1-research-codebase

name	1_research_codebase
description	Conducts a comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings.

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.

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, read them FULLY first
   • Use the Read tool WITHOUT limit/offset parameters to read entire files
   • Read these files yourself in the main context before spawning any sub-tasks

2. Analyze and decompose the research question:

   • Break down the user's query into composable research areas
   • 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

3. Spawn parallel sub-agent tasks for comprehensive research:

   • Create multiple Task agents to research different aspects concurrently
   • Use specialized agents like codebase-locator, codebase-analyzer, pattern-finder
   • Run multiple agents in parallel when searching for different things

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

   • Wait for ALL sub-agent tasks to complete before proceeding
   • Compile all sub-agent results
   • Connect findings across different components
   • Include specific file paths and line numbers for reference
   • Highlight patterns, connections, and architectural decisions

5. Generate research document: Structure the document with YAML frontmatter followed by content:

   ---
   title: "[Concise research title]"
   type: research
   date: [Current date in YYYY-MM-DD format]
   tags: [relevant-component-names]
   status: complete
   ---
   
   # Research: [User's Question/Topic]
   
   ## 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)
   - 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]
   
   ## Open Questions
   
   [Any areas that need further investigation]
   

6. Save and present findings:

   • Check existing research files to determine next sequence number
   • Save to thoughts/research/NNN_topic.md where NNN is a 3-digit sequential number (001, 002, etc.) unless told otherwise
   • Present a concise summary of findings to the user
   • Include key file references for easy navigation

Important notes:

• Always use parallel Task agents to maximize efficiency
• Focus on finding concrete file paths and line numbers
• Research documents should be self-contained with all necessary context
• Each sub-agent prompt should be specific and focused
• Consider cross-component connections and architectural patterns