research-codebase

name	research-codebase
description	Conduct comprehensive research across the codebase to answer questions by spawning parallel sub-agents and synthesizing findings. Use when the user wants to understand how code works, find where components live, document architecture, or trace connections between systems.
license	Apache-2.0
compatibility	Designed for GitHub Copilot CLI
metadata	{"author":"humanlayer","version":"1.0","original-source":"https://github.com/humanlayer/humanlayer"}

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 skill 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
• Use the view tool to read entire files 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
• Identify specific components, patterns, or concepts to investigate
• Use the available todo/task-tracking mechanism to track all subtasks
• Consider which directories, files, or architectural patterns are relevant

3. Spawn parallel sub-agent tasks for comprehensive research

Use the task tool with appropriate agent types to research different aspects concurrently:

For codebase research, use these agent types:

• explore agent - Use to find WHERE files and components live, and to understand HOW specific code works

  • Example prompts:
    • "Find all files related to authentication in this codebase"
    • "Explain how the database connection pooling works in src/db/"
    • "Find examples of the repository pattern in this codebase"

• codebase-analyzer agent (if available) - Use for deeper analysis of specific components

For web research (only if user explicitly asks):

• Use the available web search/fetch tools for external documentation and resources
• Include links in your final report

The key is to use these agents intelligently:

• Run multiple agents in parallel when they're searching for different things
• Start with exploration to find what exists
• Then use analysis on the most promising findings to document how they work
• Each agent knows its job - just tell it what you're looking for
• Remind agents they are documenting, not evaluating or improving

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
• Answer the user's specific questions with concrete evidence

5. Gather metadata for the research document

Run these commands to collect metadata:

git rev-parse --short HEAD 2>/dev/null || echo "not-a-git-repo"
git branch --show-current 2>/dev/null || echo "unknown"
basename "$(git rev-parse --show-toplevel 2>/dev/null)" || basename "$PWD"
date -u +"%Y-%m-%dT%H:%M:%SZ"

6. Generate research document

Structure the document with YAML frontmatter followed by content:

---
date: [Current date and time in ISO format]
researcher: copilot
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
---

# Research: [User's Question/Topic]

**Date**: [Current date and time]
**Git Commit**: [Current commit hash]
**Branch**: [Current branch name]
**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`)
- 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]

## Open Questions

[Any areas that need further investigation]

7. Add GitHub permalinks (if applicable)

• Check if on main branch or if commit is pushed
• If on main/master or pushed, generate GitHub permalinks:

  gh repo view --json owner,name 2>/dev/null
  

• Create permalinks: https://github.com/{owner}/{repo}/blob/{commit}/{file}#L{line}
• Replace local file references with permalinks in the document

8. 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

9. Handle follow-up questions

• If the user has follow-up questions, append to the same research document
• Update the frontmatter last_updated field
• Add a new section: ## Follow-up Research [timestamp]
• Spawn new sub-agents as needed for additional investigation

Important Notes

• Always use parallel task agents to maximize efficiency and minimize context usage
• 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
• 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