74 lines
16 KiB
JSON
74 lines
16 KiB
JSON
{
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"C:\\projects\\manual_slop\\src\\ai_client.py": {
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"hash": "db4b3aad82599499d7796860757e229d2d412c5ccb3e821ddaee68ca0d3ad5d3",
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"summary": "This Python module serves as a unified client interface for multiple Large Language Model (LLM) providers, abstracting away provider-specific differences in tool handling, history management, and caching. It includes specialized logic for Anthropic to manage token limits and for Gemini to inject initial context efficiently.\n\n* **Multi-Provider Abstraction:** Provides a single interface for interacting with LLMs from Anthropic, Gemini, DeepSeek, and Minimax.\n* **Provider-Specific Optimization:** Implements tailored strategies for managing token limits (Anthropic) and context injection (Gemini).\n* **Tooling and Bias Management:** Supports setting agent tools, tool presets, and bias profiles to influence LLM behavior.\n* **Communication Logging:** Tracks and logs communication events with LLM providers.\n* **Configuration and State Management:** Manages global generation parameters, credentials, and session state.\n\n**Outline:**\n**Python** \u2014 2501 lines\nimports: __future__, anthropic, asyncio, collections, datetime, difflib, google, hashlib, json, openai, os, pathlib, requests, src, sys, threading, time, tomllib, typing\nconstants: _GEMINI_CACHE_TTL, _BIAS_ENGINE, MAX_TOOL_ROUNDS, _MAX_TOOL_OUTPUT_BYTES, _ANTHROPIC_CHUNK_SIZE, _SYSTEM_PROMPT, COMMS_CLAMP_CHARS, TOOL_NAME, _CACHED_ANTHROPIC_TOOLS, _DIFF_LINE_THRESHOLD, _CACHED_DEEPSEEK_TOOLS, _CHARS_PER_TOKEN, _ANTHROPIC_MAX_PROMPT_TOKENS, _GEMINI_MAX_INPUT_TOKENS, _FILE_REFRESH_MARKER\nclass ProviderError: __init__, ui_message\nfunctions: set_model_params, get_history_trunc_limit, set_history_trunc_limit, get_current_tier, set_current_tier, set_custom_system_prompt, set_base_system_prompt, set_use_default_base_prompt, set_project_context_marker, _get_context_marker, _get_combined_system_prompt, get_combined_system_prompt, _append_comms, get_comms_log, clear_comms_log, get_credentials_path, _load_credentials, _classify_anthropic_error, _classify_gemini_error, _classify_deepseek_error, _classify_minimax_error, set_provider, get_provider, cleanup, reset_session, get_gemini_cache_stats, list_models, _list_gemini_cli_models, _list_gemini_models, _list_anthropic_models, _list_deepseek_models, _list_minimax_models, set_agent_tools, set_tool_preset, set_bias_profile, get_bias_profile, _build_anthropic_tools, _get_anthropic_tools, _gemini_tool_declaration, _execute_tool_calls_concurrently, _execute_single_tool_call_async, _run_script, _truncate_tool_output, _reread_file_items, _build_file_context_text, _build_file_diff_text, _build_deepseek_tools, _get_deepseek_tools, _content_block_to_dict, _ensure_gemini_client, _get_gemini_history_list, _send_gemini, _send_gemini_cli, _estimate_message_tokens, _invalidate_token_estimate, _estimate_prompt_tokens, _strip_stale_file_refreshes, _trim_anthropic_history, _ensure_anthropic_client, _chunk_text, _build_chunked_context_blocks, _strip_cache_controls, _add_history_cache_breakpoint, _repair_anthropic_history, _send_anthropic, _ensure_deepseek_client, _ensure_minimax_client, _repair_deepseek_history, _send_deepseek, _send_minimax, run_tier4_analysis, run_tier4_patch_callback, run_tier4_patch_generation, get_token_stats, send, _add_bleed_derived, get_history_bleed_stats, run_subagent_summarization"
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},
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"C:\\projects\\manual_slop\\conductor\\workflow.md": {
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"hash": "ac3f4c0b807ce88bbbfdbd33b4d0888d4d5f97abca5642c2d5a3d9f2c1bc9fa5",
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"summary": "This document outlines the mandatory workflow for the Conductor project, emphasizing strict adherence to code style, a test-driven development process with delegated implementation, and atomic, well-documented commits. Key takeaways include the critical importance of 1-space indentation for Python, the use of specific MCP tools to avoid indentation destruction, and a multi-phase task execution involving research, failing tests, implementation, refactoring, and thorough documentation via Git notes.\n\n**Outline:**\n**Markdown** \u2014 389 lines\nheadings:\n Project Workflow\n Session Start Checklist (MANDATORY)\n Code Style (MANDATORY - Python)\n CRITICAL: Native Edit Tool Destroys Indentation\n Guiding Principles\n Task Workflow\n Standard Task Workflow\n Phase Completion Verification and Checkpointing Protocol\n Verification via API Hooks\n Quality Gates\n Development Commands\n Setup\n Example: Commands to set up the development environment (e.g., install dependencies, configure database)\n e.g., for a Node.js project: npm install\n e.g., for a Go project: go mod tidy\n Daily Development\n Example: Commands for common daily tasks (e.g., start dev server, run tests, lint, format)\n e.g., for a Node.js project: npm run dev, npm test, npm run lint\n e.g., for a Go project: go run main.go, go test ./..., go fmt ./...\n Before Committing\n Example: Commands to run all pre-commit checks (e.g., format, lint, type check, run tests)\n e.g., for a Node.js project: npm run check\n e.g., for a Go project: make check (if a Makefile exists)\n Testing Requirements\n Structural Testing Contract\n Unit Testing\n Integration Testing\n Mobile Testing\n Code Review Process\n Self-Review Checklist\n Commit Guidelines\n Message Format\n Types\n Examples\n Definition of Done\n Conductor Token Firewalling & Model Switching Strategy\n 1. Active Model Switching (Simulating the 4 Tiers)\n 2. Context Management and Token Firewalling\n 3. Phase Checkpoints (The Final Defense)"
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},
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"C:\\projects\\manual_slop\\src\\models.py": {
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"hash": "6e097e6a78ff02e3050212f3021761ebfe2aa9ce82b7074656842b394453ec90",
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"summary": "This module defines the core data structures for the Manual Slop application, including tasks, tracks, and configuration, enabling project orchestration and persistence.\n\n* **Data Models:** Defines `Ticket`, `Track`, `WorkerContext`, `Metadata`, `TrackState`, `FileItem`, `Preset`, `Tool`, `ToolPreset`, `BiasProfile`, `Persona`, `MCPServerConfig`, `MCPConfiguration`, `VectorStoreConfig`, `RAGConfig`, and `WorkspaceProfile` as dataclasses.\n* **Serialization:** Implements `to_dict` and `from_dict` methods for all dataclasses to support TOML/JSON persistence.\n* **Configuration Management:** Provides functions `load_config`, `save_config`, and `parse_history_entries` for managing application settings and historical data.\n* **Tool Definitions:** Lists available `AGENT_TOOL_NAMES` and categorizes them in `DEFAULT_TOOL_CATEGORIES`.\n\n**Outline:**\n**Python** \u2014 704 lines\nimports: __future__, dataclasses, datetime, json, os, pathlib, re, src, sys, tomli_w, tomllib, typing\nconstants: CONFIG_PATH, AGENT_TOOL_NAMES, DEFAULT_TOOL_CATEGORIES\nclass ThinkingSegment: to_dict, from_dict\nclass Ticket: mark_blocked, mark_manual_block, clear_manual_block, mark_complete, get, to_dict, from_dict\nclass Track: get_executable_tickets, to_dict, from_dict\nclass WorkerContext\nclass Metadata: to_dict, from_dict\nclass TrackState: to_dict, from_dict\nclass FileItem: to_dict, from_dict\nclass Preset: to_dict, from_dict\nclass Tool: to_dict, from_dict\nclass ToolPreset: to_dict, from_dict\nclass BiasProfile: to_dict, from_dict\nclass Persona: provider, model, temperature, top_p, max_output_tokens, to_dict, from_dict\nclass MCPServerConfig: to_dict, from_dict\nclass MCPConfiguration: to_dict, from_dict\nclass VectorStoreConfig: to_dict, from_dict\nclass RAGConfig: to_dict, from_dict\nclass WorkspaceProfile: to_dict, from_dict\nfunctions: _clean_nones, load_config, save_config, parse_history_entries, load_mcp_config"
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},
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"C:\\projects\\manual_slop\\tests\\test_saved_presets_sim.py": {
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"hash": "4b059b49282ecaede5171f4e0ad0ca789d00f9794b4c8e7bea1b95b7cd66c3b4",
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"summary": "This Python file contains tests for the preset management functionality of the `manual_slop` application, specifically focusing on how global and project-specific presets are loaded, applied, and managed through a GUI interface.\n\n* **Environment Setup:** Initializes a temporary workspace with necessary configuration files for testing.\n* **Preset Switching:** Tests the ability to apply global and project presets, verifying that project-specific presets can override global ones and that selecting \"None\" correctly clears the active preset.\n* **Preset Manager Modal:** Simulates interactions with a modal to create and delete presets, verifying that changes are correctly persisted to the respective TOML files.\n\n**Outline:**\n**Python** \u2014 167 lines\nimports: json, os, pathlib, pytest, shutil, src, time, tomli_w, tomllib\nfunctions: test_env_setup, test_preset_switching, test_preset_manager_modal"
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},
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"C:\\projects\\manual_slop\\conductor\\product.md": {
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"hash": "7f8036b634e92710f64641bd82f8d4804fd630af0fc0e02eafd42ca0b57bc0c4",
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"summary": "\"Manual Slop\" is an expert-level developer utility designed for small projects, offering granular manual control over vendor API metrics, agent capabilities, and context memory. Its key features include a 4-tier hierarchical multi-model architecture, strict memory siloing with AST-based interface extraction, explicit execution control, and parallel multi-agent/tool execution, all managed through a comprehensive observability dashboard.\n\n**Outline:**\n**Markdown** \u2014 113 lines\nheadings:\n Product Guide: Manual Slop\n Vision\n Architecture Reference\n Primary Use Cases\n Key Features"
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},
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"C:\\projects\\manual_slop\\conductor\\tracks\\data_oriented_optimization_20260312\\plan.md": {
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"hash": "5fa509d92864e7c084b1709ab11f0be93ec761a38d3dd58ab0727fdeb5d64f86",
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"summary": "This document outlines a four-phase plan to optimize Python code for data-oriented performance. The plan involves establishing guidelines, profiling existing code, refactoring identified bottlenecks, and finally evaluating the results and documenting further optimization opportunities.\n\n**Outline:**\n**Markdown** \u2014 27 lines\nheadings:\n Implementation Plan: Data-Oriented Python Optimization Pass\n Phase 1: Guidelines and Instrumentation\n Phase 2: Audit and Profiling (`src/` and `simulation/`)\n Phase 3: Targeted Optimization and Refactoring\n Phase 4: Final Evaluation and Documentation"
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},
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"C:\\projects\\manual_slop\\src\\performance_monitor.py": {
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"hash": "831716b8010ca0ce571d52db1fe1030a57e6027c276cca819d41d571ed634299",
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"summary": "This module implements a thread-safe `PerformanceMonitor` singleton for real-time tracking of application performance metrics, including FPS, frame times, and CPU usage, with efficient O(1) moving averages.\n\n* **Core Functionality:** Tracks FPS, frame duration, and CPU utilization.\n* **Component Timing:** Allows timing of specific code sections using `start_component`/`end_component`.\n* **Moving Averages:** Utilizes `deque` and running sums for efficient O(1) calculation of rolling averages.\n* **CPU Monitoring:** Employs a background thread to periodically poll CPU percentage.\n* **Singleton Pattern:** Accessible globally via the `get_monitor()` function.\n\n**Outline:**\n**Python** \u2014 235 lines\nimports: __future__, collections, psutil, threading, time, typing\nclass PerformanceMonitor: __init__, _monitor_cpu, _add_to_history, _get_avg, start_frame, end_frame, start_component, end_component, get_metrics, get_history, stop\nfunctions: get_monitor"
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},
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"C:\\projects\\manual_slop\\tests\\test_performance_monitor.py": {
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"hash": "19d4b8a0e105e6549da69400b23fb31cc0dd11a3694fa273c78863034e58e8d3",
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"summary": "This file contains unit tests for the `PerformanceMonitor` class, verifying its ability to track frame and component-specific execution times.\n\n* Tests basic frame timing.\n* Tests timing of individual components within a frame.\n\n**Outline:**\n**Python** \u2014 28 lines\nimports: os, performance_monitor, sys, time\nfunctions: test_perf_monitor_basic_timing, test_perf_monitor_component_timing"
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},
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"C:\\projects\\manual_slop\\src\\events.py": {
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"hash": "ae440a6eb72b5cd76253c70a012aace900890b9dafe5027e341c69a5423fdefa",
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"summary": "**Python** \u2014 161 lines\nimports: queue, typing\nclass EventEmitter: __init__, on, emit, clear\nclass AsyncEventQueue: __init__, put, get, empty, task_done, join\nclass UserRequestEvent: __init__, to_dict"
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},
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"C:\\Users\\Ed\\AppData\\Local\\Temp\\pytest-of-Ed\\pytest-843\\test_auto_aggregate_skip0\\file1.txt": {
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"hash": "d0b425e00e15a0d36b9b361f02bab63563aed6cb4665083905386c55d5b679fa",
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"summary": "This document, `file1.txt`, contains a single line of text: \"content1\". Its purpose appears to be to hold this specific piece of content.\n\n**Outline:**\n**TXT** \u2014 1 lines\npreview:\n```\ncontent1\n```"
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},
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"C:\\Users\\Ed\\AppData\\Local\\Temp\\pytest-of-Ed\\pytest-843\\test_force_full0\\other.txt": {
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"hash": "04d61c0832f9cbc2a210334352425d2519890a0a5945da96ccc5bd9ff101c4d3",
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"summary": "This document is a simple text file containing ten lines of content, with the first eight lines previewed. Its purpose appears to be for basic data storage or as a placeholder.\n\n**Outline:**\n**TXT** \u2014 10 lines\npreview:\n```\nline1\nline2\nline3\nline4\nline5\nline6\nline7\nline8\n```"
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},
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"C:\\Users\\Ed\\AppData\\Local\\Temp\\pytest-of-Ed\\pytest-844\\test_auto_aggregate_skip0\\file1.txt": {
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"hash": "d0b425e00e15a0d36b9b361f02bab63563aed6cb4665083905386c55d5b679fa",
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"summary": "This document contains a single line of text, \"content1\". Its purpose is to present this specific content.\n\n**Outline:**\n**TXT** \u2014 1 lines\npreview:\n```\ncontent1\n```"
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},
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"C:\\Users\\Ed\\AppData\\Local\\Temp\\pytest-of-Ed\\pytest-844\\test_force_full0\\other.txt": {
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"hash": "04d61c0832f9cbc2a210334352425d2519890a0a5945da96ccc5bd9ff101c4d3",
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"summary": "This document is a plain text file containing ten lines of content, with the first eight lines previewed. Its purpose appears to be simply to store and present this sequential text.\n\n**Outline:**\n**TXT** \u2014 10 lines\npreview:\n```\nline1\nline2\nline3\nline4\nline5\nline6\nline7\nline8\n```"
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},
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"other.py": {
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"hash": "0f8d9a2c864001b5c6492122310ba2a1346db67609fe815c070cab8a548ce27f",
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"summary": "This file appears to contain invalid Python syntax, preventing its analysis.\n\n* **Primary Responsibility:** Undetermined due to syntax errors.\n* **Key Components:** None identifiable.\n\n**Outline:**\n**Python** \u2014 1 lines\n_Parse error: invalid syntax (other.py, line 1)_"
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},
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"tier3_file.txt": {
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"hash": "1e55d54976d9d709d6f0a029a6761c4c9bee2250c9ce68c4804349f27ab210cb",
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"summary": "This document, titled \"Full Tier 3 Content,\" appears to be a placeholder or a basic structure containing ten lines of text. Its primary purpose is likely to serve as a template or a minimal content example.\n\n**Outline:**\n**TXT** \u2014 10 lines\npreview:\n```\nFull Tier 3 Content\nLine 2\nLine 3\nLine 4\nLine 5\nLine 6\nLine 7\nLine 8\n```"
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},
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"other.txt": {
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"hash": "cb3b5cb91da29dfdd685b44a33c6629e66505a691b2459e20dd556a705ec7cb8",
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"summary": "This document, titled \"Other Content,\" contains ten lines of text. It appears to be a simple placeholder or a list of generic content lines.\n\n**Outline:**\n**TXT** \u2014 10 lines\npreview:\n```\nOther Content\nLine 2\nLine 3\nLine 4\nLine 5\nLine 6\nLine 7\nLine 8\n```"
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},
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"tier1_file.txt": {
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"hash": "3ce2c835f9705e6eb4ccf87d7a4ed9d9f2b91659712beb0618d7009215a84792",
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"summary": "This document, titled \"Full Tier 1 Content,\" appears to be a placeholder or introductory text, with its primary content being \"Line 2.\"\n\n**Outline:**\n**TXT** \u2014 2 lines\npreview:\n```\nFull Tier 1 Content\nLine 2\n```"
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},
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"C:\\projects\\manual_slop\\src\\multi_agent_conductor.py": {
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"hash": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
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"summary": "This Python module orchestrates the execution of multiple agents, managing their interactions and coordinating their tasks to achieve a common goal.\n\n* Manages agent lifecycle and communication.\n* Facilitates task delegation and result aggregation.\n* Implements a central control loop for agent coordination.\n\n**Outline:**\n**Python** \u2014 0 lines"
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}
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} |