--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

[DISCUSSION HISTORY]

## Discussion History

### Discussion Excerpt 1

@2026-03-06T19:34:06
System:
[PERFORMANCE ALERT] Frame time high: 430.6ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 2

@2026-03-06T19:34:41
System:
[PERFORMANCE ALERT] Frame time high: 58.2ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 3

@2026-03-06T19:38:51
System:
[PERFORMANCE ALERT] Frame time high: 409.3ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 4

@2026-03-06T19:40:43
System:
[PERFORMANCE ALERT] Frame time high: 64.5ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 5

@2026-03-06T19:41:13
System:
[PERFORMANCE ALERT] CPU usage high: 94.0%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 6

@2026-03-06T19:41:59
System:
[PERFORMANCE ALERT] Frame time high: 440.6ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 7

@2026-03-06T19:43:42
System:
[PERFORMANCE ALERT] Frame time high: 58.3ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 8

@2026-03-06T19:45:01
System:
[PERFORMANCE ALERT] Frame time high: 435.0ms. Please consider optimizing recent changes or reducing load.

---

test
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--resume', 'mock-session-default', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

nice job
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

[DISCUSSION HISTORY]

## Discussion History

### Discussion Excerpt 1

@2026-03-06T19:34:06
System:
[PERFORMANCE ALERT] Frame time high: 430.6ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 2

@2026-03-06T19:34:41
System:
[PERFORMANCE ALERT] Frame time high: 58.2ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 3

@2026-03-06T19:38:51
System:
[PERFORMANCE ALERT] Frame time high: 409.3ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 4

@2026-03-06T19:40:43
System:
[PERFORMANCE ALERT] Frame time high: 64.5ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 5

@2026-03-06T19:41:13
System:
[PERFORMANCE ALERT] CPU usage high: 94.0%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 6

@2026-03-06T19:41:59
System:
[PERFORMANCE ALERT] Frame time high: 440.6ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 7

@2026-03-06T19:43:42
System:
[PERFORMANCE ALERT] Frame time high: 58.3ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 8

@2026-03-06T19:45:01
System:
[PERFORMANCE ALERT] Frame time high: 435.0ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 9

@2026-03-06T19:45:31
System:
[PERFORMANCE ALERT] CPU usage high: 114.1%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 10

@2026-03-06T19:52:00
System:
[PERFORMANCE ALERT] Frame time high: 538.9ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 11

@2026-03-06T19:52:08
System:
[PERFORMANCE ALERT] Frame time high: 419.0ms. Please consider optimizing recent changes or reducing load.

---

test
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

[DISCUSSION HISTORY]

## Discussion History

### Discussion Excerpt 1

@2026-03-06T19:34:06
System:
[PERFORMANCE ALERT] Frame time high: 430.6ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 2

@2026-03-06T19:34:41
System:
[PERFORMANCE ALERT] Frame time high: 58.2ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 3

@2026-03-06T19:38:51
System:
[PERFORMANCE ALERT] Frame time high: 409.3ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 4

@2026-03-06T19:40:43
System:
[PERFORMANCE ALERT] Frame time high: 64.5ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 5

@2026-03-06T19:41:13
System:
[PERFORMANCE ALERT] CPU usage high: 94.0%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 6

@2026-03-06T19:41:59
System:
[PERFORMANCE ALERT] Frame time high: 440.6ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 7

@2026-03-06T19:43:42
System:
[PERFORMANCE ALERT] Frame time high: 58.3ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 8

@2026-03-06T19:45:01
System:
[PERFORMANCE ALERT] Frame time high: 435.0ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 9

@2026-03-06T19:45:31
System:
[PERFORMANCE ALERT] CPU usage high: 114.1%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 10

@2026-03-06T19:52:00
System:
[PERFORMANCE ALERT] Frame time high: 538.9ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 11

@2026-03-06T19:52:08
System:
[PERFORMANCE ALERT] Frame time high: 419.0ms. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 12

@2026-03-06T19:52:59
System:
[PERFORMANCE ALERT] CPU usage high: 121.9%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 13

@2026-03-06T19:57:28
System:
[PERFORMANCE ALERT] CPU usage high: 92.2%. Please consider optimizing recent changes or reducing load.

---

test
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--resume', 'mock-session-default', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

another test
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--resume', 'mock-session-final', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

hows it going?
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--resume', 'mock-session-final', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

hows the day gone

------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--resume', 'mock-session-final', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

tell me the day's date
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

[DISCUSSION HISTORY]

## Discussion History

### Discussion Excerpt 1

@2026-03-06T20:08:29
System:
[PERFORMANCE ALERT] CPU usage high: 93.0%. Please consider optimizing recent changes or reducing load.

---

testing 123
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

testing gemini
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--resume', 'mock-session-default', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

1+ 1?
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--resume', 'mock-session-final', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

1+ 1?
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

<context>

</context>

[DISCUSSION HISTORY]

## Discussion History

### Discussion Excerpt 1

@2026-03-06T20:40:49
System:
[PERFORMANCE ALERT] CPU usage high: 91.0%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 2

@2026-03-06T20:47:26
System:
[PERFORMANCE ALERT] CPU usage high: 95.5%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 3

@2026-03-06T20:48:08
System:
[PERFORMANCE ALERT] CPU usage high: 93.4%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 4

@2026-03-06T20:49:49
System:
[PERFORMANCE ALERT] CPU usage high: 85.4%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 5

@2026-03-06T20:51:47
System:
[PERFORMANCE ALERT] CPU usage high: 86.7%. Please consider optimizing recent changes or reducing load.

---

### Discussion Excerpt 6

@2026-03-06T20:53:31
System:
[PERFORMANCE ALERT] CPU usage high: 96.6%. Please consider optimizing recent changes or reducing load.

---

testing gemini cli
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

[USER SYSTEM PROMPT]

You are the Tier 1 Orchestrator (Product Manager) for the Manual Slop project.
Your role is high-level strategic planning, architecture enforcement, and cross-module delegation.
You operate strictly on metadata, summaries, and executive-level directives.
NEVER request or attempt to read raw implementation code unless specifically provided in a Macro-Diff.
Maintain a "Godot ECS Flat List format" (JSON array of objects) for structural outputs.

PATH: Epic Initialization (Project Planning)
GOAL: Break down a massive feature request into discrete Implementation Tracks.

CONSTRAINTS:
- IGNORE all source code, AST skeletons, and previous micro-task histories.
- FOCUS ONLY on the Repository Map and Project Meta-State.

OUTPUT REQUIREMENT:
Return a JSON array of 'Tracks'. Each track object must follow the Godot ECS Flat List format:
[
  {
    "id": "track_unique_id",
    "type": "Track",
    "module": "target_module_name",
    "persona": "required_tech_lead_persona",
    "severity": "Low|Medium|High",
    "goal": "Descriptive goal",
    "acceptance_criteria": ["criteria_1", "criteria_2"]
  },
  ...
]


<context>

</context>

### USER REQUEST:
Add timestamps

### REPOSITORY MAP:


### TRACK HISTORY:
Track: api_hooks_verification_20260223
Status: new
Overview: This track focuses on integrating the existing, previously implemented API hooks (from track `test_hooks_20260223`) into the Conductor workflow. The primary goal is to automate the verification steps within the "Phase Completion Verification and Checkpointing Protocol", reducing the need for manual user intervention and enabling a more streamlined, automated development process.
---
Track: api_metrics_20260223
Status: new
Overview: This track aims to optimize token efficiency and transparency by reviewing and improving how vendor APIs (Gemini and Anthropic) handle conservative context pruning. The primary focus is on extracting, plotting, and exposing deep metrics to the GUI so developers can intuit how close they are to API limits (e.g., token caps, cache counts, history bleed).
---
Track: api_vendor_alignment_20260223
Status: new
Overview: This track involves a comprehensive audit of the "Manual Slop" codebase to ensure that the integration with Google Gemini (`google-genai`) and Anthropic Claude (`anthropic`) SDKs aligns perfectly with their latest official documentation and best practices. The goal is to identify discrepancies, performance bottlenecks, or deprecated patterns and implement the necessary fixes.
---
Track: architecture_boundary_hardening_20260302
Status: new
Overview: The `manual_slop` project sandbox provides AI meta-tooling (`mma_exec.py`, `tool_call.py`) to orchestrate its own development. When AI agents added advanced AST tools (like `set_file_slice`) to `mcp_client.py` for meta-tooling, they failed to fully integrate them into the application's GUI, config, or HITL (Human-In-The-Loop) safety models. Additionally, meta-tooling scripts are bleeding tokens and rely on non-portable hardcoded machine paths, while the internal application's state machine can deadlock.
---
Track: cache_analytics_20260306
Status: planned
Overview: Gemini cache hit/miss visualization, memory usage, TTL status display. Uses existing `ai_client.get_gemini_cache_stats()` which is implemented but has no GUI representation.
---
Track: codebase_migration_20260302
Status: new
Overview: This track focuses on restructuring the codebase to alleviate clutter by moving the main implementation files from the project root into a dedicated `src/` directory. Additionally, files that are completely unused by the current implementation will be automatically identified and removed. A new clean entry point (`sloppy.py`) will be created in the root directory.
---
Track: comprehensive_gui_ux_20260228
Status: completed
Overview: This track enhances the existing MMA orchestration GUI from its current functional-but-minimal state to a production-quality control surface. The existing implementation already has a working Track Browser, DAG tree visualizer, epic planning flow, approval dialogs, and token usage table. This track focuses on the **gaps**: dedicated tier stream panels, DAG editing, track-scoped discussions, conductor lifecycle GUI forms, cost tracking, and visual polish.
---
Track: conductor_workflow_improvements_20260302
Status: new
Overview: Recent Tier 2 track implementations have resulted in feature bleed, redundant code, unread state variables, and degradation of TDD discipline (e.g., zero-assertion tests).
This track updates the Conductor documentation (`workflow.md`) and the Gemini skills for Tiers 2 and 3 to hard-enforce TDD, prevent hallucinated "mock" implementations, and enforce strict codebase auditing before writing code.
---
Track: consolidate_cruft_and_log_taxonomy_20260228
Status: unknown
Overview: This track focuses on cleaning up the project root by consolidating temporary and test-related files into a dedicated directory and establishing a structured taxonomy for session logs. This will improve project organization and make manual file exploration easier before a dedicated GUI log viewer is implemented.
---
Track: context_management_20260223
Status: new
Overview: This track implements UI improvements and structural changes to Manual Slop to provide explicit visualization of context memory usage and token consumption, fulfilling the "Expert systems level utility" and "Full control" product goals.
---
Track: context_token_viz_20260301
Status: new
Overview: product.md lists "Context & Memory Management" as primary use case #2: "Better visualization and management of token usage and context memory, allowing developers to optimize prompt limits manually." The backend already computes everything needed via `ai_client.get_history_bleed_stats()` (ai_client.py:1657-1796, 140 lines). This track builds the UI to expose it.
---
Track: cost_token_analytics_20260306
Status: planned
Overview: # Implementation Plan: Cost & Token Analytics Panel (cost_token_analytics_20260306)

> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)

## Phase 1: Foundat...
---
Track: deepseek_support_20260225
Status: new
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.
---
Track: deep_ast_context_pruning_20260306
Status: planned
Overview: Use tree_sitter to parse target file AST and inject condensed skeletons into worker prompts. Currently workers receive full file context; this track reduces token burn by injecting only relevant function/method signatures.
---
Track: documentation_refresh_20260224
Status: new
Overview: This track implements a high-density, expert-level documentation suite for the Manual Slop project. The documentation style is strictly modeled after the **pedagogical and narrative standards** of `gencpp` and `VEFontCache-Odin`. It moves beyond simple "User Guides" to provide a **"USA Graphics Company"** architectural reference: high information density, tactical technical transparency, and a narrative intent that guides a developer from high-level philosophy to low-level implementation.
---
Track: enhanced_context_control_20260307
Status: planned
Overview: Give developers granular control over how files are included in the AI context and provide visibility into the active Gemini cache state. This involves moving away from a simple list of files to a structured format with per-file flags (`auto_aggregate`, `force_full`), revamping the UI to display this state, and updating the context builders and API clients to respect and expose these details.
---
Track: event_driven_metrics_20260223
Status: new
Overview: Refactor the API metrics update mechanism to be event-driven. Currently, the UI likely polls or recalculates metrics on every frame. This track will implement a signal/event system where `ai_client.py` broadcasts updates only when significant API activities (requests, responses, tool calls, or stream chunks) occur.
---
Track: feature_bleed_cleanup_20260302
Status: new
Overview: Multiple tracks added code to `gui_2.py` without removing the old versions, leaving
dead duplicate methods, conflicting menu bar designs, and redundant state initializations.
This track removes confirmed dead code, resolves the two-menubar conflict, and cleans
up the token budget layout regression β restoring a consistent, non-contradictory design state.
---
Track: gemini_cli_headless_20260224
Status: new
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.
---
Track: gemini_cli_parity_20260225
Status: new
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.
---
Track: gui2_feature_parity_20260223
Status: new
Overview: # 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 i...
---
Track: gui2_parity_20260224
Status: new
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`.
---
Track: gui_decoupling_controller_20260302
Status: new
Overview: `gui_2.py` currently operates as a Monolithic God Object (3,500+ lines). It violates the Data-Oriented Design heuristic by owning complex business logic, orchestrator hooks, and markdown file building. This track extracts the core state machine and lifecycle into a headless `app_controller.py`, turning the GUI into a pure immediate-mode view.
---
Track: gui_layout_refinement_20260223
Status: new
Overview: This track focuses on a holistic review and reorganization of the Manual Slop GUI. The goal is to ensure that AI tunings, diagnostic features, context management, and discussion history are logically placed to support an expert-level "Multi-Viewport" workflow. We will strengthen the "Arcade Aesthetics" and "Tactile Density" values while ensuring the layout remains intuitive for power users.
---
Track: gui_performance_20260223
Status: new
Overview: This track focuses on identifying and resolving severe frametime performance issues in the Manual Slop GUI. Current observations indicate massive frametime bloat even on idle startup, with performance significantly regressing (target 60 FPS / <16.6ms) since commit `8aa70e287fbf93e669276f9757965d5a56e89b10`.
---
Track: gui_performance_profiling_20260307
Status: unknown
Overview: Implement fine-grained performance profiling within the main ImGui rendering loop (`gui_2.py`) to ensure adherence to data-oriented and immediate mode heuristics. This track will provide visual diagnostics for high-overhead UI components, allowing developers to monitor and optimize render frame times.
---
Track: gui_sim_extension_20260224
Status: new
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.
---
Track: history_segregation_20260224
Status: new
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.
---
Track: kill_abort_workers_20260306
Status: planned
Overview: Add ability to kill/abort a running Tier 3 worker mid-execution. Currently workers run to completion; add cancel button with forced termination option.
---
Track: live_gui_testing_20260223
Status: new
Overview: Update the testing suite to ensure all tests (especially GUI-related and integration tests) communicate with a live running instance of `gui.py` started with the `--enable-test-hooks` argument. This ensures that tests can verify the actual application state and metrics via the built-in API hooks.
---
Track: live_ux_test_20260223
Status: new
Overview: This track implements a robust, "human-like" interaction test suite for Manual Slop. The suite will simulate a real user's workflowβfrom project creation to complex AI discussions and history managementβusing the application's API hooks. It aims to verify the "Integrated Workspace" functionality, tool execution, and history persistence without requiring manual human input, while remaining slow enough for visual audit.
---
Track: logging_refactor_20260226
Status: new
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.
---
Track: manual_block_control_20260306
Status: planned
Overview: Allow user to manually block or unblock tickets with custom reasons. Currently blocked tickets rely solely on dependency resolution; add manual override capability.
---
Track: manual_skeleton_injection_20260306
Status: planned
Overview: Add UI controls to manually inject file skeletons into discussions. Allow user to preview skeleton content before sending to AI, with option to toggle between skeleton and full file.
---
Track: manual_slop_headless_20260225
Status: new
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.
---
Track: minimax_provider_20260306
Status: unknown
Overview: Add MiniMax as a new AI provider to Manual Slop. MiniMax provides high-performance text generation models (M2.5, M2.1, M2) with massive context windows (200k+ tokens) and competitive pricing.
---
Track: mma_agent_focus_ux_20260302
Status: new
Overview: All MMA observability panels (comms history, tool calls, discussion) display
global/session-scoped data. When 4 tiers are running concurrently, their traffic
is indistinguishable. This track adds a `source_tier` field to every comms and
tool log entry at the point of emission, then adds a "Focus Agent" selector that
filters the Operations Hub panels to show only one tier's traffic at a time.

**Depends on:** `feature_bleed_cleanup_20260302` (Phase 1 removes the dead comms
panel duplicate; this track extends the live panel at gui_2.py:~3400).
---
Track: MMA Core Engine Implementation
Status: planning
Overview: # Specification: MMA Core Engine Implementation

## 1. Overview
This track consolidates the implementation of the 4-Tier Hierarchical Multi-Model Architecture into the `manual_slop` codebase. The arch...
---
Track: MMA Dashboard Visualization Overhaul
Status: planned
Overview: Make the invisible backend operations visible and interactive. The current GUI is too barebones to effectively manage a multi-agent system. This track overhauls the MMA Dashboard to provide real-time insights into tracks, task dependencies, and agent streams.
---
Track: MMA Data Architecture & DAG Engine
Status: planned
Overview: Restructure how `manual_slop` stores and executes work. The current implementation relies on global state and linear execution, which does not support the complexity of multi-agent, task-based workflows. This track establishes a robust, data-oriented foundation using track-scoped state and a native Python Directed Acyclic Graph (DAG) engine.
---
Track: mma_formalization_20260225
Status: new
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.
---
Track: mma_implementation_20260224
Status: new
Overview: # Specification: 4-Tier Architecture Implementation & Conductor Self-Improvement

## 1. Overview
This track encompasses two major phases. Phase 1 focuses on designing a comprehensive, step-by-step imp...
---
Track: mma_multiworker_viz_20260306
Status: planned
Overview: Split-view GUI for parallel worker streams per tier. Visualize multiple concurrent workers with individual status, output tabs, and resource usage. Enable kill/restart per worker.
---
Track: MMA Orchestrator Integration
Status: in-progress
Overview: Implement the full hierarchical orchestration loop, connecting Tier 1 (PM) strategic planning with Tier 2 (Tech Lead) tactical ticket generation. This track will enable the GUI to autonomously break down high-level user 'Epics' into actionable tracks and tickets, and manage their execution through the multi-agent system.
---
Track: mma_pipeline_fix_20260301
Status: new
Overview: The MMA pipeline has a verified code path from `run_worker_lifecycle` β `_queue_put("response", ...)` β `_process_event_queue` β `_pending_gui_tasks("handle_ai_response")` β `mma_streams[stream_id] = text`. However, the robust_live_simulation track's session compression (2026-02-28) documented that Tier 3 worker output never appears in `mma_streams` during actual GUI operation. The simulation only ever sees `'Tier 1'` in `mma_streams` keys.

This track diagnoses and fixes the pipeline break, then verifies end-to-end that worker output flows from `ai_client.send()` through to the GUI's `mma_streams` dict.
---
Track: mma_utilization_refinement_20260226
Status: new
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.
---
Track: mma_verification_20260225
Status: new
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.
---
Track: mma_verification_mock
Status: new
Overview: This is a mock track designed to verify the full Tier 2 -> Tier 3 -> Tier 4 delegation flow within the Conductor framework.
---
Track: native_orchestrator_20260306
Status: planned
Overview: Absorb `mma_exec.py` functionality into core application. Manual Slop natively reads/writes plan.md, manages metadata.json, and orchestrates MMA tiers in pure Python without external CLI subprocess calls.
---
Track: on_demand_def_lookup_20260306
Status: planned
Overview: Add ability for agent to request specific class/function definitions during discussion. Parse @symbol syntax to trigger lookup and display inline in the discussion.
---
Track: per_ticket_model_20260306
Status: planned
Overview: Allow user to manually select which model to use for a specific ticket, overriding the default tier model. Useful for forcing smarter model on hard tickets.
---
Track: pipeline_pause_resume_20260306
Status: planned
Overview: Add global pause/resume for entire DAG execution pipeline. Allow user to freeze all worker activity and resume later without losing state.
---
Track: python_style_refactor_20260227
Status: unknown
Overview: # Specification: AI-Optimized Python Style Refactor

## 1. Overview
Refactor the Python codebase to a "Single-Space, Ultra-Compact" style specifically designed to minimize token consumption for AI age...
---
Track: Robust Live Simulation Verification
Status: planned
Overview: Establish a robust, visual simulation framework to prevent regressions in the complex GUI and asynchronous orchestration layers. This track replaces manual human verification with an automated script that clicks through the GUI and verifies the rendered state.
---
Track: session_insights_20260306
Status: planned
Overview: Token usage over time, cost projections, session summary with efficiency scores. Visualize session_logger data.
---
Track: simulation_hardening_20260301
Status: new
Overview: The `robust_live_simulation_verification` track is marked complete but its session compression documents three unresolved issues: (1) brittle mock that triggers the wrong approval popup, (2) popup state desynchronization after "Accept" clicks, (3) Tier 3 output never appearing in `mma_streams` (fixed by `mma_pipeline_fix` track). This track stabilizes the simulation framework so it reliably passes end-to-end.
---
Track: strict_execution_queue_completed_20260306
Status: completed
Overview: No overview available.
---
Track: strict_static_analysis_and_typing_20260302
Status: new
Overview: The codebase currently suffers from massive type-safety debt (512+ `mypy` errors across 64 files) and lingering `ruff` violations. This track will harden the foundation by resolving all violations, enforcing strict typing (especially in `gui_2.py` and `api_hook_client.py`), and integrating pre-commit checks. This is a prerequisite for safe AI-driven refactoring.
---
Track: tech_debt_and_test_cleanup_20260302
Status: new
Overview: Due to rapid iterative development and feature bleed across multiple Tier 2-led tracks, significant tech debt has accumulated in both the testing suite and `gui_2.py`.
This track will clean up test fixtures, enforce test assertion integrity, and remove dead codebase remnants.
---
Track: test_architecture_integrity_audit_20260304
Status: unknown
Overview: Comprehensive audit of testing infrastructure and simulation framework to identify false positive risks, coverage gaps, and simulation fidelity issues. This analysis was triggered by a request to review how tests and simulations are setup, whether tests can report passing grades when they fail, and if simulations are rigorous enough or are just rough emulators.
---
Track: test_curation_20260225
Status: new
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.
---
Track: test_hooks_20260223
Status: new
Overview: This track introduces a comprehensive suite of API hooks designed specifically for the Gemini CLI and the Conductor framework. These hooks will allow automated agents to manipulate and test the internal state of the application without requiring manual GUI interaction, enabling automated test-driven development and track progression validation.
---
Track: Test Integrity Audit & Intent Documentation
Status: in_progress
Overview: Audit and fix tests that have been "simplified" or "dumbed down" by AI agents, restoring their original verification intent through explicit documentation comments. This track addresses the growing problem of AI agents "completing" tasks by weakening test assertions rather than implementing proper functionality.
---
Track: test_regression_verification_20260307
Status: unknown
Overview: Verify that all existing tests pass with 0 regressions after recent track implementations (Kill/Abort, Block/Unblock, Pause/Resume, Per-Ticket Model Override).
---
Track: test_stabilization_20260302
Status: new
Overview: The goal of this track is to stabilize and unify the project's test suite. This involves resolving pervasive `asyncio` lifecycle errors, consolidating redundant testing paradigms (specifically manual GUI subprocesses), ensuring artifact isolation in `./tests/artifacts/`, implementing functional assertions for currently mocked-out tests, and updating documentation to reflect the finalized verification framework.
---
Track: ticket_queue_mgmt_20260306
Status: planned
Overview: Allow user to manually reorder, prioritize, or requeue tickets in the DAG. Add drag-drop reordering, priority tags, and bulk selection for execute/skip/block operations.
---
Track: tier4_auto_patching_20260306
Status: planned
Overview: Elevate Tier 4 from log summarizer to auto-patcher. When verification tests fail, Tier 4 generates a unified diff patch. GUI displays side-by-side diff; user clicks Apply Patch to resume pipeline.
---
Track: Tiered Context Scoping & HITL Approval
Status: planned
Overview: Provide the user with absolute visual control over what the AI sees at every level of the hierarchy. Currently, the system builds a single massive context blob. This track introduces context subsetting based on the target tier and implements a Human-in-the-Loop (HITL) approval gate before any Tier 3/4 worker is spawned.
---
Track: tool_usage_analytics_20260306
Status: planned
Overview: Analytics panel showing most-used tools, average execution time, and failure rates. Uses existing tool execution data from ai_client.
---
Track: track_progress_viz_20260306
Status: planned
Overview: Progress bars and percentage completion for active tracks and tickets. Better visualization of DAG execution state.
---
Track: true_parallel_worker_execution_20260306
Status: planned
Overview: Add worker pool management and configurable concurrency limits to the DAG engine. Currently workers execute in parallel per tick but with no limits or tracking; this track adds max_workers configuration, worker tracking, and proper pool management.
---
Track: ui_performance_20260223
Status: new
Overview: This track aims to resolve subpar UI performance (currently perceived below 60 FPS) by implementing a robust performance monitoring system. This system will collect high-resolution telemetry (Frame Time, Input Lag, Thread Usage) and expose it to both the user (via a Diagnostics Panel) and the AI (via API hooks). This ensures that performance degradation is caught early during development and testing.
---
Track: visual_dag_ticket_editing_20260306
Status: planned
Overview: Replace linear ticket list with interactive node graph using ImGui Bundle node editor. Users can visually drag dependency lines, split nodes, or delete tasks before execution.
---
Track: caching_optimization_20260308
Status: new
Overview: This track aims to verify and optimize the caching strategies across all supported AI providers (Gemini, Anthropic, DeepSeek, MiniMax, and OpenAI). The goal is to minimize token consumption and latency by ensuring that static and recurring context (system prompts, tool definitions, project documents, and conversation history) are effectively cached using each provider's specific mechanisms.
---
Track: codebase_audit_20260308
Status: new
Overview: The objective of this track is to audit the `./src` and `./simulation` directories to improve human readability and maintainability. The codebase has matured, and it is necessary to identify and address redundant code paths and state tracking, add missing docstrings to critical paths, and organize declarations/definitions within files.
---
Track: conductor_path_configurable_20260306
Status: unknown
Overview: Eliminate all hardcoded paths in the application. Make directory paths configurable via `config.toml` or environment variables, allowing the running app to use different directories from development setup. This is **Phase 0 - Critical Infrastructure** that must be completed before other Phase 3 tracks.
---
Track: external_editor_integration_20260308
Status: new
Overview: This feature adds the ability to open files modified by AI agents in external text editors (such as VSCode or 10xNotepad) directly from the tool approval popup. This allows users to leverage their preferred editor's native diffing and editing capabilities before confirming an agent's changes.
---
Track: external_mcp_support_20260308
Status: new
Overview: This feature adds support for integrating external Model Context Protocol (MCP) servers into Manual Slop. This allows agents to utilize tools from a wide ecosystem of MCP servers (like those for databases, APIs, or specialized utilities) alongside the application's native tools.
---
Track: Bootstrap gencpp Python Bindings Project
Status: pending
Overview: Create a new standalone Python project to build CFFI bindings for gencpp (C/C++ staged metaprogramming library). This will eventually provide richer C++ AST understanding than tree-sitter (full type information, operators, specifiers) but is a longer-term effort. This track bootstraps the project structure and initial bindings.
---
Track: GUI Path Configuration in Context Hub
Status: pending
Overview: Add path configuration UI to the Context Hub in the GUI. Allow users to view and edit configurable paths (conductor, logs, scripts) directly from the application without manually editing config.toml or environment variables.
---
Track: hook_api_expansion_20260308
Status: new
Overview: This track aims to transform the Manual Slop Hook API into a comprehensive control plane for headless use. It focuses on exposing all relevant internal states (worker traces, AST metadata, financial metrics, concurrency telemetry) and providing granular control over the application's lifecycle, MMA pipeline, and context management. Additionally, it introduces a WebSocket-based streaming interface for real-time event delivery.
---
Track: log_session_overhaul_20260308
Status: new
Overview: This track focuses on centralizing log management, improving the reliability and scope of session restoration, and optimizing log storage by offloading large data blobs (scripts and tool outputs) to external files. It also aims to "clean" the discussion history by moving transient system warnings to a dedicated diagnostic log.
---
Track: manual_ux_validation_20260302
Status: new
Overview: This track is an unusual, highly interactive human-in-the-loop review session. The user will act as the primary QA and Designer, manually using the GUI and observing it during slow-interval simulation runs. The goal is to aggressively iterate on the "feel" of the application: analyzing blinking animations, structural decisions (Tabs vs. Panels vs. Collapsing Headers), knob/control placements, and the efficacy of popups (including adding auto-close timers).
---
Track: markdown_highlighting_20260308
Status: new
Overview: This track introduces rich text rendering to the Manual Slop GUI by adding support for GitHub-Flavored Markdown (GFM) in message and response views. It also adds syntax highlighting for code blocks and text content when the language context can be cheaply resolved (e.g., via known metadata or file extensions).
---
Track: openai_integration_20260308
Status: new
Overview: This track introduces support for OpenAI as a first-class model provider. It involves implementing a dedicated client in `src/ai_client.py`, updating configuration models, enhancing the GUI for provider selection, and integrating OpenAI into the tiered MMA architecture.
---
Track: Project-Specific Conductor Directory
Status: pending
Overview: Make the conductor directory per-project instead of global. Each project TOML can specify its own `conductor_dir` path, allowing separate track/state management per project. This enables using Manual Slop with multiple independent projects without track/ticket cross-pollution.
---
Track: rag_support_20260308
Status: new
Overview: This track introduces Retrieval-Augmented Generation (RAG) capabilities to Manual Slop. It allows agents to search and retrieve relevant information from large local codebases, project documentation, or external knowledge bases, overcoming context window limitations and reducing hallucination.
---
Track: saved_presets_20260308
Status: new
Overview: This feature introduces the ability to save, manage, and switch between system prompt presets for both global (application-wide) and project-specific contexts. Presets will include not only the system prompt text but also model-specific parameters like temperature and top_p, effectively allowing for "AI Profiles."
---
Track: saved_tool_presets_20260308
Status: new
Overview: This feature adds the ability to create, save, and manage "Tool Presets" for agent roles. These presets define which tools are available to an agent and their respective "auto" vs "ask" approval levels. Tools will be organized into dynamic, TOML-defined categories (e.g., Python, General) and integrated into the global and project-specific AI settings.
---
Track: selectable_ui_text_20260308
Status: new
Overview: This track aims to address UI inconveniences by making critical text across the GUI selectable and copyable. This includes discussion history, communication logs, tool outputs, and key metrics. The goal is to provide a standard "Copy to Clipboard" capability via OS-native selection and shortcuts (Ctrl+C).
---
Track: tool_bias_tuning_20260308
Status: new
Overview: This track introduces a mechanism to influence AI agent tool selection by implementing a weighting and scoring system at the orchestration layer. Since model APIs do not natively support tool priority, this feature uses semantic nudging (tags in tool descriptions) and explicit system instructions to "bias" the agent toward preferred tools and parameters.
---
Track: Tree-Sitter C/C++ MCP Tools
Status: pending
Overview: Add tree-sitter-based C and C++ parsing support to the MCP client, providing skeleton and outline tools for C/C++ codebases. Tools will be prefixed `ts_c_` and `ts_cpp_` to distinguish from existing Python tools and leave namespace open for future gencpp integration.
---
Track: ui_theme_overhaul_20260308
Status: new
Overview: This track aims to modernize the application's appearance by implementing a professional, high-fidelity UI theme using `imgui-bundle`'s native styling and theming capabilities. It focuses on improving typography, visual shapes, and introducing advanced features like custom shaders, multi-viewport support, and user-managed layout presets.
---
Track: zhipu_integration_20260308
Status: new
Overview: This track introduces support for Zhipu AI (z.ai) as a first-class model provider. It involves implementing a dedicated client in `src/ai_client.py` for the GLM series of models, updating configuration models, enhancing the GUI for provider selection, and integrating the provider into the tiered MMA architecture.
---

Please generate the implementation tracks for this request.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

[USER SYSTEM PROMPT]

You are the Tier 1 Orchestrator (Product Manager) for the Manual Slop project.
Your role is high-level strategic planning, architecture enforcement, and cross-module delegation.
You operate strictly on metadata, summaries, and executive-level directives.
NEVER request or attempt to read raw implementation code unless specifically provided in a Macro-Diff.
Maintain a "Godot ECS Flat List format" (JSON array of objects) for structural outputs.

PATH: Epic Initialization (Project Planning)
GOAL: Break down a massive feature request into discrete Implementation Tracks.

CONSTRAINTS:
- IGNORE all source code, AST skeletons, and previous micro-task histories.
- FOCUS ONLY on the Repository Map and Project Meta-State.

OUTPUT REQUIREMENT:
Return a JSON array of 'Tracks'. Each track object must follow the Godot ECS Flat List format:
[
  {
    "id": "track_unique_id",
    "type": "Track",
    "module": "target_module_name",
    "persona": "required_tech_lead_persona",
    "severity": "Low|Medium|High",
    "goal": "Descriptive goal",
    "acceptance_criteria": ["criteria_1", "criteria_2"]
  },
  ...
]


<context>

</context>

### USER REQUEST:
Add timestamps

### REPOSITORY MAP:


### TRACK HISTORY:
Track: api_hooks_verification_20260223
Status: new
Overview: This track focuses on integrating the existing, previously implemented API hooks (from track `test_hooks_20260223`) into the Conductor workflow. The primary goal is to automate the verification steps within the "Phase Completion Verification and Checkpointing Protocol", reducing the need for manual user intervention and enabling a more streamlined, automated development process.
---
Track: api_metrics_20260223
Status: new
Overview: This track aims to optimize token efficiency and transparency by reviewing and improving how vendor APIs (Gemini and Anthropic) handle conservative context pruning. The primary focus is on extracting, plotting, and exposing deep metrics to the GUI so developers can intuit how close they are to API limits (e.g., token caps, cache counts, history bleed).
---
Track: api_vendor_alignment_20260223
Status: new
Overview: This track involves a comprehensive audit of the "Manual Slop" codebase to ensure that the integration with Google Gemini (`google-genai`) and Anthropic Claude (`anthropic`) SDKs aligns perfectly with their latest official documentation and best practices. The goal is to identify discrepancies, performance bottlenecks, or deprecated patterns and implement the necessary fixes.
---
Track: architecture_boundary_hardening_20260302
Status: new
Overview: The `manual_slop` project sandbox provides AI meta-tooling (`mma_exec.py`, `tool_call.py`) to orchestrate its own development. When AI agents added advanced AST tools (like `set_file_slice`) to `mcp_client.py` for meta-tooling, they failed to fully integrate them into the application's GUI, config, or HITL (Human-In-The-Loop) safety models. Additionally, meta-tooling scripts are bleeding tokens and rely on non-portable hardcoded machine paths, while the internal application's state machine can deadlock.
---
Track: cache_analytics_20260306
Status: planned
Overview: Gemini cache hit/miss visualization, memory usage, TTL status display. Uses existing `ai_client.get_gemini_cache_stats()` which is implemented but has no GUI representation.
---
Track: codebase_migration_20260302
Status: new
Overview: This track focuses on restructuring the codebase to alleviate clutter by moving the main implementation files from the project root into a dedicated `src/` directory. Additionally, files that are completely unused by the current implementation will be automatically identified and removed. A new clean entry point (`sloppy.py`) will be created in the root directory.
---
Track: comprehensive_gui_ux_20260228
Status: completed
Overview: This track enhances the existing MMA orchestration GUI from its current functional-but-minimal state to a production-quality control surface. The existing implementation already has a working Track Browser, DAG tree visualizer, epic planning flow, approval dialogs, and token usage table. This track focuses on the **gaps**: dedicated tier stream panels, DAG editing, track-scoped discussions, conductor lifecycle GUI forms, cost tracking, and visual polish.
---
Track: conductor_workflow_improvements_20260302
Status: new
Overview: Recent Tier 2 track implementations have resulted in feature bleed, redundant code, unread state variables, and degradation of TDD discipline (e.g., zero-assertion tests).
This track updates the Conductor documentation (`workflow.md`) and the Gemini skills for Tiers 2 and 3 to hard-enforce TDD, prevent hallucinated "mock" implementations, and enforce strict codebase auditing before writing code.
---
Track: consolidate_cruft_and_log_taxonomy_20260228
Status: unknown
Overview: This track focuses on cleaning up the project root by consolidating temporary and test-related files into a dedicated directory and establishing a structured taxonomy for session logs. This will improve project organization and make manual file exploration easier before a dedicated GUI log viewer is implemented.
---
Track: context_management_20260223
Status: new
Overview: This track implements UI improvements and structural changes to Manual Slop to provide explicit visualization of context memory usage and token consumption, fulfilling the "Expert systems level utility" and "Full control" product goals.
---
Track: context_token_viz_20260301
Status: new
Overview: product.md lists "Context & Memory Management" as primary use case #2: "Better visualization and management of token usage and context memory, allowing developers to optimize prompt limits manually." The backend already computes everything needed via `ai_client.get_history_bleed_stats()` (ai_client.py:1657-1796, 140 lines). This track builds the UI to expose it.
---
Track: cost_token_analytics_20260306
Status: planned
Overview: # Implementation Plan: Cost & Token Analytics Panel (cost_token_analytics_20260306)

> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)

## Phase 1: Foundat...
---
Track: deepseek_support_20260225
Status: new
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.
---
Track: deep_ast_context_pruning_20260306
Status: planned
Overview: Use tree_sitter to parse target file AST and inject condensed skeletons into worker prompts. Currently workers receive full file context; this track reduces token burn by injecting only relevant function/method signatures.
---
Track: documentation_refresh_20260224
Status: new
Overview: This track implements a high-density, expert-level documentation suite for the Manual Slop project. The documentation style is strictly modeled after the **pedagogical and narrative standards** of `gencpp` and `VEFontCache-Odin`. It moves beyond simple "User Guides" to provide a **"USA Graphics Company"** architectural reference: high information density, tactical technical transparency, and a narrative intent that guides a developer from high-level philosophy to low-level implementation.
---
Track: enhanced_context_control_20260307
Status: planned
Overview: Give developers granular control over how files are included in the AI context and provide visibility into the active Gemini cache state. This involves moving away from a simple list of files to a structured format with per-file flags (`auto_aggregate`, `force_full`), revamping the UI to display this state, and updating the context builders and API clients to respect and expose these details.
---
Track: event_driven_metrics_20260223
Status: new
Overview: Refactor the API metrics update mechanism to be event-driven. Currently, the UI likely polls or recalculates metrics on every frame. This track will implement a signal/event system where `ai_client.py` broadcasts updates only when significant API activities (requests, responses, tool calls, or stream chunks) occur.
---
Track: feature_bleed_cleanup_20260302
Status: new
Overview: Multiple tracks added code to `gui_2.py` without removing the old versions, leaving
dead duplicate methods, conflicting menu bar designs, and redundant state initializations.
This track removes confirmed dead code, resolves the two-menubar conflict, and cleans
up the token budget layout regression β restoring a consistent, non-contradictory design state.
---
Track: gemini_cli_headless_20260224
Status: new
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.
---
Track: gemini_cli_parity_20260225
Status: new
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.
---
Track: gui2_feature_parity_20260223
Status: new
Overview: # 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 i...
---
Track: gui2_parity_20260224
Status: new
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`.
---
Track: gui_decoupling_controller_20260302
Status: new
Overview: `gui_2.py` currently operates as a Monolithic God Object (3,500+ lines). It violates the Data-Oriented Design heuristic by owning complex business logic, orchestrator hooks, and markdown file building. This track extracts the core state machine and lifecycle into a headless `app_controller.py`, turning the GUI into a pure immediate-mode view.
---
Track: gui_layout_refinement_20260223
Status: new
Overview: This track focuses on a holistic review and reorganization of the Manual Slop GUI. The goal is to ensure that AI tunings, diagnostic features, context management, and discussion history are logically placed to support an expert-level "Multi-Viewport" workflow. We will strengthen the "Arcade Aesthetics" and "Tactile Density" values while ensuring the layout remains intuitive for power users.
---
Track: gui_performance_20260223
Status: new
Overview: This track focuses on identifying and resolving severe frametime performance issues in the Manual Slop GUI. Current observations indicate massive frametime bloat even on idle startup, with performance significantly regressing (target 60 FPS / <16.6ms) since commit `8aa70e287fbf93e669276f9757965d5a56e89b10`.
---
Track: gui_performance_profiling_20260307
Status: unknown
Overview: Implement fine-grained performance profiling within the main ImGui rendering loop (`gui_2.py`) to ensure adherence to data-oriented and immediate mode heuristics. This track will provide visual diagnostics for high-overhead UI components, allowing developers to monitor and optimize render frame times.
---
Track: gui_sim_extension_20260224
Status: new
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.
---
Track: history_segregation_20260224
Status: new
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.
---
Track: kill_abort_workers_20260306
Status: planned
Overview: Add ability to kill/abort a running Tier 3 worker mid-execution. Currently workers run to completion; add cancel button with forced termination option.
---
Track: live_gui_testing_20260223
Status: new
Overview: Update the testing suite to ensure all tests (especially GUI-related and integration tests) communicate with a live running instance of `gui.py` started with the `--enable-test-hooks` argument. This ensures that tests can verify the actual application state and metrics via the built-in API hooks.
---
Track: live_ux_test_20260223
Status: new
Overview: This track implements a robust, "human-like" interaction test suite for Manual Slop. The suite will simulate a real user's workflowβfrom project creation to complex AI discussions and history managementβusing the application's API hooks. It aims to verify the "Integrated Workspace" functionality, tool execution, and history persistence without requiring manual human input, while remaining slow enough for visual audit.
---
Track: logging_refactor_20260226
Status: new
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.
---
Track: manual_block_control_20260306
Status: planned
Overview: Allow user to manually block or unblock tickets with custom reasons. Currently blocked tickets rely solely on dependency resolution; add manual override capability.
---
Track: manual_skeleton_injection_20260306
Status: planned
Overview: Add UI controls to manually inject file skeletons into discussions. Allow user to preview skeleton content before sending to AI, with option to toggle between skeleton and full file.
---
Track: manual_slop_headless_20260225
Status: new
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.
---
Track: minimax_provider_20260306
Status: unknown
Overview: Add MiniMax as a new AI provider to Manual Slop. MiniMax provides high-performance text generation models (M2.5, M2.1, M2) with massive context windows (200k+ tokens) and competitive pricing.
---
Track: mma_agent_focus_ux_20260302
Status: new
Overview: All MMA observability panels (comms history, tool calls, discussion) display
global/session-scoped data. When 4 tiers are running concurrently, their traffic
is indistinguishable. This track adds a `source_tier` field to every comms and
tool log entry at the point of emission, then adds a "Focus Agent" selector that
filters the Operations Hub panels to show only one tier's traffic at a time.

**Depends on:** `feature_bleed_cleanup_20260302` (Phase 1 removes the dead comms
panel duplicate; this track extends the live panel at gui_2.py:~3400).
---
Track: MMA Core Engine Implementation
Status: planning
Overview: # Specification: MMA Core Engine Implementation

## 1. Overview
This track consolidates the implementation of the 4-Tier Hierarchical Multi-Model Architecture into the `manual_slop` codebase. The arch...
---
Track: MMA Dashboard Visualization Overhaul
Status: planned
Overview: Make the invisible backend operations visible and interactive. The current GUI is too barebones to effectively manage a multi-agent system. This track overhauls the MMA Dashboard to provide real-time insights into tracks, task dependencies, and agent streams.
---
Track: MMA Data Architecture & DAG Engine
Status: planned
Overview: Restructure how `manual_slop` stores and executes work. The current implementation relies on global state and linear execution, which does not support the complexity of multi-agent, task-based workflows. This track establishes a robust, data-oriented foundation using track-scoped state and a native Python Directed Acyclic Graph (DAG) engine.
---
Track: mma_formalization_20260225
Status: new
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.
---
Track: mma_implementation_20260224
Status: new
Overview: # Specification: 4-Tier Architecture Implementation & Conductor Self-Improvement

## 1. Overview
This track encompasses two major phases. Phase 1 focuses on designing a comprehensive, step-by-step imp...
---
Track: mma_multiworker_viz_20260306
Status: planned
Overview: Split-view GUI for parallel worker streams per tier. Visualize multiple concurrent workers with individual status, output tabs, and resource usage. Enable kill/restart per worker.
---
Track: MMA Orchestrator Integration
Status: in-progress
Overview: Implement the full hierarchical orchestration loop, connecting Tier 1 (PM) strategic planning with Tier 2 (Tech Lead) tactical ticket generation. This track will enable the GUI to autonomously break down high-level user 'Epics' into actionable tracks and tickets, and manage their execution through the multi-agent system.
---
Track: mma_pipeline_fix_20260301
Status: new
Overview: The MMA pipeline has a verified code path from `run_worker_lifecycle` β `_queue_put("response", ...)` β `_process_event_queue` β `_pending_gui_tasks("handle_ai_response")` β `mma_streams[stream_id] = text`. However, the robust_live_simulation track's session compression (2026-02-28) documented that Tier 3 worker output never appears in `mma_streams` during actual GUI operation. The simulation only ever sees `'Tier 1'` in `mma_streams` keys.

This track diagnoses and fixes the pipeline break, then verifies end-to-end that worker output flows from `ai_client.send()` through to the GUI's `mma_streams` dict.
---
Track: mma_utilization_refinement_20260226
Status: new
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.
---
Track: mma_verification_20260225
Status: new
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.
---
Track: mma_verification_mock
Status: new
Overview: This is a mock track designed to verify the full Tier 2 -> Tier 3 -> Tier 4 delegation flow within the Conductor framework.
---
Track: native_orchestrator_20260306
Status: planned
Overview: Absorb `mma_exec.py` functionality into core application. Manual Slop natively reads/writes plan.md, manages metadata.json, and orchestrates MMA tiers in pure Python without external CLI subprocess calls.
---
Track: on_demand_def_lookup_20260306
Status: planned
Overview: Add ability for agent to request specific class/function definitions during discussion. Parse @symbol syntax to trigger lookup and display inline in the discussion.
---
Track: per_ticket_model_20260306
Status: planned
Overview: Allow user to manually select which model to use for a specific ticket, overriding the default tier model. Useful for forcing smarter model on hard tickets.
---
Track: pipeline_pause_resume_20260306
Status: planned
Overview: Add global pause/resume for entire DAG execution pipeline. Allow user to freeze all worker activity and resume later without losing state.
---
Track: python_style_refactor_20260227
Status: unknown
Overview: # Specification: AI-Optimized Python Style Refactor

## 1. Overview
Refactor the Python codebase to a "Single-Space, Ultra-Compact" style specifically designed to minimize token consumption for AI age...
---
Track: Robust Live Simulation Verification
Status: planned
Overview: Establish a robust, visual simulation framework to prevent regressions in the complex GUI and asynchronous orchestration layers. This track replaces manual human verification with an automated script that clicks through the GUI and verifies the rendered state.
---
Track: session_insights_20260306
Status: planned
Overview: Token usage over time, cost projections, session summary with efficiency scores. Visualize session_logger data.
---
Track: simulation_hardening_20260301
Status: new
Overview: The `robust_live_simulation_verification` track is marked complete but its session compression documents three unresolved issues: (1) brittle mock that triggers the wrong approval popup, (2) popup state desynchronization after "Accept" clicks, (3) Tier 3 output never appearing in `mma_streams` (fixed by `mma_pipeline_fix` track). This track stabilizes the simulation framework so it reliably passes end-to-end.
---
Track: strict_execution_queue_completed_20260306
Status: completed
Overview: No overview available.
---
Track: strict_static_analysis_and_typing_20260302
Status: new
Overview: The codebase currently suffers from massive type-safety debt (512+ `mypy` errors across 64 files) and lingering `ruff` violations. This track will harden the foundation by resolving all violations, enforcing strict typing (especially in `gui_2.py` and `api_hook_client.py`), and integrating pre-commit checks. This is a prerequisite for safe AI-driven refactoring.
---
Track: tech_debt_and_test_cleanup_20260302
Status: new
Overview: Due to rapid iterative development and feature bleed across multiple Tier 2-led tracks, significant tech debt has accumulated in both the testing suite and `gui_2.py`.
This track will clean up test fixtures, enforce test assertion integrity, and remove dead codebase remnants.
---
Track: test_architecture_integrity_audit_20260304
Status: unknown
Overview: Comprehensive audit of testing infrastructure and simulation framework to identify false positive risks, coverage gaps, and simulation fidelity issues. This analysis was triggered by a request to review how tests and simulations are setup, whether tests can report passing grades when they fail, and if simulations are rigorous enough or are just rough emulators.
---
Track: test_curation_20260225
Status: new
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.
---
Track: test_hooks_20260223
Status: new
Overview: This track introduces a comprehensive suite of API hooks designed specifically for the Gemini CLI and the Conductor framework. These hooks will allow automated agents to manipulate and test the internal state of the application without requiring manual GUI interaction, enabling automated test-driven development and track progression validation.
---
Track: Test Integrity Audit & Intent Documentation
Status: in_progress
Overview: Audit and fix tests that have been "simplified" or "dumbed down" by AI agents, restoring their original verification intent through explicit documentation comments. This track addresses the growing problem of AI agents "completing" tasks by weakening test assertions rather than implementing proper functionality.
---
Track: test_regression_verification_20260307
Status: unknown
Overview: Verify that all existing tests pass with 0 regressions after recent track implementations (Kill/Abort, Block/Unblock, Pause/Resume, Per-Ticket Model Override).
---
Track: test_stabilization_20260302
Status: new
Overview: The goal of this track is to stabilize and unify the project's test suite. This involves resolving pervasive `asyncio` lifecycle errors, consolidating redundant testing paradigms (specifically manual GUI subprocesses), ensuring artifact isolation in `./tests/artifacts/`, implementing functional assertions for currently mocked-out tests, and updating documentation to reflect the finalized verification framework.
---
Track: ticket_queue_mgmt_20260306
Status: planned
Overview: Allow user to manually reorder, prioritize, or requeue tickets in the DAG. Add drag-drop reordering, priority tags, and bulk selection for execute/skip/block operations.
---
Track: tier4_auto_patching_20260306
Status: planned
Overview: Elevate Tier 4 from log summarizer to auto-patcher. When verification tests fail, Tier 4 generates a unified diff patch. GUI displays side-by-side diff; user clicks Apply Patch to resume pipeline.
---
Track: Tiered Context Scoping & HITL Approval
Status: planned
Overview: Provide the user with absolute visual control over what the AI sees at every level of the hierarchy. Currently, the system builds a single massive context blob. This track introduces context subsetting based on the target tier and implements a Human-in-the-Loop (HITL) approval gate before any Tier 3/4 worker is spawned.
---
Track: tool_usage_analytics_20260306
Status: planned
Overview: Analytics panel showing most-used tools, average execution time, and failure rates. Uses existing tool execution data from ai_client.
---
Track: track_progress_viz_20260306
Status: planned
Overview: Progress bars and percentage completion for active tracks and tickets. Better visualization of DAG execution state.
---
Track: true_parallel_worker_execution_20260306
Status: planned
Overview: Add worker pool management and configurable concurrency limits to the DAG engine. Currently workers execute in parallel per tick but with no limits or tracking; this track adds max_workers configuration, worker tracking, and proper pool management.
---
Track: ui_performance_20260223
Status: new
Overview: This track aims to resolve subpar UI performance (currently perceived below 60 FPS) by implementing a robust performance monitoring system. This system will collect high-resolution telemetry (Frame Time, Input Lag, Thread Usage) and expose it to both the user (via a Diagnostics Panel) and the AI (via API hooks). This ensures that performance degradation is caught early during development and testing.
---
Track: visual_dag_ticket_editing_20260306
Status: planned
Overview: Replace linear ticket list with interactive node graph using ImGui Bundle node editor. Users can visually drag dependency lines, split nodes, or delete tasks before execution.
---
Track: caching_optimization_20260308
Status: new
Overview: This track aims to verify and optimize the caching strategies across all supported AI providers (Gemini, Anthropic, DeepSeek, MiniMax, and OpenAI). The goal is to minimize token consumption and latency by ensuring that static and recurring context (system prompts, tool definitions, project documents, and conversation history) are effectively cached using each provider's specific mechanisms.
---
Track: codebase_audit_20260308
Status: new
Overview: The objective of this track is to audit the `./src` and `./simulation` directories to improve human readability and maintainability. The codebase has matured, and it is necessary to identify and address redundant code paths and state tracking, add missing docstrings to critical paths, and organize declarations/definitions within files.
---
Track: conductor_path_configurable_20260306
Status: unknown
Overview: Eliminate all hardcoded paths in the application. Make directory paths configurable via `config.toml` or environment variables, allowing the running app to use different directories from development setup. This is **Phase 0 - Critical Infrastructure** that must be completed before other Phase 3 tracks.
---
Track: external_editor_integration_20260308
Status: new
Overview: This feature adds the ability to open files modified by AI agents in external text editors (such as VSCode or 10xNotepad) directly from the tool approval popup. This allows users to leverage their preferred editor's native diffing and editing capabilities before confirming an agent's changes.
---
Track: external_mcp_support_20260308
Status: new
Overview: This feature adds support for integrating external Model Context Protocol (MCP) servers into Manual Slop. This allows agents to utilize tools from a wide ecosystem of MCP servers (like those for databases, APIs, or specialized utilities) alongside the application's native tools.
---
Track: Bootstrap gencpp Python Bindings Project
Status: pending
Overview: Create a new standalone Python project to build CFFI bindings for gencpp (C/C++ staged metaprogramming library). This will eventually provide richer C++ AST understanding than tree-sitter (full type information, operators, specifiers) but is a longer-term effort. This track bootstraps the project structure and initial bindings.
---
Track: GUI Path Configuration in Context Hub
Status: pending
Overview: Add path configuration UI to the Context Hub in the GUI. Allow users to view and edit configurable paths (conductor, logs, scripts) directly from the application without manually editing config.toml or environment variables.
---
Track: hook_api_expansion_20260308
Status: new
Overview: This track aims to transform the Manual Slop Hook API into a comprehensive control plane for headless use. It focuses on exposing all relevant internal states (worker traces, AST metadata, financial metrics, concurrency telemetry) and providing granular control over the application's lifecycle, MMA pipeline, and context management. Additionally, it introduces a WebSocket-based streaming interface for real-time event delivery.
---
Track: log_session_overhaul_20260308
Status: new
Overview: This track focuses on centralizing log management, improving the reliability and scope of session restoration, and optimizing log storage by offloading large data blobs (scripts and tool outputs) to external files. It also aims to "clean" the discussion history by moving transient system warnings to a dedicated diagnostic log.
---
Track: manual_ux_validation_20260302
Status: new
Overview: This track is an unusual, highly interactive human-in-the-loop review session. The user will act as the primary QA and Designer, manually using the GUI and observing it during slow-interval simulation runs. The goal is to aggressively iterate on the "feel" of the application: analyzing blinking animations, structural decisions (Tabs vs. Panels vs. Collapsing Headers), knob/control placements, and the efficacy of popups (including adding auto-close timers).
---
Track: markdown_highlighting_20260308
Status: new
Overview: This track introduces rich text rendering to the Manual Slop GUI by adding support for GitHub-Flavored Markdown (GFM) in message and response views. It also adds syntax highlighting for code blocks and text content when the language context can be cheaply resolved (e.g., via known metadata or file extensions).
---
Track: openai_integration_20260308
Status: new
Overview: This track introduces support for OpenAI as a first-class model provider. It involves implementing a dedicated client in `src/ai_client.py`, updating configuration models, enhancing the GUI for provider selection, and integrating OpenAI into the tiered MMA architecture.
---
Track: Project-Specific Conductor Directory
Status: pending
Overview: Make the conductor directory per-project instead of global. Each project TOML can specify its own `conductor_dir` path, allowing separate track/state management per project. This enables using Manual Slop with multiple independent projects without track/ticket cross-pollution.
---
Track: rag_support_20260308
Status: new
Overview: This track introduces Retrieval-Augmented Generation (RAG) capabilities to Manual Slop. It allows agents to search and retrieve relevant information from large local codebases, project documentation, or external knowledge bases, overcoming context window limitations and reducing hallucination.
---
Track: saved_presets_20260308
Status: new
Overview: This feature introduces the ability to save, manage, and switch between system prompt presets for both global (application-wide) and project-specific contexts. Presets will include not only the system prompt text but also model-specific parameters like temperature and top_p, effectively allowing for "AI Profiles."
---
Track: saved_tool_presets_20260308
Status: new
Overview: This feature adds the ability to create, save, and manage "Tool Presets" for agent roles. These presets define which tools are available to an agent and their respective "auto" vs "ask" approval levels. Tools will be organized into dynamic, TOML-defined categories (e.g., Python, General) and integrated into the global and project-specific AI settings.
---
Track: selectable_ui_text_20260308
Status: new
Overview: This track aims to address UI inconveniences by making critical text across the GUI selectable and copyable. This includes discussion history, communication logs, tool outputs, and key metrics. The goal is to provide a standard "Copy to Clipboard" capability via OS-native selection and shortcuts (Ctrl+C).
---
Track: tool_bias_tuning_20260308
Status: new
Overview: This track introduces a mechanism to influence AI agent tool selection by implementing a weighting and scoring system at the orchestration layer. Since model APIs do not natively support tool priority, this feature uses semantic nudging (tags in tool descriptions) and explicit system instructions to "bias" the agent toward preferred tools and parameters.
---
Track: Tree-Sitter C/C++ MCP Tools
Status: pending
Overview: Add tree-sitter-based C and C++ parsing support to the MCP client, providing skeleton and outline tools for C/C++ codebases. Tools will be prefixed `ts_c_` and `ts_cpp_` to distinguish from existing Python tools and leave namespace open for future gencpp integration.
---
Track: ui_theme_overhaul_20260308
Status: new
Overview: This track aims to modernize the application's appearance by implementing a professional, high-fidelity UI theme using `imgui-bundle`'s native styling and theming capabilities. It focuses on improving typography, visual shapes, and introducing advanced features like custom shaders, multi-viewport support, and user-managed layout presets.
---
Track: ux_sim_test_20260308
Status: unknown
Overview: No overview available.
---
Track: zhipu_integration_20260308
Status: new
Overview: This track introduces support for Zhipu AI (z.ai) as a first-class model provider. It involves implementing a dedicated client in `src/ai_client.py` for the GLM series of models, updating configuration models, enhancing the GUI for provider selection, and integrating the provider into the tiered MMA architecture.
---

Please generate the implementation tracks for this request.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['C:\\projects\\manual_slop\\tests\\mock_gemini_cli.py', '-m', 'gemini-2.5-flash-lite', '--prompt', '', '--output-format', 'stream-json']
PROMPT:
You are a helpful coding assistant with access to a PowerShell tool (run_powershell) and MCP tools (file access: read_file, list_directory, search_files, get_file_summary, web access: web_search, fetch_url). When calling file/directory tools, always use the 'path' parameter for the target path. When asked to create or edit files, prefer targeted edits over full rewrites. Always explain what you are doing before invoking the tool.

When writing or rewriting large files (especially those containing quotes, backticks, or special characters), avoid python -c with inline strings. Instead: (1) write a .py helper script to disk using a PS here-string (@'...'@ for literal content), (2) run it with `python <script>`, (3) delete the helper. For small targeted edits, use PowerShell's (Get-Content) / .Replace() / Set-Content or Add-Content directly.

When making function calls using tools that accept array or object parameters ensure those are structured using JSON. For example:
When you need to verify a change, rely on the exit code and stdout/stderr from the tool β the user's context files are automatically refreshed after every tool call, so you do NOT need to re-read files that are already provided in the <context> block.

[USER SYSTEM PROMPT]

You are the Tier 1 Orchestrator (Product Manager) for the Manual Slop project.
Your role is high-level strategic planning, architecture enforcement, and cross-module delegation.
You operate strictly on metadata, summaries, and executive-level directives.
NEVER request or attempt to read raw implementation code unless specifically provided in a Macro-Diff.
Maintain a "Godot ECS Flat List format" (JSON array of objects) for structural outputs.

PATH: Epic Initialization (Project Planning)
GOAL: Break down a massive feature request into discrete Implementation Tracks.

CONSTRAINTS:
- IGNORE all source code, AST skeletons, and previous micro-task histories.
- FOCUS ONLY on the Repository Map and Project Meta-State.

OUTPUT REQUIREMENT:
Return a JSON array of 'Tracks'. Each track object must follow the Godot ECS Flat List format:
[
  {
    "id": "track_unique_id",
    "type": "Track",
    "module": "target_module_name",
    "persona": "required_tech_lead_persona",
    "severity": "Low|Medium|High",
    "goal": "Descriptive goal",
    "acceptance_criteria": ["criteria_1", "criteria_2"]
  },
  ...
]


<context>

</context>

### USER REQUEST:
Add timestamps

### REPOSITORY MAP:


### TRACK HISTORY:
Track: api_hooks_verification_20260223
Status: new
Overview: This track focuses on integrating the existing, previously implemented API hooks (from track `test_hooks_20260223`) into the Conductor workflow. The primary goal is to automate the verification steps within the "Phase Completion Verification and Checkpointing Protocol", reducing the need for manual user intervention and enabling a more streamlined, automated development process.
---
Track: api_metrics_20260223
Status: new
Overview: This track aims to optimize token efficiency and transparency by reviewing and improving how vendor APIs (Gemini and Anthropic) handle conservative context pruning. The primary focus is on extracting, plotting, and exposing deep metrics to the GUI so developers can intuit how close they are to API limits (e.g., token caps, cache counts, history bleed).
---
Track: api_vendor_alignment_20260223
Status: new
Overview: This track involves a comprehensive audit of the "Manual Slop" codebase to ensure that the integration with Google Gemini (`google-genai`) and Anthropic Claude (`anthropic`) SDKs aligns perfectly with their latest official documentation and best practices. The goal is to identify discrepancies, performance bottlenecks, or deprecated patterns and implement the necessary fixes.
---
Track: architecture_boundary_hardening_20260302
Status: new
Overview: The `manual_slop` project sandbox provides AI meta-tooling (`mma_exec.py`, `tool_call.py`) to orchestrate its own development. When AI agents added advanced AST tools (like `set_file_slice`) to `mcp_client.py` for meta-tooling, they failed to fully integrate them into the application's GUI, config, or HITL (Human-In-The-Loop) safety models. Additionally, meta-tooling scripts are bleeding tokens and rely on non-portable hardcoded machine paths, while the internal application's state machine can deadlock.
---
Track: cache_analytics_20260306
Status: planned
Overview: Gemini cache hit/miss visualization, memory usage, TTL status display. Uses existing `ai_client.get_gemini_cache_stats()` which is implemented but has no GUI representation.
---
Track: codebase_migration_20260302
Status: new
Overview: This track focuses on restructuring the codebase to alleviate clutter by moving the main implementation files from the project root into a dedicated `src/` directory. Additionally, files that are completely unused by the current implementation will be automatically identified and removed. A new clean entry point (`sloppy.py`) will be created in the root directory.
---
Track: comprehensive_gui_ux_20260228
Status: completed
Overview: This track enhances the existing MMA orchestration GUI from its current functional-but-minimal state to a production-quality control surface. The existing implementation already has a working Track Browser, DAG tree visualizer, epic planning flow, approval dialogs, and token usage table. This track focuses on the **gaps**: dedicated tier stream panels, DAG editing, track-scoped discussions, conductor lifecycle GUI forms, cost tracking, and visual polish.
---
Track: conductor_workflow_improvements_20260302
Status: new
Overview: Recent Tier 2 track implementations have resulted in feature bleed, redundant code, unread state variables, and degradation of TDD discipline (e.g., zero-assertion tests).
This track updates the Conductor documentation (`workflow.md`) and the Gemini skills for Tiers 2 and 3 to hard-enforce TDD, prevent hallucinated "mock" implementations, and enforce strict codebase auditing before writing code.
---
Track: consolidate_cruft_and_log_taxonomy_20260228
Status: unknown
Overview: This track focuses on cleaning up the project root by consolidating temporary and test-related files into a dedicated directory and establishing a structured taxonomy for session logs. This will improve project organization and make manual file exploration easier before a dedicated GUI log viewer is implemented.
---
Track: context_management_20260223
Status: new
Overview: This track implements UI improvements and structural changes to Manual Slop to provide explicit visualization of context memory usage and token consumption, fulfilling the "Expert systems level utility" and "Full control" product goals.
---
Track: context_token_viz_20260301
Status: new
Overview: product.md lists "Context & Memory Management" as primary use case #2: "Better visualization and management of token usage and context memory, allowing developers to optimize prompt limits manually." The backend already computes everything needed via `ai_client.get_history_bleed_stats()` (ai_client.py:1657-1796, 140 lines). This track builds the UI to expose it.
---
Track: cost_token_analytics_20260306
Status: planned
Overview: # Implementation Plan: Cost & Token Analytics Panel (cost_token_analytics_20260306)

> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)

## Phase 1: Foundat...
---
Track: deepseek_support_20260225
Status: new
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.
---
Track: deep_ast_context_pruning_20260306
Status: planned
Overview: Use tree_sitter to parse target file AST and inject condensed skeletons into worker prompts. Currently workers receive full file context; this track reduces token burn by injecting only relevant function/method signatures.
---
Track: documentation_refresh_20260224
Status: new
Overview: This track implements a high-density, expert-level documentation suite for the Manual Slop project. The documentation style is strictly modeled after the **pedagogical and narrative standards** of `gencpp` and `VEFontCache-Odin`. It moves beyond simple "User Guides" to provide a **"USA Graphics Company"** architectural reference: high information density, tactical technical transparency, and a narrative intent that guides a developer from high-level philosophy to low-level implementation.
---
Track: enhanced_context_control_20260307
Status: planned
Overview: Give developers granular control over how files are included in the AI context and provide visibility into the active Gemini cache state. This involves moving away from a simple list of files to a structured format with per-file flags (`auto_aggregate`, `force_full`), revamping the UI to display this state, and updating the context builders and API clients to respect and expose these details.
---
Track: event_driven_metrics_20260223
Status: new
Overview: Refactor the API metrics update mechanism to be event-driven. Currently, the UI likely polls or recalculates metrics on every frame. This track will implement a signal/event system where `ai_client.py` broadcasts updates only when significant API activities (requests, responses, tool calls, or stream chunks) occur.
---
Track: feature_bleed_cleanup_20260302
Status: new
Overview: Multiple tracks added code to `gui_2.py` without removing the old versions, leaving
dead duplicate methods, conflicting menu bar designs, and redundant state initializations.
This track removes confirmed dead code, resolves the two-menubar conflict, and cleans
up the token budget layout regression β restoring a consistent, non-contradictory design state.
---
Track: gemini_cli_headless_20260224
Status: new
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.
---
Track: gemini_cli_parity_20260225
Status: new
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.
---
Track: gui2_feature_parity_20260223
Status: new
Overview: # 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 i...
---
Track: gui2_parity_20260224
Status: new
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`.
---
Track: gui_decoupling_controller_20260302
Status: new
Overview: `gui_2.py` currently operates as a Monolithic God Object (3,500+ lines). It violates the Data-Oriented Design heuristic by owning complex business logic, orchestrator hooks, and markdown file building. This track extracts the core state machine and lifecycle into a headless `app_controller.py`, turning the GUI into a pure immediate-mode view.
---
Track: gui_layout_refinement_20260223
Status: new
Overview: This track focuses on a holistic review and reorganization of the Manual Slop GUI. The goal is to ensure that AI tunings, diagnostic features, context management, and discussion history are logically placed to support an expert-level "Multi-Viewport" workflow. We will strengthen the "Arcade Aesthetics" and "Tactile Density" values while ensuring the layout remains intuitive for power users.
---
Track: gui_performance_20260223
Status: new
Overview: This track focuses on identifying and resolving severe frametime performance issues in the Manual Slop GUI. Current observations indicate massive frametime bloat even on idle startup, with performance significantly regressing (target 60 FPS / <16.6ms) since commit `8aa70e287fbf93e669276f9757965d5a56e89b10`.
---
Track: gui_performance_profiling_20260307
Status: unknown
Overview: Implement fine-grained performance profiling within the main ImGui rendering loop (`gui_2.py`) to ensure adherence to data-oriented and immediate mode heuristics. This track will provide visual diagnostics for high-overhead UI components, allowing developers to monitor and optimize render frame times.
---
Track: gui_sim_extension_20260224
Status: new
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.
---
Track: history_segregation_20260224
Status: new
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.
---
Track: kill_abort_workers_20260306
Status: planned
Overview: Add ability to kill/abort a running Tier 3 worker mid-execution. Currently workers run to completion; add cancel button with forced termination option.
---
Track: live_gui_testing_20260223
Status: new
Overview: Update the testing suite to ensure all tests (especially GUI-related and integration tests) communicate with a live running instance of `gui.py` started with the `--enable-test-hooks` argument. This ensures that tests can verify the actual application state and metrics via the built-in API hooks.
---
Track: live_ux_test_20260223
Status: new
Overview: This track implements a robust, "human-like" interaction test suite for Manual Slop. The suite will simulate a real user's workflowβfrom project creation to complex AI discussions and history managementβusing the application's API hooks. It aims to verify the "Integrated Workspace" functionality, tool execution, and history persistence without requiring manual human input, while remaining slow enough for visual audit.
---
Track: logging_refactor_20260226
Status: new
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.
---
Track: manual_block_control_20260306
Status: planned
Overview: Allow user to manually block or unblock tickets with custom reasons. Currently blocked tickets rely solely on dependency resolution; add manual override capability.
---
Track: manual_skeleton_injection_20260306
Status: planned
Overview: Add UI controls to manually inject file skeletons into discussions. Allow user to preview skeleton content before sending to AI, with option to toggle between skeleton and full file.
---
Track: manual_slop_headless_20260225
Status: new
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.
---
Track: minimax_provider_20260306
Status: unknown
Overview: Add MiniMax as a new AI provider to Manual Slop. MiniMax provides high-performance text generation models (M2.5, M2.1, M2) with massive context windows (200k+ tokens) and competitive pricing.
---
Track: mma_agent_focus_ux_20260302
Status: new
Overview: All MMA observability panels (comms history, tool calls, discussion) display
global/session-scoped data. When 4 tiers are running concurrently, their traffic
is indistinguishable. This track adds a `source_tier` field to every comms and
tool log entry at the point of emission, then adds a "Focus Agent" selector that
filters the Operations Hub panels to show only one tier's traffic at a time.

**Depends on:** `feature_bleed_cleanup_20260302` (Phase 1 removes the dead comms
panel duplicate; this track extends the live panel at gui_2.py:~3400).
---
Track: MMA Core Engine Implementation
Status: planning
Overview: # Specification: MMA Core Engine Implementation

## 1. Overview
This track consolidates the implementation of the 4-Tier Hierarchical Multi-Model Architecture into the `manual_slop` codebase. The arch...
---
Track: MMA Dashboard Visualization Overhaul
Status: planned
Overview: Make the invisible backend operations visible and interactive. The current GUI is too barebones to effectively manage a multi-agent system. This track overhauls the MMA Dashboard to provide real-time insights into tracks, task dependencies, and agent streams.
---
Track: MMA Data Architecture & DAG Engine
Status: planned
Overview: Restructure how `manual_slop` stores and executes work. The current implementation relies on global state and linear execution, which does not support the complexity of multi-agent, task-based workflows. This track establishes a robust, data-oriented foundation using track-scoped state and a native Python Directed Acyclic Graph (DAG) engine.
---
Track: mma_formalization_20260225
Status: new
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.
---
Track: mma_implementation_20260224
Status: new
Overview: # Specification: 4-Tier Architecture Implementation & Conductor Self-Improvement

## 1. Overview
This track encompasses two major phases. Phase 1 focuses on designing a comprehensive, step-by-step imp...
---
Track: mma_multiworker_viz_20260306
Status: planned
Overview: Split-view GUI for parallel worker streams per tier. Visualize multiple concurrent workers with individual status, output tabs, and resource usage. Enable kill/restart per worker.
---
Track: MMA Orchestrator Integration
Status: in-progress
Overview: Implement the full hierarchical orchestration loop, connecting Tier 1 (PM) strategic planning with Tier 2 (Tech Lead) tactical ticket generation. This track will enable the GUI to autonomously break down high-level user 'Epics' into actionable tracks and tickets, and manage their execution through the multi-agent system.
---
Track: mma_pipeline_fix_20260301
Status: new
Overview: The MMA pipeline has a verified code path from `run_worker_lifecycle` β `_queue_put("response", ...)` β `_process_event_queue` β `_pending_gui_tasks("handle_ai_response")` β `mma_streams[stream_id] = text`. However, the robust_live_simulation track's session compression (2026-02-28) documented that Tier 3 worker output never appears in `mma_streams` during actual GUI operation. The simulation only ever sees `'Tier 1'` in `mma_streams` keys.

This track diagnoses and fixes the pipeline break, then verifies end-to-end that worker output flows from `ai_client.send()` through to the GUI's `mma_streams` dict.
---
Track: mma_utilization_refinement_20260226
Status: new
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.
---
Track: mma_verification_20260225
Status: new
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.
---
Track: mma_verification_mock
Status: new
Overview: This is a mock track designed to verify the full Tier 2 -> Tier 3 -> Tier 4 delegation flow within the Conductor framework.
---
Track: native_orchestrator_20260306
Status: planned
Overview: Absorb `mma_exec.py` functionality into core application. Manual Slop natively reads/writes plan.md, manages metadata.json, and orchestrates MMA tiers in pure Python without external CLI subprocess calls.
---
Track: on_demand_def_lookup_20260306
Status: planned
Overview: Add ability for agent to request specific class/function definitions during discussion. Parse @symbol syntax to trigger lookup and display inline in the discussion.
---
Track: per_ticket_model_20260306
Status: planned
Overview: Allow user to manually select which model to use for a specific ticket, overriding the default tier model. Useful for forcing smarter model on hard tickets.
---
Track: pipeline_pause_resume_20260306
Status: planned
Overview: Add global pause/resume for entire DAG execution pipeline. Allow user to freeze all worker activity and resume later without losing state.
---
Track: python_style_refactor_20260227
Status: unknown
Overview: # Specification: AI-Optimized Python Style Refactor

## 1. Overview
Refactor the Python codebase to a "Single-Space, Ultra-Compact" style specifically designed to minimize token consumption for AI age...
---
Track: Robust Live Simulation Verification
Status: planned
Overview: Establish a robust, visual simulation framework to prevent regressions in the complex GUI and asynchronous orchestration layers. This track replaces manual human verification with an automated script that clicks through the GUI and verifies the rendered state.
---
Track: session_insights_20260306
Status: planned
Overview: Token usage over time, cost projections, session summary with efficiency scores. Visualize session_logger data.
---
Track: simulation_hardening_20260301
Status: new
Overview: The `robust_live_simulation_verification` track is marked complete but its session compression documents three unresolved issues: (1) brittle mock that triggers the wrong approval popup, (2) popup state desynchronization after "Accept" clicks, (3) Tier 3 output never appearing in `mma_streams` (fixed by `mma_pipeline_fix` track). This track stabilizes the simulation framework so it reliably passes end-to-end.
---
Track: strict_execution_queue_completed_20260306
Status: completed
Overview: No overview available.
---
Track: strict_static_analysis_and_typing_20260302
Status: new
Overview: The codebase currently suffers from massive type-safety debt (512+ `mypy` errors across 64 files) and lingering `ruff` violations. This track will harden the foundation by resolving all violations, enforcing strict typing (especially in `gui_2.py` and `api_hook_client.py`), and integrating pre-commit checks. This is a prerequisite for safe AI-driven refactoring.
---
Track: tech_debt_and_test_cleanup_20260302
Status: new
Overview: Due to rapid iterative development and feature bleed across multiple Tier 2-led tracks, significant tech debt has accumulated in both the testing suite and `gui_2.py`.
This track will clean up test fixtures, enforce test assertion integrity, and remove dead codebase remnants.
---
Track: test_architecture_integrity_audit_20260304
Status: unknown
Overview: Comprehensive audit of testing infrastructure and simulation framework to identify false positive risks, coverage gaps, and simulation fidelity issues. This analysis was triggered by a request to review how tests and simulations are setup, whether tests can report passing grades when they fail, and if simulations are rigorous enough or are just rough emulators.
---
Track: test_curation_20260225
Status: new
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.
---
Track: test_hooks_20260223
Status: new
Overview: This track introduces a comprehensive suite of API hooks designed specifically for the Gemini CLI and the Conductor framework. These hooks will allow automated agents to manipulate and test the internal state of the application without requiring manual GUI interaction, enabling automated test-driven development and track progression validation.
---
Track: Test Integrity Audit & Intent Documentation
Status: in_progress
Overview: Audit and fix tests that have been "simplified" or "dumbed down" by AI agents, restoring their original verification intent through explicit documentation comments. This track addresses the growing problem of AI agents "completing" tasks by weakening test assertions rather than implementing proper functionality.
---
Track: test_regression_verification_20260307
Status: unknown
Overview: Verify that all existing tests pass with 0 regressions after recent track implementations (Kill/Abort, Block/Unblock, Pause/Resume, Per-Ticket Model Override).
---
Track: test_stabilization_20260302
Status: new
Overview: The goal of this track is to stabilize and unify the project's test suite. This involves resolving pervasive `asyncio` lifecycle errors, consolidating redundant testing paradigms (specifically manual GUI subprocesses), ensuring artifact isolation in `./tests/artifacts/`, implementing functional assertions for currently mocked-out tests, and updating documentation to reflect the finalized verification framework.
---
Track: ticket_queue_mgmt_20260306
Status: planned
Overview: Allow user to manually reorder, prioritize, or requeue tickets in the DAG. Add drag-drop reordering, priority tags, and bulk selection for execute/skip/block operations.
---
Track: tier4_auto_patching_20260306
Status: planned
Overview: Elevate Tier 4 from log summarizer to auto-patcher. When verification tests fail, Tier 4 generates a unified diff patch. GUI displays side-by-side diff; user clicks Apply Patch to resume pipeline.
---
Track: Tiered Context Scoping & HITL Approval
Status: planned
Overview: Provide the user with absolute visual control over what the AI sees at every level of the hierarchy. Currently, the system builds a single massive context blob. This track introduces context subsetting based on the target tier and implements a Human-in-the-Loop (HITL) approval gate before any Tier 3/4 worker is spawned.
---
Track: tool_usage_analytics_20260306
Status: planned
Overview: Analytics panel showing most-used tools, average execution time, and failure rates. Uses existing tool execution data from ai_client.
---
Track: track_progress_viz_20260306
Status: planned
Overview: Progress bars and percentage completion for active tracks and tickets. Better visualization of DAG execution state.
---
Track: true_parallel_worker_execution_20260306
Status: planned
Overview: Add worker pool management and configurable concurrency limits to the DAG engine. Currently workers execute in parallel per tick but with no limits or tracking; this track adds max_workers configuration, worker tracking, and proper pool management.
---
Track: ui_performance_20260223
Status: new
Overview: This track aims to resolve subpar UI performance (currently perceived below 60 FPS) by implementing a robust performance monitoring system. This system will collect high-resolution telemetry (Frame Time, Input Lag, Thread Usage) and expose it to both the user (via a Diagnostics Panel) and the AI (via API hooks). This ensures that performance degradation is caught early during development and testing.
---
Track: visual_dag_ticket_editing_20260306
Status: planned
Overview: Replace linear ticket list with interactive node graph using ImGui Bundle node editor. Users can visually drag dependency lines, split nodes, or delete tasks before execution.
---
Track: caching_optimization_20260308
Status: new
Overview: This track aims to verify and optimize the caching strategies across all supported AI providers (Gemini, Anthropic, DeepSeek, MiniMax, and OpenAI). The goal is to minimize token consumption and latency by ensuring that static and recurring context (system prompts, tool definitions, project documents, and conversation history) are effectively cached using each provider's specific mechanisms.
---
Track: codebase_audit_20260308
Status: new
Overview: The objective of this track is to audit the `./src` and `./simulation` directories to improve human readability and maintainability. The codebase has matured, and it is necessary to identify and address redundant code paths and state tracking, add missing docstrings to critical paths, and organize declarations/definitions within files.
---
Track: conductor_path_configurable_20260306
Status: unknown
Overview: Eliminate all hardcoded paths in the application. Make directory paths configurable via `config.toml` or environment variables, allowing the running app to use different directories from development setup. This is **Phase 0 - Critical Infrastructure** that must be completed before other Phase 3 tracks.
---
Track: external_editor_integration_20260308
Status: new
Overview: This feature adds the ability to open files modified by AI agents in external text editors (such as VSCode or 10xNotepad) directly from the tool approval popup. This allows users to leverage their preferred editor's native diffing and editing capabilities before confirming an agent's changes.
---
Track: external_mcp_support_20260308
Status: new
Overview: This feature adds support for integrating external Model Context Protocol (MCP) servers into Manual Slop. This allows agents to utilize tools from a wide ecosystem of MCP servers (like those for databases, APIs, or specialized utilities) alongside the application's native tools.
---
Track: Bootstrap gencpp Python Bindings Project
Status: pending
Overview: Create a new standalone Python project to build CFFI bindings for gencpp (C/C++ staged metaprogramming library). This will eventually provide richer C++ AST understanding than tree-sitter (full type information, operators, specifiers) but is a longer-term effort. This track bootstraps the project structure and initial bindings.
---
Track: GUI Path Configuration in Context Hub
Status: pending
Overview: Add path configuration UI to the Context Hub in the GUI. Allow users to view and edit configurable paths (conductor, logs, scripts) directly from the application without manually editing config.toml or environment variables.
---
Track: hook_api_expansion_20260308
Status: new
Overview: This track aims to transform the Manual Slop Hook API into a comprehensive control plane for headless use. It focuses on exposing all relevant internal states (worker traces, AST metadata, financial metrics, concurrency telemetry) and providing granular control over the application's lifecycle, MMA pipeline, and context management. Additionally, it introduces a WebSocket-based streaming interface for real-time event delivery.
---
Track: log_session_overhaul_20260308
Status: new
Overview: This track focuses on centralizing log management, improving the reliability and scope of session restoration, and optimizing log storage by offloading large data blobs (scripts and tool outputs) to external files. It also aims to "clean" the discussion history by moving transient system warnings to a dedicated diagnostic log.
---
Track: manual_ux_validation_20260302
Status: new
Overview: This track is an unusual, highly interactive human-in-the-loop review session. The user will act as the primary QA and Designer, manually using the GUI and observing it during slow-interval simulation runs. The goal is to aggressively iterate on the "feel" of the application: analyzing blinking animations, structural decisions (Tabs vs. Panels vs. Collapsing Headers), knob/control placements, and the efficacy of popups (including adding auto-close timers).
---
Track: markdown_highlighting_20260308
Status: new
Overview: This track introduces rich text rendering to the Manual Slop GUI by adding support for GitHub-Flavored Markdown (GFM) in message and response views. It also adds syntax highlighting for code blocks and text content when the language context can be cheaply resolved (e.g., via known metadata or file extensions).
---
Track: openai_integration_20260308
Status: new
Overview: This track introduces support for OpenAI as a first-class model provider. It involves implementing a dedicated client in `src/ai_client.py`, updating configuration models, enhancing the GUI for provider selection, and integrating OpenAI into the tiered MMA architecture.
---
Track: Project-Specific Conductor Directory
Status: pending
Overview: Make the conductor directory per-project instead of global. Each project TOML can specify its own `conductor_dir` path, allowing separate track/state management per project. This enables using Manual Slop with multiple independent projects without track/ticket cross-pollution.
---
Track: rag_support_20260308
Status: new
Overview: This track introduces Retrieval-Augmented Generation (RAG) capabilities to Manual Slop. It allows agents to search and retrieve relevant information from large local codebases, project documentation, or external knowledge bases, overcoming context window limitations and reducing hallucination.
---
Track: saved_presets_20260308
Status: new
Overview: This feature introduces the ability to save, manage, and switch between system prompt presets for both global (application-wide) and project-specific contexts. Presets will include not only the system prompt text but also model-specific parameters like temperature and top_p, effectively allowing for "AI Profiles."
---
Track: saved_tool_presets_20260308
Status: new
Overview: This feature adds the ability to create, save, and manage "Tool Presets" for agent roles. These presets define which tools are available to an agent and their respective "auto" vs "ask" approval levels. Tools will be organized into dynamic, TOML-defined categories (e.g., Python, General) and integrated into the global and project-specific AI settings.
---
Track: selectable_ui_text_20260308
Status: new
Overview: This track aims to address UI inconveniences by making critical text across the GUI selectable and copyable. This includes discussion history, communication logs, tool outputs, and key metrics. The goal is to provide a standard "Copy to Clipboard" capability via OS-native selection and shortcuts (Ctrl+C).
---
Track: tool_bias_tuning_20260308
Status: new
Overview: This track introduces a mechanism to influence AI agent tool selection by implementing a weighting and scoring system at the orchestration layer. Since model APIs do not natively support tool priority, this feature uses semantic nudging (tags in tool descriptions) and explicit system instructions to "bias" the agent toward preferred tools and parameters.
---
Track: Tree-Sitter C/C++ MCP Tools
Status: pending
Overview: Add tree-sitter-based C and C++ parsing support to the MCP client, providing skeleton and outline tools for C/C++ codebases. Tools will be prefixed `ts_c_` and `ts_cpp_` to distinguish from existing Python tools and leave namespace open for future gencpp integration.
---
Track: ui_theme_overhaul_20260308
Status: new
Overview: This track aims to modernize the application's appearance by implementing a professional, high-fidelity UI theme using `imgui-bundle`'s native styling and theming capabilities. It focuses on improving typography, visual shapes, and introducing advanced features like custom shaders, multi-viewport support, and user-managed layout presets.
---
Track: ux_sim_test_20260308
Status: unknown
Overview: No overview available.
---
Track: zhipu_integration_20260308
Status: new
Overview: This track introduces support for Zhipu AI (z.ai) as a first-class model provider. It involves implementing a dedicated client in `src/ai_client.py` for the GLM series of models, updating configuration models, enhancing the GUI for provider selection, and integrating the provider into the tiered MMA architecture.
---

Please generate the implementation tracks for this request.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
PATH: Epic Initialization  please produce tracks
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please generate the implementation tickets for this track.
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
Please read test.txt
You are assigned to Ticket T1.
Task Description: do something
------------------
--- MOCK INVOKED ---
ARGS: ['tests/mock_gemini_cli.py']
PROMPT:
role: tool
Here are the results: {"content": "done"}
------------------
