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

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 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
    • We now have specialized agents that know how to do specific research tasks:

    For codebase research:

    • Use the codebase-locator agent to find WHERE files and components live
    • Use the codebase-analyzer agent to understand HOW specific code works (without critiquing it)
    • Use the codebase-pattern-finder agent to find examples of existing patterns (without evaluating them)

    IMPORTANT: All agents are documentarians, not critics. They will describe what exists without suggesting improvements or identifying issues.

    For thoughts directory:

    • Use the thoughts-locator agent to discover what documents exist about the topic
    • Use the thoughts-analyzer agent to extract key insights from specific documents (only the most relevant ones)

    For web research (only if user explicitly asks):

    • Use the web-search-researcher agent for external documentation and resources
    • IF you use web-research agents, instruct them to return LINKS with their findings, and please INCLUDE those links in your final report

    For Linear tickets (if relevant):

    • Use the linear-ticket-reader agent to get full details of a specific ticket
    • Use the linear-searcher agent to find related tickets or historical context

    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 to document how they work
    • 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
    • Remind agents they are documenting, not evaluating or improving
  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 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
  5. Gather metadata for the research document:
    • Run the hack/spec_metadata.sh script to generate all relevant metadata
    • Filename: thoughts/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
  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]
researcher: [Researcher name from thoughts status]
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 from thoughts status]
**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 documentation of what was found, answering the user's question by describing what exists]

## Detailed Findings

### [Component/Area 1]
- Description of what exists ([file.ext:line](link))
- How it connects to other components
- Current implementation details (without evaluation)

### [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 Documentation
[Current patterns, conventions, and design implementations found in the codebase]

## 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]
  7. 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
  8. Sync and present findings:
    • Run humanlayer thoughts sync to sync the thoughts directory
    • 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
  9. 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: