General

Research Codebase

You are tasked with conducting comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings.

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Feb 7, 2026

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Steps to follow after receiving the research query:

Read any directly mentioned files first:

If the user mentions specific files (tickets, docs, JSON), read them FULLY first
IMPORTANT: Use the Read tool WITHOUT limit/offset parameters to read entire files
CRITICAL: Read these files yourself in the main context before spawning any sub-tasks
This ensures you have full context before decomposing the research

Analyze and decompose the research question:

Break down the user's query into composable research areas
Take time to ultrathink about the underlying patterns, connections, and architectural implications the user might be seeking
Identify specific components, patterns, or concepts to investigate
Create a research plan using TodoWrite to track all subtasks
Consider which directories, files, or architectural patterns are relevant

Spawn parallel sub-agent tasks for comprehensive research:

Create multiple Task agents to research different aspects concurrently

The key is to use these agents intelligently:

Start with locator agents to find what exists
Then use analyzer agents on the most promising findings
Run multiple agents in parallel when they're searching for different things
Each agent knows its job - just tell it what you're looking for
Don't write detailed prompts about HOW to search - the agents already know

Wait for all sub-agents to complete and synthesize findings:

IMPORTANT: Wait for ALL sub-agent tasks to complete before proceeding
Compile all sub-agent results (both codebase and thoughts findings)
Prioritize live codebase findings as primary source of truth
Use thoughts/ findings as supplementary historical context
Connect findings across different components
Include specific file paths and line numbers for reference
Verify all thoughts/ paths are correct (e.g., thoughts/allison/ not thoughts/shared/ for personal files)
Highlight patterns, connections, and architectural decisions
Answer the user's specific questions with concrete evidence

Gather metadata for the research document:

generate all relevant metadata

Filename:

thoughts/shared/research/YYYY-MM-DD_HH-MM-SS_topic.md

Generate research document:

Use the metadata gathered in step 4

Structure the document with YAML frontmatter followed by content:

---
date: [Current date and time with timezone in ISO format]
researcher: [Researcher name]
git_commit: [Current commit hash]
branch: [Current branch name]
repository: [Repository name]
topic: "[User's Question/Topic]"
tags: [research, codebase, relevant-component-names]
status: complete
last_updated: [Current date in YYYY-MM-DD format]
last_updated_by: [Researcher name]
---

# Research: [User's Question/Topic]

**Date**: [Current date and time with timezone from step 4]
**Researcher**: [Researcher name]
**Git Commit**: [Current commit hash from step 4]
**Branch**: [Current branch name from step 4]
**Repository**: [Repository name]

## Research Question
[Original user query]

## Summary
[High-level findings answering the user's question]

## Detailed Findings

### [Component/Area 1]
- Finding with reference ([file.ext:line](link))
- Connection to other components
- Implementation details

### [Component/Area 2]
...

## Code References
- `path/to/file.py:123` - Description of what's there
- `another/file.ts:45-67` - Description of the code block

## Architecture Insights
[Patterns, conventions, and design decisions discovered]

## Historical Context (from thoughts/)
[Relevant insights from thoughts/ directory with references]
- `thoughts/shared/something.md` - Historical decision about X
- `thoughts/local/notes.md` - Past exploration of Y
Note: Paths exclude "searchable/" even if found there

## Related Research
[Links to other research documents in thoughts/shared/research/]

## Open Questions
[Any areas that need further investigation]

Add GitHub permalinks (if applicable):

Check if on main branch or if commit is pushed:
```
git branch --show-current
```
and
```
git status
```

If on main/master or pushed, generate GitHub permalinks:

Get repo info:
```
gh repo view --json owner,name
```

Create permalinks:

https://github.com/{owner}/{repo}/blob/{commit}/{file}#L{line}

Replace local file references with permalinks in the document

Sync and present findings:

Present a concise summary of findings to the user
Include key file references for easy navigation
Ask if they have follow-up questions or need clarification

Handle follow-up questions:

If the user has follow-up questions, append to the same research document
Update the frontmatter fields
```
last_updated
```
and
```
last_updated_by
```
to reflect the update

Add

last_updated_note: "Added follow-up research for [brief description]"

to frontmatter

Add a new section:
```
## Follow-up Research [timestamp]
```
Spawn new sub-agents as needed for additional investigation
Continue updating the document and syncing

Important notes:

Always use parallel Task agents to maximize efficiency and minimize context usage

Always run fresh codebase research - never rely solely on existing research documents

The thoughts/ directory provides historical context to supplement live findings

Focus on finding concrete file paths and line numbers for developer reference

Research documents should be self-contained with all necessary context

Each sub-agent prompt should be specific and focused on read-only operations

Consider cross-component connections and architectural patterns

Include temporal context (when the research was conducted)

Link to GitHub when possible for permanent references

Keep the main agent focused on synthesis, not deep file reading

Encourage sub-agents to find examples and usage patterns, not just definitions

Explore all of thoughts/ directory, not just research subdirectory

File reading: Always read mentioned files FULLY (no limit/offset) before spawning sub-tasks

Critical ordering: Follow the numbered steps exactly

ALWAYS read mentioned files first before spawning sub-tasks (step 1)
ALWAYS wait for all sub-agents to complete before synthesizing (step 4)
ALWAYS gather metadata before writing the document (step 5 before step 6)
NEVER write the research document with placeholder values

Path handling: The thoughts/searchable/ directory contains hard links for searching

Always document paths by removing ONLY "searchable/" - preserve all other subdirectories

Examples of correct transformations:

thoughts/searchable/allison/old_stuff/notes.md

→

thoughts/allison/old_stuff/notes.md

thoughts/searchable/shared/prs/123.md

→

thoughts/shared/prs/123.md

thoughts/searchable/global/shared/templates.md

→

thoughts/global/shared/templates.md

NEVER change allison/ to shared/ or vice versa - preserve the exact directory structure
This ensures paths are correct for editing and navigation

Frontmatter consistency:

Always include frontmatter at the beginning of research documents
Keep frontmatter fields consistent across all research documents
Update frontmatter when adding follow-up research
Use snake_case for multi-word field names (e.g.,
```
last_updated
```
,
```
git_commit
```
)
Tags should be relevant to the research topic and components studied

Steps to follow after receiving the research query:

Read any directly mentioned files first:

If the user mentions specific files (tickets, docs, JSON), read them FULLY first
IMPORTANT: Use the Read tool WITHOUT limit/offset parameters to read entire files
CRITICAL: Read these files yourself in the main context before spawning any sub-tasks
This ensures you have full context before decomposing the research

Analyze and decompose the research question:

Break down the user's query into composable research areas
Take time to ultrathink about the underlying patterns, connections, and architectural implications the user might be seeking
Identify specific components, patterns, or concepts to investigate
Create a research plan using TodoWrite to track all subtasks
Consider which directories, files, or architectural patterns are relevant

Spawn parallel sub-agent tasks for comprehensive research:

Create multiple Task agents to research different aspects concurrently

The key is to use these agents intelligently:

Start with locator agents to find what exists
Then use analyzer agents on the most promising findings
Run multiple agents in parallel when they're searching for different things
Each agent knows its job - just tell it what you're looking for
Don't write detailed prompts about HOW to search - the agents already know

Wait for all sub-agents to complete and synthesize findings:

IMPORTANT: Wait for ALL sub-agent tasks to complete before proceeding
Compile all sub-agent results (both codebase and thoughts findings)
Prioritize live codebase findings as primary source of truth
Use thoughts/ findings as supplementary historical context
Connect findings across different components
Include specific file paths and line numbers for reference
Verify all thoughts/ paths are correct (e.g., thoughts/allison/ not thoughts/shared/ for personal files)
Highlight patterns, connections, and architectural decisions
Answer the user's specific questions with concrete evidence

Gather metadata for the research document:

generate all relevant metadata

Filename:

thoughts/shared/research/YYYY-MM-DD_HH-MM-SS_topic.md

Generate research document:

Use the metadata gathered in step 4

Structure the document with YAML frontmatter followed by content:

---
date: [Current date and time with timezone in ISO format]
researcher: [Researcher name]
git_commit: [Current commit hash]
branch: [Current branch name]
repository: [Repository name]
topic: "[User's Question/Topic]"
tags: [research, codebase, relevant-component-names]
status: complete
last_updated: [Current date in YYYY-MM-DD format]
last_updated_by: [Researcher name]
---

# Research: [User's Question/Topic]

**Date**: [Current date and time with timezone from step 4]
**Researcher**: [Researcher name]
**Git Commit**: [Current commit hash from step 4]
**Branch**: [Current branch name from step 4]
**Repository**: [Repository name]

## Research Question
[Original user query]

## Summary
[High-level findings answering the user's question]

## Detailed Findings

### [Component/Area 1]
- Finding with reference ([file.ext:line](link))
- Connection to other components
- Implementation details

### [Component/Area 2]
...

## Code References
- `path/to/file.py:123` - Description of what's there
- `another/file.ts:45-67` - Description of the code block

## Architecture Insights
[Patterns, conventions, and design decisions discovered]

## Historical Context (from thoughts/)
[Relevant insights from thoughts/ directory with references]
- `thoughts/shared/something.md` - Historical decision about X
- `thoughts/local/notes.md` - Past exploration of Y
Note: Paths exclude "searchable/" even if found there

## Related Research
[Links to other research documents in thoughts/shared/research/]

## Open Questions
[Any areas that need further investigation]

Add GitHub permalinks (if applicable):

Check if on main branch or if commit is pushed:
```
git branch --show-current
```
and
```
git status
```

If on main/master or pushed, generate GitHub permalinks:

Get repo info:
```
gh repo view --json owner,name
```

Create permalinks:

https://github.com/{owner}/{repo}/blob/{commit}/{file}#L{line}

Replace local file references with permalinks in the document

Sync and present findings:

Present a concise summary of findings to the user
Include key file references for easy navigation
Ask if they have follow-up questions or need clarification

Handle follow-up questions:

If the user has follow-up questions, append to the same research document
Update the frontmatter fields
```
last_updated
```
and
```
last_updated_by
```
to reflect the update

Add

last_updated_note: "Added follow-up research for [brief description]"

to frontmatter

Add a new section:
```
## Follow-up Research [timestamp]
```
Spawn new sub-agents as needed for additional investigation
Continue updating the document and syncing

Important notes:

Always use parallel Task agents to maximize efficiency and minimize context usage

Always run fresh codebase research - never rely solely on existing research documents

The thoughts/ directory provides historical context to supplement live findings

Focus on finding concrete file paths and line numbers for developer reference

Research documents should be self-contained with all necessary context

Each sub-agent prompt should be specific and focused on read-only operations

Consider cross-component connections and architectural patterns

Include temporal context (when the research was conducted)

Link to GitHub when possible for permanent references

Keep the main agent focused on synthesis, not deep file reading

Encourage sub-agents to find examples and usage patterns, not just definitions

Explore all of thoughts/ directory, not just research subdirectory

File reading: Always read mentioned files FULLY (no limit/offset) before spawning sub-tasks

Critical ordering: Follow the numbered steps exactly

ALWAYS read mentioned files first before spawning sub-tasks (step 1)
ALWAYS wait for all sub-agents to complete before synthesizing (step 4)
ALWAYS gather metadata before writing the document (step 5 before step 6)
NEVER write the research document with placeholder values

Path handling: The thoughts/searchable/ directory contains hard links for searching

Always document paths by removing ONLY "searchable/" - preserve all other subdirectories

Examples of correct transformations:

thoughts/searchable/allison/old_stuff/notes.md

→

thoughts/allison/old_stuff/notes.md

thoughts/searchable/shared/prs/123.md

→

thoughts/shared/prs/123.md

thoughts/searchable/global/shared/templates.md

→

thoughts/global/shared/templates.md

NEVER change allison/ to shared/ or vice versa - preserve the exact directory structure
This ensures paths are correct for editing and navigation

Frontmatter consistency:

Always include frontmatter at the beginning of research documents
Keep frontmatter fields consistent across all research documents
Update frontmatter when adding follow-up research
Use snake_case for multi-word field names (e.g.,
```
last_updated
```
,
```
git_commit
```
)
Tags should be relevant to the research topic and components studied

Research Codebase

Additional Files (5)

Related Skills

<h1 align="center">

2. Apply Deepthink Protocol (reason about dependencies

- Identify gaps

Additional Files (5)

Research Codebase

Initial Setup:

Steps to follow after receiving the research query:

Important notes:

Research Codebase

Initial Setup:

Steps to follow after receiving the research query:

Important notes: