201 lines
9.8 KiB
Markdown
201 lines
9.8 KiB
Markdown
# Research Codebase
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You are tasked with conducting comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesizing their findings.
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## Initial Setup:
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When this command is invoked, if you already think you know what the user wants to research, confirm that with the user. If you do not know, respond with:
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```
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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.
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```
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Then wait for the user's research query.
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## Steps to follow after receiving the research query:
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1. **Read any directly mentioned files first:**
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- If the user mentions specific files (tickets, docs, JSON), read them FULLY first
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- **IMPORTANT**: Use the Read tool WITHOUT limit/offset parameters to read entire files
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- **CRITICAL**: Read these files yourself in the main context before spawning any sub-tasks
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- This ensures you have full context before decomposing the research
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2. **Analyze and decompose the research question:**
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- Break down the user's query into composable research areas
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- Take time to ultrathink about the underlying patterns, connections, and architectural implications the user might be seeking
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- Identify specific components, patterns, or concepts to investigate
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- Create a research plan using TodoWrite to track all subtasks
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- Consider which directories, files, or architectural patterns are relevant
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3. **Spawn parallel sub-agent tasks for comprehensive research:**
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- Create multiple Task agents to research different aspects concurrently
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- We now have specialized agents that know how to do specific research tasks:
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**For codebase research:**
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- Use the `workflow-tools:codebase-locator` agent to find WHERE files and components live
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- Use the `workflow-tools:codebase-analyzer` agent to understand HOW specific code works
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- Use the `workflow-tools:codebase-pattern-finder` agent if you need examples of similar implementations
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**For `working-notes/` directory:**
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- Use the `workflow-tools:notes-locator` agent to discover what documents exist about the topic
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- Use the `workflow-tools:notes-analyzer` agent to extract key insights from specific documents (only the most relevant ones)
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**For web research:**
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- Use the `workflow-tools:web-search-researcher` agent for external documentation and resources
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- Instruct the agent to return LINKS with their findings, and please INCLUDE those links in your final report
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**For historical context:**
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- Use the `workflow-tools:jira-searcher` agent to search for relevant Jira issues that may provide business context
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- Use the `workflow-tools:git-history` agent to search git history, PRs, and PR comments for implementation context and technical decisions
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The key is to use these agents intelligently:
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- Start with locator agents to find what exists
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- Then use analyzer agents on the most promising findings
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- Run multiple agents in parallel when they're searching for different things
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- Each agent knows its job - just tell it what you're looking for
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- Do NOT write detailed prompts about HOW to search - the agents already know
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4. **Wait for all sub-agents to complete and synthesize findings:**
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- IMPORTANT: Wait for ALL sub-agent tasks to complete before proceeding
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- Compile all sub-agent results (codebase, `working-notes/` findings, and web research)
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- Prioritize live codebase findings as primary source of truth
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- Use `working-notes/` findings as supplementary historical context
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- Connect findings across different components
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- Include specific file paths and line numbers for reference
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- Highlight patterns, connections, and architectural decisions
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- Answer the user's specific questions with concrete evidence
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5. **Gather metadata for the research document:**
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- Filename: `working-notes/{YYYY-MM-DD}_research_[descriptive-name].md`. Use `date '+%Y-%m-%d'` for the timestamp in the filename.
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- Use the `workflow-tools:frontmatter-generator` agent to collect metadata.
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- Wait for the agent to return metadata before proceeding.
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6. **Generate research document:**
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- Use the metadata gathered in the previous step
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- Structure the document with YAML frontmatter followed by content:
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```markdown
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---
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date: [Current date and time with timezone in ISO format]
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git_commit: [Current commit hash]
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branch: [Current branch name]
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repository: [Repository name]
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topic: "[User's Question/Topic]"
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tags: [research, codebase, relevant-component-names]
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last_updated: [Current date in YYYY-MM-DD format]
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---
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# Research: [User's Question/Topic]
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**Date**: [Current date and time with timezone from step 4]
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**Git Commit**: [Current commit hash from step 4]
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**Branch**: [Current branch name from step 4]
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**Repository**: [Repository name]
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## Research Question
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[Original user query]
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## Summary
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[High-level findings answering the user's question]
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## Detailed Findings
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### [Component/Area 1]
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- Finding with reference ([file.ext:line](link))
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- Connection to other components
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- Implementation details
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### [Component/Area 2]
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...
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## Code References
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- `path/to/file.py:123` - Description of what's there
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- `another/file.ts:45-67` - Description of the code block
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## Architecture Insights
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[Patterns, conventions, and design decisions discovered]
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## Historical Context
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[Relevant insights from `working-notes/` directory and any relevant Jira issues. Include references for all insights.]
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## Related Research
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[Links to past research documents in `working-notes/`]
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## Open Questions
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[Any areas that need further investigation]
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```
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7. **Automatic External Review (Internal Quality Check):**
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- Check for external review environment variable: `bash -c 'echo ${CLAUDE_EXTERNAL_REVIEW_COMMAND:-NOT_SET}'`
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- **If environment variable IS set:**
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- Invoke the external review command with the comprehensive review prompt (from review-doc.md step 4) for the just-created research document
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- **Critically analyze the feedback with a VERY skeptical lens**:
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- Dismiss theoretical concerns that don't apply to this specific research
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- Ignore feedback that adds unnecessary complexity
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- Only identify feedback that reveals genuine gaps or errors
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- **Silently address ONLY critical issues**:
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- Fix any factual errors or missing critical information in the document
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- Add only truly important missing considerations
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- Make minimal, focused updates - do NOT implement every suggestion
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- **Do NOT present the review to the user** - this is an internal quality check
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- **If environment variable is NOT set:**
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- Skip this step and proceed to adding GitHub permalinks
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8. **Add GitHub permalinks (if applicable):**
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- Check if on main branch or if commit is pushed: `git branch --show-current` and `git status`
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- If on main/master or pushed, generate GitHub permalinks:
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- Get repo info: `gh repo view --json owner,name`
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- Create permalinks: `https://github.com/{}/{repo}/blob/{commit}/{file}#L{line}`
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- Replace local file references with permalinks in the document
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9. **Present findings:**
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- Present a concise summary of findings to the user
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- Include key file references for easy navigation
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- Ask if they have follow-up questions or need clarification
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10. **Handle follow-up questions:**
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- If the user has follow-up questions, append to the same research document
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- Update the frontmatter fields `last_updated` and `last_updated_by` to reflect the update
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- Add `last_updated_note: "Added follow-up research for [brief description]"` to frontmatter
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- Add a new section: `## Follow-up Research [timestamp]`
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- Spawn new sub-agents as needed for additional investigation
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- Continue updating the document
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## Important notes:
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- Always use parallel Task agents to maximize efficiency and minimize context usage
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- Always run fresh codebase research - never rely solely on existing research documents
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- The `working-notes/` directory provides historical context to supplement live findings
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- Focus on finding concrete file paths and line numbers for developer reference
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- The research document should NOT include any references to how long things will take (i.e., Phase 1 will take 2 days)
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- Research documents should be self-contained with all necessary context
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- Each sub-agent prompt should be specific and focused on read-only operations
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- Consider cross-component connections and architectural patterns
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- Include temporal context (when the research was conducted)
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- Link to GitHub when possible for permanent references
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- Keep the main agent focused on synthesis, not deep file reading. Use subagents for any deep file reading.
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- Encourage sub-agents to find examples and usage patterns, not just definitions
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- Explore all of `working-notes/` directory, not just research subdirectory
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- **File reading**: Always read mentioned files FULLY (no limit/offset) before spawning sub-tasks
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- **Critical ordering**: Follow the numbered steps exactly
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- ALWAYS read mentioned files first before spawning sub-tasks (step 1)
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- ALWAYS wait for all sub-agents to complete before synthesizing (step 4)
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- ALWAYS gather metadata before writing the document (step 5 before step 6)
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- NEVER write the research document with placeholder values
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- This ensures paths are correct for editing and navigation
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- **Frontmatter consistency**:
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- Always include frontmatter at the beginning of research documents
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- Keep frontmatter fields consistent across all research documents
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- Update frontmatter when adding follow-up research
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- Use snake_case for multi-word field names (e.g., `last_updated`, `git_commit`)
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- Tags should be relevant to the research topic and components studied
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