/research_codebase_nt

description: Document codebase as-is without evaluation or recommendations
model: opus

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 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
• 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

5. **Gather metadata for the research document:**

• Run Bash() tools 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 metadata]
     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 metadata]
     **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]
     - Descri