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:

     ---
     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