popoffvg/explore-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.

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 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 .notes/research 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 .notes/ 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: .notes/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]
     - `.notes/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 .notes/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

Search workflow (for explore sub-agents)

Every sub-agent spawned by this skill is an explore agent. It follows these rules.

Tool selection

| Question type | Tool | |---|---| | Semantic / conceptual ("how does X work") | cocoindex search first, then confirm hits with fff grep | | Known identifier (HandleRequest, userController.create) | fff grep — ONE identifier per query | | Find which file/module exists for a topic | fff find_files | | Multiple case variants in one call | fff multi_grep | | History / "when was X introduced" | commit-index search_commits |

Search tactics

1. Fan out. For "very thorough", run case variants via multi_grep and check tests/config/adjacent dirs.
2. Stop searching once you have the file paths. READ the top results to confirm — do not grep indefinitely.
3. After ~2 greps, READ the top result instead of grepping more variations.

Report format

Persist findings, THEN return the same summary to the spawning agent.

1. Write to .notes/ (repo root, create if missing). Descriptive kebab-case filename, e.g. .notes/explore-<topic>.md. This is the only place an explore agent may write.
2. Return the summary in your reply.

Both file and reply use this structure:

• Answer — direct conclusion.
• Locations — path:line for each relevant hit (clickable).
• Notes — naming conventions, related modules, or history. Omit if none.
• Gaps — what you could NOT find and where you looked, if incomplete.

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 thoughts/ 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 documentation operations
• Document cross-component connections and how systems interact
• 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
• Have sub-agents document examples and usage patterns as they exist
• Explore all of thoughts/ directory, not just research subdirectory
• CRITICAL: You and all sub-agents are documentarians, not evaluators
• REMEMBER: Document what IS, not what SHOULD BE
• NO RECOMMENDATIONS: Only describe the current state of the codebase
• 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 thoughts/searchable/ directory contains hard links for searching
  • Always document paths by removing ONLY "searchable/" - preserve all other subdirectories
  • Examples of correct transformations:
    • thoughts/searchable/allison/old_stuff/notes.md → thoughts/allison/old_stuff/notes.md
    • thoughts/searchable/shared/prs/123.md → .notes/prs/123.md
    • thoughts/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