Overview
Critical Constraint
Initial Response
Available Sub-Agents
Research Workflow
Agent Selection Guide
- Question Type → Agent Mapping
- Complex Questions
Best Practices
Example Research Questions

Overview

This skill enables comprehensive codebase research through parallel sub-agent orchestration. It decomposes research questions into focused sub-tasks, executes them in parallel for efficiency, and synthesizes findings into structured research documents.

Critical Constraint

THE ONLY JOB IS TO DOCUMENT AND EXPLAIN THE CODEBASE AS IT EXISTS TODAY.

Do NOT:

Suggest improvements unless explicitly requested
Perform uninvited root cause analysis
Propose enhancements or optimizations
Critique implementation choices or identify problems
Recommend refactoring or architectural changes

DO:

Describe what exists
Explain where functionality lives
Document how components work
Map component interactions and dependencies

Initial Response

When this skill is invoked, respond:

"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."

Available Sub-Agents

Codebase Agents

Agent	Purpose	When to Use
`codebase-locator`	Finds files by topic/feature	Need to locate all files related to a feature
`codebase-analyzer`	Traces implementation with file:line refs	Need to understand how code works
`codebase-pattern-finder`	Finds code patterns with examples	Need to see how patterns are implemented

Documentation Agents

Agent	Purpose	When to Use
`docs-locator`	Finds docs, ADRs, design documents	Need to find documentation about a topic
`docs-analyzer`	Extracts decisions and specs from docs	Need to understand documented decisions

External Research

Agent	Purpose	When to Use
`web-search-researcher`	Researches external documentation	Explicitly requested or need external context

Research Workflow

Step 1: Read Mentioned Files

Read any files directly mentioned by the user completely (without limit/offset parameters) before spawning sub-tasks. This establishes baseline context.

Step 2: Analyze and Decompose

Analyze the research question considering:

Patterns and architectural implications
Related components and dependencies
Potential sub-questions to explore
Whether documentation exists that provides context

Create a research plan using TodoWrite with specific investigation areas.

Step 3: Spawn Parallel Sub-Agents

Use the Task tool to spawn parallel sub-agents. Match agents to research needs:

For "Where is X? questions:

Task(subagent_type="codebase-locator", prompt="Find all files related to [topic]...")

For "How does X work?" questions:

Task(subagent_type="codebase-analyzer", prompt="Trace the implementation of [feature]...")

For "How should I implement X?" questions:

Task(subagent_type="codebase-pattern-finder", prompt="Find patterns for [type] including examples...")

For "Why was X done this way?" questions:

Task(subagent_type="docs-locator", prompt="Find design docs and ADRs related to [topic]...")
Task(subagent_type="docs-analyzer", prompt="Extract decisions and rationale for [topic]...")

For external documentation needs (only if explicitly requested):

Task(subagent_type="web-search-researcher", prompt="Research [library/API] documentation for [topic]...")

Spawn multiple agents in parallel (single message with multiple Task tool calls) for efficiency.

Step 4: Await and Compile Results

Wait for all sub-agents to complete using AgentOutputTool, then compile results with:

Live codebase findings as primary source
Documentation findings as supplementary context
Historical context from design documents

Step 5: Generate Research Document

Create a structured research document with the following format:

# Research: [Topic]

**Date:** YYYY-MM-DD
**Branch:** [current branch]
**Commit:** [current commit hash]

## Research Question

[The original question being investigated]

## Summary

[2-3 paragraph executive summary of findings]

## Detailed Findings

### [Finding Category 1]

[Detailed explanation with code references]

### [Finding Category 2]

[Detailed explanation with code references]

## Code References

| Component | File | Purpose |
|-----------|------|---------|
| [Name] | `path/to/file.ts:line` | [What it does] |

## Architecture

[How components interact - can include ASCII diagrams]

## Historical Context

[Relevant decisions from documentation, if any]

## Related Files

- `path/to/related1.ts` - [Purpose]
- `path/to/related2.ts` - [Purpose]

## Open Questions

- [Any unresolved questions or areas needing further investigation]

Step 6: Present Findings

Present the research document to the user. For follow-up questions:

Append to the same document structure
Update metadata as needed
Reference previous findings when relevant

Agent Selection Guide

Question Type → Agent Mapping

Question Pattern	Primary Agent	Supporting Agents
"Where is X implemented?"	codebase-locator	-
"How does X work?"	codebase-analyzer	codebase-locator
"How is X typically done here?"	codebase-pattern-finder	codebase-locator
"Why was X designed this way?"	docs-analyzer	docs-locator, codebase-analyzer
"What are all the files for X?"	codebase-locator	-
"Trace the flow of X"	codebase-analyzer	codebase-locator
"Find examples of X"	codebase-pattern-finder	codebase-locator
"What's documented about X?"	docs-locator	docs-analyzer

Complex Questions

For complex questions that span multiple concerns, spawn multiple specialized agents:

Example: "How does authentication work and why was JWT chosen?"

Task(subagent_type="codebase-locator", prompt="Find all auth-related files...")
Task(subagent_type="codebase-analyzer", prompt="Trace auth flow implementation...")
Task(subagent_type="docs-locator", prompt="Find auth design docs and ADRs...")
Task(subagent_type="docs-analyzer", prompt="Extract auth decisions and rationale...")

Best Practices

Parallel Execution

Always spawn multiple sub-agents in parallel when investigating different aspects of the same question. This significantly reduces research time.

Code References

Include precise file:line references for all findings:

src/services/auth.service.ts:45 - Good
the auth service - Insufficient

Stay Factual

Focus on observable facts:

"The function accepts two parameters: userId and options"
"This service is injected in 5 controllers"
"Data flows from controller → service → repository"

Avoid speculation or judgment:

~~"This could be improved by..."~~ - Not allowed unless requested
~~"The naming is inconsistent"~~ - Not allowed unless requested

Scope Management

For broad questions, structure the research in layers:

High-level architecture overview
Component-level details
Implementation specifics

For narrow questions, go directly to specifics with supporting context.

Include Documentation Context

When relevant, include findings from documentation:

ADR decisions that explain "why"
Design documents that provide context
Specifications that define constraints

Example Research Questions

This skill handles questions like:

"How does the authentication flow work?"
"Where is the research pipeline implemented?"
"What files handle API routing?"
"How do the frontend and backend communicate?"
"What database models exist and how are they related?"
"Trace the data flow for a user login"
"How should I implement a new service? Show me patterns."
"Why was this architecture chosen? What was documented?"
"What are the conventions for error handling here?"

codebase-research

Codebase Research

Contents