/research

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:

The research request for the user MAY be provided at the end of this instruction inside of <prompt></prompt>

When this command is invoked:

If the prompt tag is empty 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.

If the prompt tag is populated, proceed with using it as the research request.

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. Check for existing Discovery Context (Preflight): Before deep-diving into code, check if high-level repo context exists.

   • Run discovery-locator to find docs in .llm/shared/context/.
   • If found, run discovery-analyzer on the best match with the user's query.
   • Act on the recommendation:
     • proceed_using_discovery: Read the discovery doc and use it as baseline understanding of architecture/components.
     • proceed_but_verify_live_code: Read the doc but verify critical details against live code.
     • request_discovery_refresh: Warn the user that discovery is stale/missing. Ask if they want to run /discovery first, or proceed with caution.

   Why this matters: Discovery docs answer "What is this system?" and "Where does X live conceptually?" — letting you focus research on "How does it actually work in code right now?"

3. 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

4. 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

5. 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 .llm findings)
   • Prioritize live codebase findings as primary source of truth
   • Use .llm/ findings as supplementary historical context
   • Connect findings across different components
   • Include specific file paths and line numbers for reference
   • Verify all .llm/ 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

6. Gather metadata for the research document:

   • generate all relevant metadata
   • Filename: .llm/shared/research/YYYY-MM-DD-ENG-XXXX-description.md
     • Format: YYYY-MM-DD-ENG-XXXX-description.md where:
       • YYYY-MM-DD is today's date
       • ENG-XXXX is the ticket number (omit if no ticket)
       • description is a brief kebab-case description of the research topic
     • Examples:
       • With ticket: 2025-01-08-ENG-1478-parent-child-tracking.md
       • Without ticket: 2025-01-08-authentication-flow.md

7. 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 .llm/)
     [Relevant insights from .llm/ directory with references]
     - `.llm/shared/something.md` - Historical decision about X
     - `.llm/local/notes.md` - Past exploration of Y
     Note: Paths exclude "searchable/" even if found there
     
     ## Related Research
     [Links to other research documents in .llm/shared/research/]
     
     ## Open Questions
     [Any areas that need further investigation]
     

8. 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

9. 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

10. Handle follow-up questions:

• Use your question/prompt tooling to make this a good user experience.
• 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 .llm/ 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 .llm/ 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 .llm/searchable/ directory contains hard links for searching
  • Always document paths by removing ONLY "searchable/" - preserve all other subdirectories
  • Examples of correct transformations:
    • .llm/searchable/allison/old_stuff/notes.md → thoughts/allison/old_stuff/notes.md
    • .llm/searchable/shared/prs/123.md → thoughts/shared/prs/123.md
    • .llm/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

$ARGUMENTS