---
name: research-codebase
description: Research codebase comprehensively using parallel sub-agents to answer user questions. Use when the user asks to "research the codebase", "understand how X works", or "investigate Y".
---

# 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.

## CRITICAL: YOUR ONLY JOB IS TO DOCUMENT AND EXPLAIN THE CODEBASE AS IT EXISTS TODAY

- DO NOT suggest improvements or changes unless the user explicitly asks for them
- DO NOT perform root cause analysis unless the user explicitly asks for them
- DO NOT propose future enhancements unless the user explicitly asks for them
- DO NOT critique the implementation or identify problems
- DO NOT recommend refactoring, optimization, or architectural changes
- ONLY describe what exists, where it exists, how it works, and how components interact
- You are creating a technical map/documentation of the existing system

## Initial Setup:

When this command is invoked, respond with:

```
I'm ready to research the codebase. Please provide your research question or area of interest, and I'll analyze it thoroughly by exploring relevant components and connections.
```

Then wait for the user's research query.

## Steps to follow after receiving the research query:

1. **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

2. **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/write_todos to track all subtasks
   - Consider which directories, files, or architectural patterns are relevant

3. **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

4. **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 dev/log/ as supplementary historical context
   - Connect findings across different components
   - Include specific file paths and line numbers for reference
   - Highlight patterns, connections, and architectural insights and decisions
   - Answer the user's specific questions with concrete evidence

5. **Gather metadata for the research document:**

   - Generate all relevant metadata
   - Filename: `dev/research/YYYYMMDD-description.md`
     - Format: `YYYYMMDD-description.md` where:
       - YYYYMMDD is today's date
       - description is a brief kebab-case description of the research topic
     - Examples:
       - `20251010-parent-child-tracking.md`
       - `20260114-authentication-flow.md`

6. **Generate research document:**

   - Use the metadata gathered in step 4
   - Structure the document with YAML frontmatter followed by content:

     ```markdown
     ---
     date: [Current date and time with timezone in ISO format]
     topic: "[User's Question/Topic]"
     tags: [research, codebase, relevant-component-names]
     status: complete
     last_updated: [Current date in YYYY-MM-DD format]
     ---

     # Research: [User's Question/Topic]

     **Date**: [Current date and time with timezone from step 4]

     ## 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 dev/log/)

     [Relevant insights from dev/log/ directory with references]

     - `dev/log/something.md` - Historical decision about X
     - `dev/log/notes.md` - Past implementation of Y

     ## Related Research

     [Links to other research documents in dev/research/]

     ## Open Questions

     [Any areas that need further investigation]
     ```

7. **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

8. **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 dev/log/ 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)
- 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 dev/ 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
- **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