research-codebase

name: research-codebase description: Conducts comprehensive codebase research using parallel specialized subagents.

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.

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
   • Consider Shopsys architecture implications: Follow Package-First architecture (AGENTS.md ## Monorepo Architecture)
     • Framework packages contain core implementations
     • Project-base for configuration and rare extensions
     • Extension relationships: Project extends Package base classes (when needed)
     • Multi-domain and translation support
     • GraphQL API and storefront integration
   • Identify specific components, patterns, or concepts to investigate
   • Create a research plan using TodoWrite to track all subtasks
   • Consider which monorepo layers and directories 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 documentation research:

   • Use the docs-researcher agent to find relevant architectural guidance, patterns, and best practices from the docs/ folder
   • This agent understands the documentation structure and can provide context for development decisions
   • Especially useful for understanding conventions, configuration examples, and implementation approaches

   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
   • Use the codebase-pattern-finder agent if you need examples of similar implementations

   For pattern validation research:

   • ALWAYS use codebase-pattern-finder to find 3-5 concrete examples of similar implementations
   • Focus on extracting exact namespace imports, method signatures, option configurations
   • Validate form type options, security attribute patterns, interface names
   • Get file:line references for all patterns to verify against framework conventions

   These agents understand the Shopsys architecture (see AGENTS.md for Package-First details):

   • They prioritize packages/framework layers for core implementations
   • They check project-base only for configuration and rare extensions
   • They recognize extension patterns (project extends package)
   • They understand GraphQL, storefront, and multi-domain patterns

   For Jira tickets (if relevant):

   • Use mcp__atlassian__getJiraIssue to get full details of a specific ticket
   • Use mcp__atlassian__searchJiraIssuesUsingJql to find related tickets or historical context

   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

   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

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 focusing on live codebase findings
   • Connect findings across different components and layers (project-base vs packages)
   • Include specific file paths and line numbers for reference
   • Highlight monorepo patterns, inheritance chains, and architectural decisions
   • Note relationships between framework base classes and project extensions
   • Answer the user's specific questions with concrete evidence

5. Gather metadata for the research document:

   • Collect git metadata directly:
     • Current commit: git rev-parse HEAD
     • Current branch: git branch --show-current
     • Repository info: git remote get-url origin
   • Generate filename: docs/research/YYYY-MM-DD-topic-description.md
     • Format: YYYY-MM-DD-topic-description.md where:
       • YYYY-MM-DD is today's date
       • topic-description is a brief kebab-case description of the research
     • Examples:
       • 2025-01-15-product-variant-management.md
       • 2025-01-15-graphql-resolver-patterns.md

6. Generate research document:

   • Use the metadata gathered in step 5
   • Structure the document with YAML frontmatter followed by content:

     ---
     date: [Current date and time with timezone in ISO format]
     git_commit: [Current commit hash]
     branch: [Current branch name]
     repository: "shopsys/shopsys"
     topic: "[User's Question/Topic]"
     tags: [research, codebase, relevant-component-names]
     ---
     
     # Research: [User's Question/Topic]
     
     **Date**: [Current date and time with timezone]
     **Git Commit**: [Current commit hash]
     **Branch**: [Current branch name]
     **Repository**: shopsys/shopsys
     
     ## Research Question
     [Original user query]
     
     ## Summary
     [High-level findings answering the user's question]
     
     ## Detailed Findings
     
     ### Framework Layer (packages/framework/)
     - Finding with reference (`file.php:line`)
     - Base class implementations and patterns
     - Core framework functionality
     
     ### Project Layer (project-base/app/)
     - Custom implementations and extensions
     - Project-specific business logic
     - Inheritance relationships with framework
     
     ### Frontend API Layer (project-base/app/src/FrontendApi/)
     - GraphQL resolvers and mutations
     - API integration patterns
     
     ### Storefront Layer (project-base/storefront/)
     - React components and TypeScript patterns
     - Frontend implementation details
     
     ## Code References
     - `packages/framework/src/Model/Product/ProductFacade.php:45` - Base facade implementation
     - `project-base/app/src/Model/Product/ProductFacade.php:12` - Project facade extension
     - `project-base/storefront/components/Product/ProductDetail.tsx:23` - Frontend component
     
     ## Architecture Insights
     [Monorepo patterns, inheritance chains, and design decisions discovered]
     - Inheritance pattern: Project extends Framework base classes
     - Multi-domain support implementation
     - GraphQL integration between backend and storefront
     
     ## Related Research
     [Links to other research documents in docs/research/]
     
     ## Open Questions
     [Any areas that need further investigation]
     

7. Present findings to user:

   • Save the research document to docs/research/
   • Present a concise summary of findings to the user
   • Include key file references for easy navigation
   • Highlight monorepo patterns and inheritance relationships discovered
   • Ask if they have follow-up questions or need clarification

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 monorepo architectural patterns
• Include temporal context (when the research was conducted)
• Keep the main agent focused on synthesis, not deep file reading
• Encourage sub-agents to find examples and usage patterns, not just definitions
• File reading: Always read mentioned files FULLY (no limit/offset) before spawning sub-tasks
• Critical ordering: Follow the numbered steps exactly
  • ALWAYS read mentioned files first before spawning sub-tasks (step 1)
  • ALWAYS wait for all sub-agents to complete before synthesizing (step 4)
  • ALWAYS gather metadata before writing the document (step 5 before step 6)
  • NEVER write the research document with placeholder values
• Shopsys Architecture awareness (see AGENTS.md):
  • Prioritize framework packages for core implementations and patterns
  • Check project-base for configuration and rare extensions
  • Look for extension patterns (project extends package)
  • Include GraphQL/Frontend API and storefront patterns
  • Note multi-domain and translation support patterns
• Frontmatter consistency:
  • Always include frontmatter at the beginning of research documents
  • Keep frontmatter fields consistent across all research documents
  • Use snake_case for multi-word field names (e.g., git_commit)
  • Tags should be relevant to the research topic and Shopsys components studied