Agent Skills: Research Codebase Workflow

Research Codebase Workflow

Overview

Conduct comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings into a structured research document.

Type: FLEXIBLE - Adapt research depth and agent spawning to the complexity of the question.

Output: research.md with YAML frontmatter containing findings, code references, and architecture insights.

When to Use

Use this workflow when:

• Exploring unfamiliar parts of a codebase
• Preparing context for implementation planning
• Understanding how existing features work
• Answering architectural questions
• Gathering context before creating PRDs or implementation plans

Announce at start: "I'm using the research-codebase workflow to investigate this thoroughly."

Initial Setup

When this workflow 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

Step 1: Read Directly Mentioned Files First

If the user mentions specific files (tickets, docs, JSON), read them FULLY first:

• 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

Step 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
• 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

Step 3: Spawn Parallel Sub-Agent Tasks

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

Step 4: Wait and Synthesize

• IMPORTANT: Wait for ALL sub-agent tasks to complete before proceeding
• Compile all sub-agent results
• Prioritize live codebase findings as primary source of truth
• 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

Step 5: Gather Metadata

Generate all relevant metadata:

• Filename: research.md
• Current date and time with timezone in ISO format
• Current commit hash via: git rev-parse HEAD
• Current branch via: git branch --show-current
• Repository name (infer from git remote or env)

Step 6: Generate Research Document

Structure the document with YAML frontmatter followed by content:

---
date: [Current date and time with timezone in ISO format]
researcher: opencode
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: opencode
---

# Research: [User's Question/Topic]

**Date**: [Current date and time with timezone from step 5]
**Researcher**: opencode
**Git Commit**: [Current commit hash from step 5]
**Branch**: [Current branch name from step 5]
**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]

## Open Questions
[Any areas that need further investigation]

Step 7: 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

Step 8: 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
• Add last_updated_note: "Added follow-up research for [brief description]"
• 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
• 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/GitLab 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

Critical Ordering

Follow the numbered steps exactly:

1. ALWAYS read mentioned files first before spawning sub-tasks (step 1)
2. ALWAYS wait for all sub-agents to complete before synthesizing (step 4)
3. ALWAYS gather metadata before writing the document (step 5 before step 6)
4. NEVER write the research document with placeholder values

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

Integration with Other Workflows

This workflow naturally flows into:

1. workflow-create-prd - Formalize requirements from research findings
2. workflow-create-plan - Create implementation plan using research as input