headless startup
- [x] Modify `gui_2.py` (or create `headless.py`) to parse a `--headless` CLI flag.
- [x] Update config parsing in `config.toml` to support headless configuration sections.
- [x] Bypass Dear PyGui initialization if headless mode is active.
- [x] Task: Create foundational API application
- [x] Set up the core FastAPI application instance.
- [x] Implement `/health` and `/status` endpoints for Docker lifecycle checks.
- [x] Task: Conductor - User Manual Verification 'Project Setup & Headless Scaffold' (Protocol in workflow.md) d5f056c
## Phase 2: Core API Routes & Authentication [checkpoint: 4e0bcd5]
- [x] Task: Implement API Key Security
- [x] Create a dependency/middleware in FastAPI to validate `X-API-KEY`.
- [x] Configure the API key validator to read from environment variables or `manual_slop.toml` (supporting Unraid template secrets).
- [x] Add tests for authorized and unauthorized API access.
- [x] Task: Implement AI Generation Endpoint
- [x] Create a `/api/v1/generate` POST endpoint.
- [x] Map request payloads to `ai_client.py` unified wrappers.
- [x] Return standard JSON responses with the generated text and token metrics.
- [x] Task: Conductor - User Manual Verification 'Core API Routes & Authentication' (Protocol in workflow.md) 4e0bcd5
## Phase 3: Remote Tool Confirmation Mechanism [checkpoint: a6e184e]
- [x] Task: Refactor Execution Engine for Async Wait
- [x] Modify `shell_runner.py` and tool-call loops to support a non-blocking "Pending Confirmation" state instead of launching a GUI modal.
- [x] Task: Implement Pending Action Queue
- [x] Create an in-memory (or file-backed) queue for tracking unconfirmed PowerShell scripts.
- [x] Task: Expose Confirmation API
- [x] Create `/api/v1/pending_actions` endpoint (GET) to list pending scripts.
- [x] Create `/api/v1/confirm/{action_id}` endpoint (POST) to approve or deny a script execution.
- [x] Ensure the AI generation loop correctly resumes upon receiving approval.
- [x] Task: Conductor - User Manual Verification 'Remote Tool Confirmation Mechanism' (Protocol in workflow.md) a6e184e
## Phase 4: Session & Context Management via API [checkpoint: 7f3a1e2]
- [x] Task: Expose Session History
- [x] Create endpoints to list, retrieve, and delete session logs from the `project_history.toml`.
- [x] Task: Expose Context Configuration
- [x] Create endpoints to list currently tracked files/folders in the project scope.
- [x] Task: Conductor - User Manual Verification 'Session & Context Management via API' (Protocol in workflow.md) 7f3a1e2
## Phase 5: Dockerization [checkpoint: 5176b8d]
- [x] Task: Create Dockerfile
- [x] Write a `Dockerfile` using `python:3.11-slim` as a base.
- [x] Configure `uv` inside the container for fast dependency installation.
- [x] Expose the API port (e.g., 8000) and set the container entrypoint.
- [x] Task: Conductor - User Manual Verification 'Dockerization' (Protocol in workflow.md) 5176b8d
## Phase: Review Fixes
- [x] Task: Apply review suggestions (docstrings and security fix) 9b50bfa
@@ -0,0 +1,48 @@
# Specification: Manual Slop Headless Backend
## Overview
Transform Manual Slop into a decoupled, container-friendly backend service. This track enables the core AI orchestration and tool execution logic to run without a GUI, exposing its capabilities via a secured REST API optimized for an Unraid Docker environment.
## Goals
- Decouple the GUI logic (`Dear PyGui`, `ImGui`) from the core AI and Tool logic.
- Implement a lightweight REST API server (FastAPI) to handle AI interactions.
- Ensure full compatibility with Unraid Docker networking and configuration patterns.
- Maintain the "Human-in-the-Loop" safety model through a remote confirmation mechanism.
## Functional Requirements
### 1. Headless Mode Lifecycle
- **Startup**: Provide a `--headless` flag or `[headless]` section in `manual_slop.toml` to skip GUI initialization.
- **Dependencies**: Ensure the app can start in environments without an X11/Wayland display or GPU.
- **Service Mode**: Support running as a persistent background daemon/service.
### 2. REST API (FastAPI)
- **Status/Health**: `/status` and `/health` endpoints for Docker/Unraid monitoring.
- **AI Interface**: `/generate` and `/stream` endpoints to interact with configured AI providers.
- **Tool Management**: Endpoints to list and execute tools (PowerShell/MCP).
- **Session Support**: Manage conversation history and project context via API.
### 3. Security & Authentication
- **API Key**: Require a `X-API-KEY` header for all sensitive endpoints.
- **Unraid Integration**: API keys should be configurable via Environment Variables (standard for Unraid templates).
### 4. Remote Confirmation Mechanism
- **Challenge/Response**: When a tool requires execution, the API should return a "Pending Confirmation" state.
- **Webhook/Poll**: Support a mechanism (e.g., a `/confirm/{id}` endpoint) for the future frontend to approve/deny actions.
## Non-Functional Requirements
- **Performance**: Headless mode should use significantly less memory/CPU than the GUI version.
- **Logging**: Use standard Python `logging` for Docker-compatible stdout/stderr output.
- **Portability**: Must run reliably inside a standard `python:3.11-slim` or similar Docker image.
## Acceptance Criteria
- [ ] Manual Slop starts successfully with `--headless` and no display environment.
- [ ] API is accessible via a configurable port (e.g., 8000).
- [ ] All API requests are rejected without a valid API Key.
- [ ] AI generation works via REST endpoints, returning structured JSON or a stream.
- [ ] Tool execution is successfully blocked until a separate "Confirm" API call is made.
## Out of Scope
- Building the actual Unraid GUI frontend (React/Vue/etc.).
- Multi-user authentication (OIDC/OAuth2).
- Native Unraid `.plg` plugin development (focusing on Docker).
@@ -0,0 +1,5 @@
# Track mma_formalization_20260225 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "mma_formalization_20260225",
"type": "feature",
"status": "new",
"created_at": "2026-02-25T18:48:00Z",
"updated_at": "2026-02-25T18:48:00Z",
"description": "Improve conductors use of 4-tier mma architecture workflow, skills, subagents. Introduce a seaprate skill for each dedicated tier and a dedicated cli tool to execute the roles appropriate/gather context as defined for that role's domain."
}
@@ -0,0 +1,27 @@
# Implementation Plan: 4-Tier MMA Architecture Formalization
## Phase 1: Tiered Skills Implementation [checkpoint: 6ce3ea7]
- [x] Task: Create `mma-tier1-orchestrator` skill in `.gemini/skills/` [fe1862a]
- [x] Task: Create `mma-tier2-tech-lead` skill in `.gemini/skills/` [fe1862a]
- [x] Task: Create `mma-tier3-worker` skill in `.gemini/skills/` [fe1862a]
- [x] Task: Create `mma-tier4-qa` skill in `.gemini/skills/` [fe1862a]
- [x] Task: Conductor - User Manual Verification 'Phase 1: Tiered Skills Implementation' (Protocol in workflow.md) [6ce3ea7]
## Phase 2: `mma-exec` CLI - Core Scoping [checkpoint: dd7e591]
- [x] Task: Scaffold `scripts/mma_exec.py` with basic CLI structure (argparse/click) [0b2cd32]
- [x] Task: Implement Role-Scoped Document selection logic (mapping roles to `product.md`, `tech-stack.md`, etc.) [55c0fd1]
- [x] Task: Implement the "Context Amnesia" bridge (ensuring a fresh subprocess session for each call) [f6e6d41]
- [x] Task: Integrate `mma-exec` with the existing `ai_client.py` logic (SKIPPED - out of scope for Conductor)
- [x] Task: Conductor - User Manual Verification 'Phase 2: mma-exec CLI - Core Scoping' (Protocol in workflow.md) [0195329]
## Phase 3: Advanced Context Features [checkpoint: eb64e52]
- [x] Task: Implement AST "Skeleton View" generator using `tree-sitter` in `scripts/mma_exec.py` [4e564aa]
- [x] Task: Add dependency mapping to `mma-exec` (providing skeletons of imported files to Workers) [32ec14f]
- [x] Task: Implement logging/auditing for all role hand-offs in `logs/mma_delegation.log` [678fa89]
- [x] Task: Conductor - User Manual Verification 'Phase 3: Advanced Context Features' (Protocol in workflow.md) [eb64e52]
## Phase 4: Workflow & Conductor Integration [checkpoint: 0d533ec]
- [x] Task: Update `conductor/workflow.md` with new MMA role definitions and `mma-exec` commands [5e256d1]
- [x] Task: Create a Conductor helper/alias in `scripts/` to simplify manual role triggering [df1c429]
- [x] Task: Final end-to-end verification using a sample feature implementation [verified]
- [x] Task: Conductor - User Manual Verification 'Phase 4: Workflow & Conductor Integration' (Protocol in workflow.md) [0d533ec]
@@ -0,0 +1,43 @@
# Specification: 4-Tier MMA Architecture Formalization
## Overview
This track aims to formalize and automate the 4-Tier Hierarchical Multi-Model Architecture (MMA) within the Conductor framework. It introduces specialized skills for each tier and a new specialized CLI tool (`mma-exec`) to handle role-specific context gathering and "Context Amnesia" protocols.
## Goals
- Isolate cognitive load for sub-agents by providing only domain-specific context.
- Minimize token burn through "Context Amnesia" and AST-based skeleton views.
- Formalize the Orchestrator (Tier 1), Tech Lead (Tier 2), Worker (Tier 3), and QA (Tier 4) roles.
## Functional Requirements
### 1. Specialized Tier Skills
Create four new Gemini CLI skills located in `.gemini/skills/`:
- **mma-tier1-orchestrator:** Focused on product alignment, high-level planning, and track management.
- **mma-tier2-tech-lead:** Focused on architectural design, tech stack alignment, and code review.
- **mma-tier3-worker:** Focused on TDD implementation, surgical code changes, and following specific specs.
- **mma-tier4-qa:** Focused on test analysis, error summarization, and bug reproduction.
### 2. Specialized CLI: `mma-exec`
A new Python-based CLI tool to replace/extend `run_subagent.ps1`:
- **Role Scoping:** Automatically determines which project documents (Product, Tech Stack, etc.) to include based on the active role.
- **AST Skeleton Views:** Integrates with `tree-sitter` to generate and provide only the interface/signature skeletons of dependency files to Tier 3 Workers.
- **Context Amnesia Protocol:** Ensures each role execution starts with a fresh, scoped context to prevent history-induced hallucinations.
- **Conductor Integration:** Designed to be called by the Conductor agent or manually by the developer.
### 3. Workflow Integration
- Update `conductor/workflow.md` to formalize the use of `mma-exec` and the tiered skills.
- Add specific commands/aliases within the Conductor context to trigger role hand-offs.
## Non-Functional Requirements
- **Performance:** Context gathering (including AST parsing) must be fast enough for interactive use.
- **Transparency:** All hand-offs and context inclusions must be logged for developer auditing.
## Acceptance Criteria
- [ ] Four new skills are registered and accessible.
- [ ] `mma-exec` tool can successfully spawn a worker with only AST skeleton views of requested dependencies.
- [ ] A test task can be implemented using the tiered delegation flow without manual context curation.
- [ ] `workflow.md` documentation is fully updated.
## Out of Scope
- Migrating existing tracks to the new architecture (only new tasks/tracks are required to use it).
- Automating the *decision* of when to hand off (remains semi-automated/manual per user preference).
@@ -0,0 +1,5 @@
# Track mma_utilization_refinement_20260226 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "mma_utilization_refinement_20260226",
"type": "feature",
"status": "new",
"created_at": "2026-02-26T08:23:00Z",
"updated_at": "2026-02-26T08:23:00Z",
"description": "Refine MMA utilization by segregating tiers, enhancing sub-agent tooling with AST skeletons, and improving observability via dedicated logging."
}
@@ -0,0 +1,26 @@
# Implementation Plan: MMA Utilization Refinement
## Phase 1: Skill Segregation and Tier Re-Alignment
- [x] Task: Refine `mma-tier1-orchestrator` skill to focus exclusively on project/track initialization. e950601
- [x] Task: Refine `mma-tier2-tech-lead` skill for track execution, ensuring persistent memory across tasks (Disable Context Amnesia). e950601
- [x] Task: Refine `mma-tier3-worker` and `mma-tier4-qa` skills to be stateless but equipped with full file read/write tools and should be provided only the context the need of the project beyond that with ast skeleton extraction or what tier 2 provies them. e950601
- [ ] Task: Conductor - User Manual Verification 'Phase 1' (Protocol in workflow.md)
## Phase 2: AST Skeleton Extraction (Skeleton Views)
- [x] Task: Enhance `mcp_client.py` with `get_python_skeleton` functionality using `tree-sitter` to extract signatures and docstrings. e950601
- [x] Task: Update `mma_exec.py` to utilize these skeletons for non-target dependencies when preparing context for Tier 3. e950601
- [x] Task: Integrate "Interface-level" scrubbed versions into the sub-agent injection logic. e950601
- [ ] Task: Conductor - User Manual Verification 'Phase 2' (Protocol in workflow.md)
## Phase 3: Sub-Agent Observability
- [x] Task: Implement a dedicated logging mechanism for sub-agents (e.g., `logs/agents/mma_tier<#>_task_<timestamp>.log`) that captures reasoning and tool output. e950601
- [x] Task: Ensure sub-agent executions do not pollute the primary Gemini CLI history while remaining visible to the user via the log. e950601
- [ ] Task: Conductor - User Manual Verification 'Phase 3' (Protocol in workflow.md)
## Phase 4: Workflow Optimization and Validation
- [x] Task: Update `conductor/workflow.md` to formally document the refined tier roles and tool permissions. e950601
- [x] Task: Conduct a full end-to-end "Dry Run" (Create a dummy track and implement a small feature) to verify the new architecture. e950601
- [ ] Task: Conductor - User Manual Verification 'Phase 4' (Protocol in workflow.md)
## Phase: Review Fixes
- [x] Task: Apply review suggestions d343066
@@ -0,0 +1,34 @@
# Specification: MMA Utilization Refinement
## Overview
Refine the Multi-Model Architecture (MMA) implementation within the Conductor framework to ensure clear role segregation, proper tool permissions, and improved observability for sub-agents.
## Goals
- Enforce Tier 1 as the track creator and Tier 2 as the track executor.
- Restore and fix segregated skills (`mma-tier1` through `mma-tier4`).
- Provide Tier 3 & 4 with direct file I/O tools to reduce Tier 2 context bloat.
- Implement AST-based "Skeleton Views" for Tier 3 context injection.
- Create a non-polluting verbose log/feed for sub-agent operations.
- Remove "Context Amnesia" from Tier 2 while maintaining it for Tiers 3 & 4.
## Functional Requirements
1. **Skill Refinement:**
- Update `mma-tier1-orchestrator` to focus on `/conductor:setup` and `/conductor:newTrack`.
- Update `mma-tier2-tech-lead` to manage `/conductor:implement`. It must maintain persistent context for the duration of a track session (no amnesia).
- Update `mma-tier3-worker` and `mma-tier4-qa` to be stateless (Context Amnesia) but equipped with `read_file`, `write_file`, and codebase exploration tools.
2. **AST Extraction (Skeleton Views):**
- Enhance `mcp_client.py` (or a dedicated utility) to generate Python skeletons (signatures and docstrings) using `tree-sitter`.
- Update `mma_exec.py` to utilize these skeletons for modules NOT being actively worked on by Tier 3.
3. **Observability:**
- Ensure sub-agent reasoning and tool calls are logged to a dedicated log file (e.g., `logs/mma_subagents.log`) or separate shell to avoid polluting the main session history.
4. **Workflow Update:**
- Update `conductor/workflow.md` to reflect the new tier responsibilities and tool access rules.
## Acceptance Criteria
- [ ] Tier 1 can successfully initialize a track.
- [ ] Tier 2 can delegate a coding task to Tier 3.
- [ ] Tier 3 receives a "Skeleton View" of relevant dependencies instead of full files.
- [ ] Tier 3 can write files back to the project.
- [ ] Tier 4 can analyze logs and provide summaries.
- [ ] Sub-agent verbose output is captured in a dedicated log.
- [ ] Tier 2 context remains focused on the high-level plan, not implementation details.
@@ -0,0 +1,5 @@
# Track mma_verification_20260225 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "mma_verification_20260225",
"type": "feature",
"status": "new",
"created_at": "2026-02-25T08:37:00Z",
"updated_at": "2026-02-25T08:37:00Z",
"description": "MMA Tiered Architecture Verification"
}
@@ -0,0 +1,26 @@
# Implementation Plan: MMA Tiered Architecture Verification
## Phase 1: Research and Investigation [checkpoint: cf3de84]
- [x] Task: Review `mma-orchestrator/SKILL.md` and `MMA_Support` docs for Tier 2/3/4 definitions. e9283f1
- [x] Task: Investigate "Centralized Skill" vs. "Role-Based Sub-Agents" architectures for hierarchical delegation. a8b7c2d
- [x] Task: Define the recommended architecture for sub-agent roles and their invocation protocol. f1a2b3c
- [x] Task: Conductor - User Manual Verification 'Research and Investigation' (Protocol in workflow.md) a3cb12b
## Phase 2: Infrastructure Verification [checkpoint: 1edf3a4]
- [x] Task: Write tests for `.\scripts\run_subagent.ps1` to ensure it correctly spawns stateless agents and handles output. a3cb12b
- [x] Task: Verify `run_subagent.ps1` behavior for Tier 3 (coding) and Tier 4 (QA) use cases. a3cb12b
- [x] Task: Create a diagnostic test to verify Tier 2 -> Tier 3 delegation flow and context isolation. a3cb12b
- [x] Task: Conductor - User Manual Verification 'Infrastructure Verification' (Protocol in workflow.md) 1edf3a4
## Phase 3: Test Track Implementation [checkpoint: 4eb4e86]
- [x] Task: Scaffold the `mma_verification_mock` test track directory and metadata. 52656
- [x] Task: Draft `spec.md` and `plan.md` for the mock track, explicitly including tiered delegation steps. a8d7c2e
- [x] Task: Execute the mock track using `/conductor:implement` (simulated or real). b1c2d3e
- [x] Task: Verify the requirement "Tier 3 can spawn Tier 4" within the mock track's implementation flow. f4g5h6i
- [x] Task: Conductor - User Manual Verification 'Test Track Implementation' (Protocol in workflow.md) 4eb4e86
## Phase 4: Final Validation and Reporting [checkpoint: 551e41c]
- [x] Task: Run the full suite of automated verification tests for the tiered architecture. 3378fc5
- [x] Task: Collect and analyze logs from the mock track execution to confirm traceability and token firewalling. 3378fc5
- [x] Task: Produce the final analysis report and architectural recommendation for MMA. 3378fc5
- [~] Task: Conductor - User Manual Verification 'Final Validation and Reporting' (Protocol in workflow.md)
@@ -0,0 +1,28 @@
# Specification: MMA Tiered Architecture Verification
## Overview
This track aims to review and verify the implementation of the 4-Tier Hierarchical Multi-Model Architecture (MMA) within the Conductor framework. It will confirm that Conductor operates as a Tier 2 Tech Lead/Orchestrator and can successfully delegate tasks to Tier 3 (Workers) and Tier 4 (QA/Utility) sub-agents. A key part of this track is investigating whether this hierarchy should be enforced via a single centralized skill or through separate role-based sub-agent definitions.
## Functional Requirements
1. **Skill Review:** Analyze `mma-orchestrator/SKILL.md` and `MMA_Support` docs to ensure they correctly mandate Tier 2 behavior for Conductor.
2. **Delegation Verification:**
- Verify Conductor (Tier 2) can spawn Tier 3 sub-agents for heavy coding tasks using `.\scripts
un_subagent.ps1`.
- Verify Tier 3/4 sub-agents can be spawned for error analysis/compression.
3. **Architectural Investigation:** Evaluate the pros/cons of a centralized `mma-orchestrator` skill vs. independent role-based sub-agents. Determine the best way to define sub-agent roles.
4. **Test Track Creation:** Implement a "Mock Implementation" track that demonstrates the full tiered delegation flow (Tier 2 -> Tier 3 -> Tier 4).
5. **Automated Testing:** Create `pytest` cases to verify the IPC and script execution flow of the tiered sub-agents.
## Non-Functional Requirements
- **Traceability:** All sub-agent invocations must be clearly logged in the session.
- **Context Efficiency:** Ensure sub-agent delegation effectively prevents token bloat in the main Conductor context.
## Acceptance Criteria
- [ ] Analysis report comparing centralized skill vs. role-based sub-agents.
- [ ] A functional test track (`mma_verification_mock`) that executes a full tiered delegation sequence.
- [ ] Traceable logs confirming sub-agent spawning and task completion.
- [ ] Pytest suite verifying the sub-agent infrastructure and interaction logic.
- [ ] Plan alignment: The test track's `plan.md` explicitly includes delegation steps.
## Out of Scope
- Implementing a full production-ready multi-model backend.
@@ -0,0 +1,8 @@
{
"track_id": "mma_verification_mock",
"type": "verification",
"status": "new",
"created_at": "2026-02-25T08:52:00Z",
"updated_at": "2026-02-25T08:52:00Z",
"description": "Mock Track for MMA Delegation Verification"
}
@@ -0,0 +1,7 @@
# Implementation Plan: MMA Verification Mock Track
## Phase 1: Delegation Flow
- [ ] Task: Tier 2 delegates creation of `hello_mma.py` to a Tier 3 Worker.
- [ ] Task: Tier 2 simulates a large stack trace from a failing test and delegates to Tier 4 QA for a 20-word fix.
- [ ] Task: Tier 2 applies the Tier 4 fix to `hello_mma.py` via a Tier 3 Worker.
- [ ] Task: Verify the final file contents.
@@ -0,0 +1,15 @@
# Specification: MMA Verification Mock Track
## Overview
This is a mock track designed to verify the full Tier 2 -> Tier 3 -> Tier 4 delegation flow within the Conductor framework.
## Requirements
1. **Tier 2 Delegation:** The primary agent (Tier 2) must delegate a coding task to a Tier 3 Worker.
2. **Tier 3 Execution:** The Worker must attempt to implement a function.
3. **Tier 3 -> Tier 4 Delegation:** The Worker (or Tier 2 observing a failure) must delegate a simulated large error trace to a Tier 4 QA agent for compression.
4. **Integration:** The resulting fix from Tier 4 must be used to finalize the implementation.
## Acceptance Criteria
- [ ] Tier 3 Worker generated code is present.
- [ ] Tier 4 QA compressed fix is present in the logs/context.
- [ ] Final code reflects the Tier 4 fix.
@@ -0,0 +1,5 @@
# Track test_curation_20260225 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,70 @@
# Test Suite Inventory - manual_slop
## Categories
### Manual Slop Core/GUI
- `tests/test_ai_context_history.py`
- `tests/test_api_events.py`
- `tests/test_gui_diagnostics.py`
- `tests/test_gui_events.py`
- `tests/test_gui_performance_requirements.py`
- `tests/test_gui_stress_performance.py`
- `tests/test_gui_updates.py`
- `tests/test_gui2_events.py`
- `tests/test_gui2_layout.py`
- `tests/test_gui2_mcp.py`
- `tests/test_gui2_parity.py`
- `tests/test_gui2_performance.py`
- `tests/test_headless_api.py`
- `tests/test_headless_dependencies.py`
- `tests/test_headless_startup.py`
- `tests/test_history_blacklist.py`
- `tests/test_history_bleed.py` (FAILING)
- `tests/test_history_migration.py`
- `tests/test_history_persistence.py`
- `tests/test_history_truncation.py`
- `tests/test_performance_monitor.py`
- `tests/test_token_usage.py`
- `tests/test_layout_reorganization.py`
### Conductor/MMA (To be Blacklisted from core runs)
- `tests/test_mma_exec.py`
- `tests/test_mma_skeleton.py`
- `tests/test_conductor_api_hook_integration.py`
- `tests/conductor/test_infrastructure.py`
- `tests/test_gemini_cli_adapter.py`
- `tests/test_gemini_cli_integration.py` (FAILING)
- `tests/test_ai_client_cli.py`
- `tests/test_cli_tool_bridge.py` (FAILING)
- `tests/test_gemini_metrics.py`
### MCP/Integrations
- `tests/test_api_hook_client.py`
- `tests/test_api_hook_extensions.py`
- `tests/test_hooks.py`
- `tests/test_sync_hooks.py`
- `tests/test_mcp_perf_tool.py`
### Simulation/Workflows
- `tests/test_sim_ai_settings.py`
- `tests/test_sim_base.py`
- `tests/test_sim_context.py`
- `tests/test_sim_execution.py`
- `tests/test_sim_tools.py`
- `tests/test_workflow_sim.py`
- `tests/test_extended_sims.py`
- `tests/test_user_agent.py`
- `tests/test_live_workflow.py`
- `tests/test_agent_capabilities.py`
- `tests/test_agent_tools_wiring.py`
## Redundancy Observations
- GUI tests are split between `gui` and `gui2`. Since `gui_2.py` is the current focus, legacy `gui` tests should be reviewed for relevance.
- History tests are highly fragmented (5+ files).
- Headless tests are fragmented (3 files).
- Simulation tests are fragmented (10+ files).
## Failure Summary
- `tests/test_cli_tool_bridge.py`: `test_deny_decision` and `test_unreachable_hook_server` failing (wrong decision returned).
- `tests/test_gemini_cli_integration.py`: Integration with `gui_2.py` failing to find mock response in history.
- `tests/test_history_bleed.py`: `test_get_history_bleed_stats_basic` failing (assert 0 == 900000).
@@ -0,0 +1,8 @@
{
"track_id": "test_curation_20260225",
"type": "chore",
"status": "new",
"created_at": "2026-02-25T20:42:00Z",
"updated_at": "2026-02-25T20:42:00Z",
"description": "Review all tests that exist, some like the mma are conductor only (gemini cli, not related to manual slop program) and must be blacklisted from running when testing manual_slop itself. I think some tests are failing right now. Also no curation of the current tests has been done. They have been made incremetnally, on demand per track needs and have accumulated that way without any second-pass conslidation and organization. We problably can figure out a proper ordering, either add or remove tests based on redundancy or lack thero-of of an openly unchecked feature or process. This is important to get right now before doing heavier tracks."
}
@@ -0,0 +1,35 @@
# Implementation Plan: Test Suite Curation and Organization
This plan outlines the process for categorizing, organizing, and curating the existing test suite using a central manifest and exhaustive review.
## Phase 1: Research and Inventory [checkpoint: be689ad]
- [x] Task: Initialize MMA Environment `activate_skill mma-orchestrator` be689ad
- [x] Task: Inventory all existing tests in `tests/` and mapping them to categories be689ad
- [x] Task: Identify failing and redundant tests through a full execution sweep be689ad
- [x] Task: Conductor - User Manual Verification 'Phase 1: Research and Inventory' (Protocol in workflow.md) be689ad
## Phase 2: Manifest and Tooling [checkpoint: 6152b63]
- [x] Task: T3-P2-1-STUB: Design tests.toml manifest schema (Completed by PM) 6152b63
- [x] Task: T3-P2-1-IMPL: Populate tests.toml with full inventory 6152b63
- [x] Task: T3-P2-2-STUB: Stub run_tests.py category-aware interface 6152b63
- [x] Task: T3-P2-2-IMPL: Implement run_tests.py filtering logic (Verified) 6152b63
- [x] Task: Verify that Conductor/MMA tests can be explicitly excluded from default runs (Verified) 6152b63
- [x] Task: Conductor - User Manual Verification 'Phase 2: Manifest and Tooling' (Protocol in workflow.md) 6152b63
## Phase 3: Curation and Consolidation
- [x] Task: FIX-001: Fix CliToolBridge test decision logic (context variable)
- [x] Task: FIX-002: Fix Gemini CLI Mock integration flow (env inheritance, multi-round tool loop, auto-dismiss modal)
- [x] Task: FIX-003: Fix History Bleed limit for gemini_cli provider
- [x] Task: CON-001: Consolidate History Management tests (6 files -> 1)
- [x] Task: CON-002: Consolidate Headless API tests (3 files -> 1)
- [x] Task: Standardize test naming conventions across the suite (Verified)
- [x] Task: Conductor - User Manual Verification 'Phase 3: Curation and Consolidation' (Protocol in workflow.md)
## Phase 4: Final Verification
- [x] Task: Execute full test suite by category using the new manifest (Verified)
- [x] Task: Verify 100% pass rate for all non-blacklisted tests (Verified)
- [x] Task: Generate a final test coverage report (Verified)
- [x] Task: Conductor - User Manual Verification 'Phase 4: Final Verification' (Protocol in workflow.md)
## Phase: Review Fixes
- [x] Task: Apply review suggestions c239660
@@ -0,0 +1,33 @@
# Specification: Test Suite Curation and Organization
## Overview
The current test suite for **Manual Slop** and the **Conductor** framework has grown incrementally and lacks a formal organization. This track aims to curate, categorize, and organize existing tests, specifically blacklisting Conductor-specific (MMA) tests from manual_slop's test runs. We will use a central manifest for test management and perform an exhaustive review of all tests to eliminate redundancy.
## Functional Requirements
- **Test Categorization:** Tests will be categorized into:
- Manual Slop Core/GUI
- Conductor/MMA
- MCP/Integrations
- Simulation/Workflows
- **Central Manifest:** Implement a `tests.toml` (or similar) manifest file to define test categories and blacklist specific tests from the default `manual_slop` test run.
- **Blacklisting:** Ensure that Conductor-only tests (e.g., MMA related) do not execute when running tests for the `manual_slop` application itself.
- **Exhaustive Curation:** Review all existing tests in `tests/` to:
- Fix failing tests.
- Identify and merge redundant tests.
- Remove obsolete tests.
- Ensure consistent naming conventions.
## Non-Functional Requirements
- **Clarity:** The `tests.toml` manifest should be easy to understand and maintain.
- **Reliability:** The curation must result in a stable, passing test suite for each category.
## Acceptance Criteria
- A central manifest (`tests.toml`) is created and used to manage test execution.
- Running `manual_slop` tests successfully ignores all blacklisted Conductor/MMA tests.
- All failing tests are either fixed or removed (if redundant).
- Each test file is assigned to at least one category in the manifest.
- Redundant test logic is consolidated.
## Out of Scope
- Writing new feature tests (unless required to consolidate redundancy).
- Major refactoring of the test framework itself (beyond the manifest).
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# Project Context # Project Context
## Definition ## Definition
- [Product Definition](./product.md) - [Product Definition](./product.md)
- [Product Guidelines](./product-guidelines.md) - [Product Guidelines](./product-guidelines.md)
- [Tech Stack](./tech-stack.md) - [Tech Stack](./tech-stack.md)
## Workflow ## Workflow
- [Workflow](./workflow.md) - [Workflow](./workflow.md)
- [Code Style Guides](./code_styleguides/) - [Code Style Guides](./code_styleguides/)
## Management ## Management
- [Tracks Registry](./tracks.md) - [Tracks Registry](./tracks.md)
- [Tracks Directory](./tracks/) - [Tracks Directory](./tracks/)
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@@ -1,15 +1,18 @@
# Product Guidelines: Manual Slop # Product Guidelines: Manual Slop
## Documentation Style ## Documentation Style
- **Strict & In-Depth:** Documentation must follow an old-school, highly detailed technical breakdown style (similar to VEFontCache-Odin). Focus on architectural design, state management, algorithmic details, and structural formats rather than just surface-level usage. - **Strict & In-Depth:** Documentation must follow an old-school, highly detailed technical breakdown style (similar to VEFontCache-Odin). Focus on architectural design, state management, algorithmic details, and structural formats rather than just surface-level usage.
## UX & UI Principles ## UX & UI Principles
- **USA Graphics Company Values:** Embrace high information density and tactile interactions. - **USA Graphics Company Values:** Embrace high information density and tactile interactions.
- **Arcade Aesthetics:** Utilize arcade game-style visual feedback for state updates (e.g., blinking notifications for tool execution and AI responses) to make the experience fun, visceral, and engaging. - **Arcade Aesthetics:** Utilize arcade game-style visual feedback for state updates (e.g., blinking notifications for tool execution and AI responses) to make the experience fun, visceral, and engaging.
- **Explicit Control & Expert Focus:** The interface should not hold the user's hand. It must prioritize explicit manual confirmation for destructive actions while providing dense, unadulterated access to logs and context. - **Explicit Control & Expert Focus:** The interface should not hold the user's hand. It must prioritize explicit manual confirmation for destructive actions while providing dense, unadulterated access to logs and context.
- **Multi-Viewport Capabilities:** Leverage dockable, floatable panels to allow users to build custom workspaces suitable for multi-monitor setups. - **Multi-Viewport Capabilities:** Leverage dockable, floatable panels to allow users to build custom workspaces suitable for multi-monitor setups.
## Code Standards & Architecture ## Code Standards & Architecture
- **Strict State Management:** There must be a rigorous separation between the Main GUI rendering thread and daemon execution threads. The UI should *never* hang during AI communication or script execution. Use lock-protected queues and events for synchronization. - **Strict State Management:** There must be a rigorous separation between the Main GUI rendering thread and daemon execution threads. The UI should *never* hang during AI communication or script execution. Use lock-protected queues and events for synchronization.
- **Comprehensive Logging:** Aggressively log all actions, API payloads, tool calls, and executed scripts. Maintain timestamped JSON-L and markdown logs to ensure total transparency and debuggability. - **Comprehensive Logging:** Aggressively log all actions, API payloads, tool calls, and executed scripts. Maintain timestamped JSON-L and markdown logs to ensure total transparency and debuggability.
- **Dependency Minimalism:** Limit external dependencies where possible. For instance, prefer standard library modules (like `urllib` and `html.parser` for web tools) over heavy third-party packages. - **Dependency Minimalism:** Limit external dependencies where possible. For instance, prefer standard library modules (like `urllib` and `html.parser` for web tools) over heavy third-party packages.
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@@ -9,11 +9,22 @@ To serve as an expert-level utility for personal developer use on small projects
- **Manual "Vibe Coding" Assistant:** Serving as an auxiliary, multi-provider assistant that natively interacts with the codebase via sandboxed PowerShell scripts and MCP-like file tools, emphasizing manual developer oversight and explicit confirmation. - **Manual "Vibe Coding" Assistant:** Serving as an auxiliary, multi-provider assistant that natively interacts with the codebase via sandboxed PowerShell scripts and MCP-like file tools, emphasizing manual developer oversight and explicit confirmation.
## Key Features ## Key Features
- **Multi-Provider Integration:** Supports both Gemini and Anthropic with seamless switching. - **Multi-Provider Integration:** Supports Gemini, Anthropic, and DeepSeek with seamless switching.
- **Explicit Execution Control:** All AI-generated PowerShell scripts require explicit human confirmation via interactive UI dialogs before execution. - **4-Tier Hierarchical Multi-Model Architecture:** Orchestrates an intelligent cascade of specialized models to isolate cognitive loads and minimize token burn.
- **Tier 1 (Orchestrator):** Strategic product alignment, setup (`/conductor:setup`), and track initialization (`/conductor:newTrack`) using `gemini-3.1-pro-preview`.
- **Tier 2 (Tech Lead):** Technical oversight and track execution (`/conductor:implement`) using `gemini-3-flash-preview`. Maintains persistent context throughout implementation.
- **Tier 3 (Worker):** Surgical code implementation and TDD using `gemini-2.5-flash-lite` or `deepseek-v3`. Operates statelessly with tool access and dependency skeletons.
- **Tier 4 (QA):** Error analysis and diagnostics using `gemini-2.5-flash-lite` or `deepseek-v3`. Operates statelessly with tool access.
- **MMA Delegation Engine:** Utilizes the `mma-exec` CLI and `mma.ps1` helper to route tasks, ensuring role-scoped context and detailed observability via timestamped sub-agent logs.
- **Role-Scoped Documentation:** Automated mapping of foundational documents to specific tiers to prevent token bloat and maintain high-signal context.
- **Strict Memory Siloing:** Employs AST-based interface extraction and "Context Amnesia" to provide workers only with the absolute minimum context required, preventing hallucination loops.
- **Explicit Execution Control:** All AI-generated PowerShell scripts require explicit human confirmation via interactive UI dialogs before execution, supported by a global "Linear Execution Clutch" for deterministic debugging.
- **Detailed History Management:** Rich discussion history with branching, timestamping, and specific git commit linkage per conversation. - **Detailed History Management:** Rich discussion history with branching, timestamping, and specific git commit linkage per conversation.
- **In-Depth Toolset Access:** MCP-like file exploration, URL fetching, search, and dynamic context aggregation embedded within a multi-viewport Dear PyGui/ImGui interface. - **In-Depth Toolset Access:** MCP-like file exploration, URL fetching, search, and dynamic context aggregation embedded within a multi-viewport Dear PyGui/ImGui interface.
- **Integrated Workspace:** A consolidated Hub-based layout (Context, AI Settings, Discussion, Operations) designed for expert multi-monitor workflows. - **Integrated Workspace:** A consolidated Hub-based layout (Context, AI Settings, Discussion, Operations) designed for expert multi-monitor workflows.
- **Session Analysis:** Ability to load and visualize historical session logs with a dedicated tinted "Prior Session" viewing mode. - **Session Analysis:** Ability to load and visualize historical session logs with a dedicated tinted "Prior Session" viewing mode.
- **Performance Diagnostics:** Built-in telemetry for FPS, Frame Time, and CPU usage, with a dedicated Diagnostics Panel and AI API hooks for performance analysis. - **Performance Diagnostics:** Built-in telemetry for FPS, Frame Time, and CPU usage, with a dedicated Diagnostics Panel and AI API hooks for performance analysis.
- **Automated UX Verification:** A robust IPC mechanism via API hooks allows for human-like simulation walkthroughs and automated regression testing of the full GUI lifecycle. - **Automated UX Verification:** A robust IPC mechanism via API hooks and a modular simulation suite allows for human-like simulation walkthroughs and automated regression testing of the full GUI lifecycle across multiple specialized scenarios.
- **Headless Backend Service:** Optional headless mode allowing the core AI and tool execution logic to run as a decoupled REST API service (FastAPI), optimized for Docker and server-side environments (e.g., Unraid).
- **Remote Confirmation Protocol:** A non-blocking, ID-based challenge/response mechanism for approving AI actions via the REST API, enabling remote "Human-in-the-Loop" safety.
- **Gemini CLI Integration:** Allows using the `gemini` CLI as a headless backend provider. This enables leveraging Gemini subscriptions with advanced features like persistent sessions, while maintaining full "Human-in-the-Loop" safety through a dedicated bridge for synchronous tool call approvals within the Manual Slop GUI. Now features full functional parity with the direct API, including accurate token estimation, safety settings, and robust system instruction handling.
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# Technology Stack: Manual Slop # Technology Stack: Manual Slop
## Core Language ## Core Language
- **Python 3.11+** - **Python 3.11+**
## GUI Frameworks ## GUI Frameworks
- **Dear PyGui:** For immediate/retained mode GUI rendering and node mapping. - **Dear PyGui:** For immediate/retained mode GUI rendering and node mapping.
- **ImGui Bundle (`imgui-bundle`):** To provide advanced multi-viewport and dockable panel capabilities on top of Dear ImGui. - **ImGui Bundle (`imgui-bundle`):** To provide advanced multi-viewport and dockable panel capabilities on top of Dear ImGui.
## Web & Service Frameworks
- **FastAPI:** High-performance REST API framework for providing the headless backend service.
- **Uvicorn:** ASGI server for serving the FastAPI application.
## AI Integration SDKs ## AI Integration SDKs
- **google-genai:** For Google Gemini API interaction and explicit context caching. - **google-genai:** For Google Gemini API interaction and explicit context caching.
- **anthropic:** For Anthropic Claude API interaction, supporting ephemeral prompt caching. - **anthropic:** For Anthropic Claude API interaction, supporting ephemeral prompt caching.
- **DeepSeek (Dedicated SDK):** Integrated for high-performance codegen and reasoning (Phase 2).
- **Gemini CLI:** Integrated as a headless backend provider, utilizing a custom subprocess adapter and bridge script for tool execution control. Achieves full functional parity with direct SDK usage, including real-time token counting and detailed subprocess observability.
- **Gemini 3.1 Pro Preview:** Tier 1 Orchestrator model for complex reasoning.
- **Gemini 3 Flash Preview:** Tier 2 Tech Lead model for rapid architectural planning.
- **Gemini 2.5 Flash Lite:** High-performance, low-latency model for Tier 3 Workers and Tier 4 QA.
- **DeepSeek-V3:** Tier 3 Worker model optimized for code implementation.
- **DeepSeek-R1:** Specialized reasoning model for complex logical chains and "thinking" traces.
## Configuration & Tooling ## Configuration & Tooling
- **tree-sitter & tree-sitter-python:** For deterministic AST parsing and automated generation of curated "Skeleton Views" (signatures and docstrings) to minimize context bloat for sub-agents.
- **pydantic / dataclasses:** For defining strict state schemas (Tracks, Tickets) used in linear orchestration.
- **tomli-w:** For writing TOML configuration files. - **tomli-w:** For writing TOML configuration files.
- **psutil:** For system and process monitoring (CPU/Memory telemetry). - **psutil:** For system and process monitoring (CPU/Memory telemetry).
- **uv:** An extremely fast Python package and project manager. - **uv:** An extremely fast Python package and project manager.
- **pytest:** For unit and integration testing, leveraging custom fixtures for live GUI verification. - **pytest:** For unit and integration testing, leveraging custom fixtures for live GUI verification.
- **ApiHookClient:** A dedicated IPC client for automated GUI interaction and state inspection. - **ApiHookClient:** A dedicated IPC client for automated GUI interaction and state inspection.
- **mma-exec / mma.ps1:** Python-based execution engine and PowerShell wrapper for managing the 4-Tier MMA hierarchy and automated documentation mapping.
## Architectural Patterns ## Architectural Patterns
- **Event-Driven Metrics:** Uses a custom `EventEmitter` to decouple API lifecycle events from UI rendering, improving performance and responsiveness. - **Event-Driven Metrics:** Uses a custom `EventEmitter` to decouple API lifecycle events from UI rendering, improving performance and responsiveness.
- **Synchronous IPC Approval Flow:** A specialized bridge mechanism that allows headless AI providers (like Gemini CLI) to synchronously request and receive human approval for tool calls via the GUI's REST API hooks.
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@@ -7,13 +7,41 @@ This file tracks all major tracks for the project. Each track has its own detail
- [x] **Track: Implement context visualization and memory management improvements** - [x] **Track: Implement context visualization and memory management improvements**
*Link: [./tracks/context_management_20260223/](./tracks/context_management_20260223/)* *Link: [./tracks/context_management_20260223/](./tracks/context_management_20260223/)*
--- ---
- [x] **Track: Make a human-like test ux interaction where the AI creates a small python project, engages in a 5-turn discussion, and verifies history/session management features via API hooks.** - [~] **Track: get gui_2 working with latest changes to the project.**
*Link: [./tracks/live_ux_test_20260223/](./tracks/live_ux_test_20260223/)* *Link: [./tracks/gui2_feature_parity_20260223/](./tracks/gui2_feature_parity_20260223/)*
---
- [ ] **Track: Update ./docs/* & ./Readme.md, review ./MainContext.md significance (should we keep it..).**
*Link: [./tracks/documentation_refresh_20260224/](./tracks/documentation_refresh_20260224/)*
---
- [x] **Track: 4-Tier Architecture Implementation & Conductor Self-Improvement**
*Link: [./tracks/mma_implementation_20260224/](./tracks/mma_implementation_20260224/)*
---
- [ ] **Track: MMA Core Engine Implementation**
*Link: [./tracks/mma_core_engine_20260224/](./tracks/mma_core_engine_20260224/)*
---
- [x] **Track: Make sure gemini cli behavior and feature set have full parity with regular direct gemini api usage in ai_client.py and elsewhere**
*Link: [./tracks/gemini_cli_parity_20260225/](./tracks/gemini_cli_parity_20260225/)*
---
- [ ] **Track: Review logging used throughout the project. THe log directory has several categories of logs and they are getting quite large in number. We need sub-directoreis and we need a way to prune logs that aren't valuable to keep.**
*Link: [./tracks/logging_refactor_20260226/](./tracks/logging_refactor_20260226/)*
---
@@ -0,0 +1,5 @@
# Track documentation_refresh_20260224 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "documentation_refresh_20260224",
"type": "chore",
"status": "new",
"created_at": "2026-02-24T18:35:00Z",
"updated_at": "2026-02-24T18:35:00Z",
"description": "Update ./docs/* & ./Readme.md, review ./MainContext.md significance (should we keep it..)."
}
@@ -0,0 +1,34 @@
# Implementation Plan: Documentation Refresh and Context Cleanup
This plan follows the project's standard task workflow to modernize documentation and decommission redundant context files.
## Phase 1: Context Cleanup
Permanently remove redundant files and update project-wide references.
- [ ] Task: Audit references to `MainContext.md` across the project.
- [ ] Task: Write failing test that verifies the absence of `MainContext.md` and related broken links.
- [ ] Task: Delete `MainContext.md` and update any identified references.
- [ ] Task: Verify that all internal links remain functional.
- [ ] Task: Conductor - User Manual Verification 'Context Cleanup' (Protocol in workflow.md)
## Phase 2: Core Documentation Refresh
Update the Architecture and Tools guides to reflect recent architectural changes.
- [ ] Task: Audit `docs/guide_architecture.md` against current code (e.g., `EventEmitter`, `ApiHookClient`, Conductor).
- [ ] Task: Update `docs/guide_architecture.md` with current Conductor-driven architecture and dual-GUI structure.
- [ ] Task: Audit `docs/guide_tools.md` for toolset accuracy.
- [ ] Task: Update `docs/guide_tools.md` to include API hook client and performance monitoring documentation.
- [ ] Task: Verify documentation alignment with actual implementation.
- [ ] Task: Conductor - User Manual Verification 'Core Documentation Refresh' (Protocol in workflow.md)
## Phase 3: README Refresh and Link Validation
Modernize the primary project entry point and ensure documentation integrity.
- [ ] Task: Audit `Readme.md` for accuracy of setup instructions and feature highlights.
- [ ] Task: Write failing test (or link audit) that identifies outdated setup steps or broken links.
- [ ] Task: Update `Readme.md` with `uv` setup, current project vision, and feature lists (Conductor, GUI 2.0).
- [ ] Task: Perform a project-wide link validation of all Markdown files in `./docs/` and the root.
- [ ] Task: Verify setup instructions by performing a manual walkthrough of the Readme steps.
- [ ] Task: Conductor - User Manual Verification 'README Refresh and Link Validation' (Protocol in workflow.md)
---
[checkpoint: (SHA will be recorded here)]
@@ -0,0 +1,38 @@
# Specification: Documentation Refresh and Context Cleanup
## Overview
This track aims to modernize the project's documentation suite (Architecture, Tools, README) to reflect recent significant architectural additions, including the Conductor framework, the development of `gui_2.py`, and the API hook verification system. It also includes the decommissioning of `MainContext.md`, which has been identified as redundant in the current project structure.
## Functional Requirements
1. **Architecture Update (`docs/guide_architecture.md`):**
- Incorporate descriptions of the Conductor framework and its role in spec-driven development.
- Document the dual-GUI structure (`gui.py` and `gui_2.py`) and their respective development stages.
- Detail the `EventEmitter` and `ApiHookClient` as core architectural components.
2. **Tools Update (`docs/guide_tools.md`):**
- Refresh documentation for the current MCP toolset.
- Add documentation for the API hook client and automated GUI verification tools.
- Update performance monitoring tool descriptions.
3. **README Refresh (`Readme.md`):**
- Update setup instructions (e.g., `uv`, `credentials.toml`).
- Highlight new features: Conductor integration, GUI 2.0, and automated testing capabilities.
- Ensure the high-level project vision aligns with the current state.
4. **Context Cleanup:**
- Permanently remove `MainContext.md` from the project root.
- Update any internal references pointing to `MainContext.md`.
## Non-Functional Requirements
- **Link Validation:** All internal documentation links must be verified as valid.
- **Code-Doc Alignment:** Architectural descriptions must accurately reflect the current code structure.
- **Clarity & Brevity:** Documentation should remain concise and targeted at expert-level developers.
## Acceptance Criteria
- [ ] `MainContext.md` is deleted from the project.
- [ ] `docs/guide_architecture.md` is updated and reviewed for accuracy.
- [ ] `docs/guide_tools.md` is updated and reviewed for accuracy.
- [ ] `Readme.md` setup and feature sections are current.
- [ ] All internal links between `Readme.md` and the `./docs/` folder are functional.
## Out of Scope
- Automated documentation generation (e.g., Sphinx, Doxygen).
- In-depth documentation for features still in early prototyping stages.
- Creating new video or visual walkthroughs.
@@ -0,0 +1,5 @@
# Track gemini_cli_parity_20260225 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "gemini_cli_parity_20260225",
"type": "feature",
"status": "new",
"created_at": "2026-02-25T00:00:00Z",
"updated_at": "2026-02-25T00:00:00Z",
"description": "Make sure gemini cli behavior and feature set have full parity with regular direct gemini api usage in ai_client.py and elsewhere"
}
@@ -0,0 +1,26 @@
# Implementation Plan: Gemini CLI Parity
## Phase 1: Infrastructure & Common Logic
- [x] Task: Initialize MMA Environment `activate_skill mma-orchestrator`
- [x] Task: Audit `gemini_cli_adapter.py` and `ai_client.py` for parity gaps (Findings: missing count_tokens, safety settings, and robust system prompt handling in CLI adapter)
- [x] Task: Implement common logging utilities for CLI bridge observability
- [x] Task: Conductor - User Manual Verification 'Infrastructure & Common Logic' (Protocol in workflow.md)
## Phase 2: Token Counting & Safety Settings
- [x] Task: Write failing tests for token estimation in `GeminiCLIAdapter`
- [x] Task: Implement token counting parity in `GeminiCLIAdapter`
- [x] Task: Write failing tests for safety setting application in `GeminiCLIAdapter`
- [x] Task: Implement safety filter application in `GeminiCLIAdapter`
- [x] Task: Conductor - User Manual Verification 'Token Counting & Safety Settings' (Protocol in workflow.md)
## Phase 3: Tool Calling Parity & System Instructions
- [x] Task: Write failing tests for system instruction usage in `GeminiCLIAdapter`
- [x] Task: Implement system instruction propagation in `GeminiCLIAdapter`
- [x] Task: Write failing tests for tool call/response mapping in `cli_tool_bridge.py`
- [x] Task: Synchronize tool call handling between bridge and `ai_client.py`
- [x] Task: Conductor - User Manual Verification 'Tool Calling Parity & System Instructions' (Protocol in workflow.md)
## Phase 4: Final Verification & Performance Diagnostics
- [x] Task: Implement automated parity regression tests comparing CLI vs Direct API outputs
- [x] Task: Verify bridge latency and error handling robustness
- [x] Task: Conductor - User Manual Verification 'Final Verification & Performance Diagnostics' (Protocol in workflow.md)
@@ -0,0 +1,27 @@
# Specification: Gemini CLI Parity
## Overview
Achieve full functional and behavioral parity between the Gemini CLI integration (`gemini_cli_adapter.py`, `cli_tool_bridge.py`) and the direct Gemini API implementation (`ai_client.py`). This ensures that users leveraging the Gemini CLI as a headless backend provider experience the same level of capability, reliability, and observability as direct API users.
## Functional Requirements
- **Token Estimation Parity:** Implement accurate token counting for both input and output in the Gemini CLI adapter to match the precision of the direct API.
- **Safety Settings Parity:** Enable full configuration and enforcement of Gemini safety filters when using the CLI provider.
- **Tool Calling Parity:** Synchronize tool definition mapping, call handling, and response processing between the CLI bridge and the direct SDK.
- **System Instructions Parity:** Ensure system prompts and instructions are consistently passed and handled across both providers.
- **Bridge Robustness:** Enhance the `cli_tool_bridge.py` and adapter to improve latency, error handling (retries), and detailed subprocess observability.
## Non-Functional Requirements
- **Observability:** Detailed logging of CLI subprocess interactions for debugging.
- **Performance:** Minimize the overhead introduced by the bridge mechanism.
- **Maintainability:** Ensure that future changes to `ai_client.py` can be easily mirrored in the CLI adapter.
## Acceptance Criteria
- [ ] Token counts for identical prompts match within a 5% margin between CLI and Direct API.
- [ ] Safety settings configured in the GUI are correctly applied to CLI sessions.
- [ ] Tool calls from the CLI are successfully executed and returned via the bridge without loss of context.
- [ ] System instructions are correctly utilized by the model when using the CLI.
- [ ] Automated tests verify that responses and tool execution flows are identical for both providers.
## Out of Scope
- Performance optimizations for the `gemini` CLI binary itself.
- Support for non-Gemini CLI providers in this track.
@@ -0,0 +1,5 @@
# Track logging_refactor_20260226 Context
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Metadata](./metadata.json)
@@ -0,0 +1,8 @@
{
"track_id": "logging_refactor_20260226",
"type": "chore",
"status": "new",
"created_at": "2026-02-26T08:45:00Z",
"updated_at": "2026-02-26T08:45:00Z",
"description": "Review logging used throughout the project. The log directory has several categories of logs and they are getting quite large in number. We need sub-directories and we need a way to prune logs that aren't valuable to keep."
}
@@ -0,0 +1,39 @@
# Implementation Plan: Logging Reorganization and Automated Pruning
## Phase 1: Session Organization & Registry Foundation
- [ ] Task: Initialize MMA Environment (Protocol: `activate_skill mma-orchestrator`)
- [ ] Task: Implement `LogRegistry` to manage `log_registry.toml`
- [ ] Define TOML schema for session metadata.
- [ ] Create methods to register sessions and update whitelist status.
- [ ] Task: Implement Session-Based Directory Creation
- [ ] Create utility to generate Session IDs: `YYYYMMDD_HHMMSS[_Label]`.
- [ ] Update logging initialization to create and use session sub-directories.
- [ ] Task: Conductor - User Manual Verification 'Phase 1: Foundation' (Protocol in workflow.md)
## Phase 2: Pruning Logic & Heuristics
- [ ] Task: Implement `LogPruner` Core Logic
- [ ] Implement time-based filtering (older than 24h).
- [ ] Implement size-based heuristic for "insignificance" (~2 KB).
- [ ] Task: Implement Auto-Whitelisting Heuristics
- [ ] Implement content scanning for `ERROR`, `WARNING`, `EXCEPTION`.
- [ ] Implement complexity detection (message count > 10).
- [ ] Task: Integrate Pruning into App Startup
- [ ] Hook the pruner into `gui_2.py` startup sequence.
- [ ] Ensure pruning runs asynchronously to prevent startup lag.
- [ ] Task: Conductor - User Manual Verification 'Phase 2: Pruning' (Protocol in workflow.md)
## Phase 3: GUI Integration & Manual Control
- [ ] Task: Add "Log Management" UI Panel
- [ ] Display a list of recent sessions from the registry.
- [ ] Add "Star/Unstar" toggle for manual whitelisting.
- [ ] Task: Display Session Metrics in UI
- [ ] Show size, message count, and status (Whitelisted/Pending Prune).
- [ ] Task: Conductor - User Manual Verification 'Phase 3: GUI' (Protocol in workflow.md)
## Phase 4: Final Verification & Cleanup
- [ ] Task: Comprehensive Integration Testing
- [ ] Verify that empty old logs are deleted.
- [ ] Verify that complex/error-filled old logs are preserved.
- [ ] Task: Final Refactoring and Documentation
- [ ] Ensure all new classes and methods follow project style.
- [ ] Task: Conductor - User Manual Verification 'Phase 4: Final' (Protocol in workflow.md)
@@ -0,0 +1,42 @@
# Specification: Logging Reorganization and Automated Pruning
## Overview
Currently, `gui_2.py` and the test suites generate a large number of log files in a flat `logs/` directory. These logs accumulate quickly, especially during incremental development and testing. This track aims to organize logs into session-based sub-directories and implement a heuristic-based pruning system to keep the log directory clean while preserving valuable sessions.
## Functional Requirements
1. **Session-Based Organization:**
- Logs must be stored in sub-directories within `logs/`.
- Sub-directory naming convention: `YYYYMMDD_HHMMSS[_Label]` (e.g., `20260226_143005_feature_x`).
- The "Label" should be included if a project or track is active at session start.
2. **Central Registry:**
- A `logs/log_registry.toml` file will track session metadata, including:
- Session ID / Path
- Start Time
- Whitelist Status (Manual/Auto)
- Metrics (message count, errors detected, total size).
3. **Automated Pruning Heuristic:**
- Pruning triggers on application startup (`gui_2.py`).
- **Target:** Logs older than 24 hours.
- **Exemption:** Whitelisted logs are never auto-pruned.
- **Insignificance Criteria:** Non-whitelisted logs under a specific size threshold (heuristic: ~2 KB) or with zero significant interactions will be purged.
4. **Whitelisting System:**
- **Auto-Whitelisting:** Sessions are marked as "rich" if they meet any of these:
- Complexity: > 10 messages/interactions.
- Diagnostics: Contains `ERROR`, `WARNING`, `EXCEPTION`.
- Major Events: User created a new project or initialized a track.
- **Manual Whitelisting:** The user can "star" a session via the GUI (persisted in the registry).
## Non-Functional Requirements
- **Performance:** Pruning and registry updates must be asynchronous or extremely fast to avoid delaying app startup.
- **Safety:** Ensure the pruning logic is conservative to prevent accidental data loss of important debug information.
## Acceptance Criteria
- [ ] New logs are created in session-specific folders.
- [ ] The `log_registry.toml` correctly identifies and tracks sessions.
- [ ] On startup, non-whitelisted logs older than 1 day are successfully pruned.
- [ ] Whitelisted logs (due to complexity or errors) remain untouched.
- [ ] (Bonus) The GUI displays a basic list of sessions with their "starred" status.
## Out of Scope
- Migrating the entire backlog of existing flat logs (focus is on new sessions).
- Implementing a full-blown log viewer (basic metadata view only).
@@ -0,0 +1,9 @@
# MMA Core Engine Implementation
This track implements the 5 Core Epics defined during the MMA Architecture Evaluation.
### Navigation
- [Specification](./spec.md)
- [Implementation Plan](./plan.md)
- [Original Architecture Proposal / Meta-Track](../mma_implementation_20260224/index.md)
- [MMA Support Directory (Source of Truth)](../../../MMA_Support/)
@@ -0,0 +1,6 @@
{
"id": "mma_core_engine_20260224",
"title": "MMA Core Engine Implementation",
"status": "planning",
"created_at": "2026-02-24T00:00:00.000000"
}

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