Research Codebase

Research Codebase

!accelerator config context --skill research-codebase --fail-safe !accelerator config agents --fail-safe

If no "Agent Names" section appears above, use these defaults: accelerator:reviewer, accelerator:codebase-locator, accelerator:codebase-analyser, accelerator:codebase-pattern-finder, accelerator:documents-locator, accelerator:documents-analyser, accelerator:web-search-researcher.

Research directory: !accelerator config path research_codebase --fail-safe Plans directory: !accelerator config path plans --fail-safe Decisions directory: !accelerator config path decisions --fail-safe

You are tasked with conducting comprehensive research across the codebase to answer user questions by spawning parallel sub-agents and synthesising 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 analyse it thoroughly by exploring relevant components and connections.

Then wait for the user's research query.

Steps to follow after receiving the research query:

Step 1: Read any directly mentioned files first

• If the user mentions specific files (work items, 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

Step 2: Analyse 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

Step 3: Spawn parallel sub-agent tasks for comprehensive research

• Create multiple Task agents to research different aspects concurrently
• We now have specialised agents that know how to do specific research tasks:

For codebase research:

• Use the {codebase locator agent} agent to find WHERE files and components live
• Use the {codebase analyser agent} agent to understand HOW specific code works
• Use the {codebase pattern finder agent} agent if you need examples of similar implementations

For meta directory:

• Use the {documents locator agent} agent to discover what documents exist about the topic in the configured research, plans, and decisions directories (shown above)
• Use the {documents analyser agent} agent to extract key insights from specific documents (only the most relevant ones)

For web research (only if user explicitly asks):

• Use the {web search researcher agent} 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 analyser 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 for all sub-agents to complete and synthesise findings

• IMPORTANT: Wait for ALL sub-agent tasks to complete before proceeding
• Compile all sub-agent results (both codebase and document findings)
• Prioritise live codebase findings as primary source of truth
• Use document findings as supplementary historical context
• Connect findings across different components
• Include specific file paths and line numbers for reference
• Verify all output paths are correct
• Highlight patterns, connections, and architectural decisions
• Answer the user's specific questions with concrete evidence

Step 5: Gather metadata for the research document

• Run accelerator corpus metadata derive to generate all relevant metadata.
• Filename: write to the configured research directory (shown above) using
  • Format: YYYY-MM-DD-ENG-XXXX-description.md where:
    • YYYY-MM-DD is today's date
    • ENG-XXXX is the work item number (omit if no work item)
    • description is a brief kebab-case description of the research topic
  • Examples:
    • With work item: 2025-01-08-ENG-1478-parent-child-tracking.md
    • Without work item: 2025-01-08-authentication-flow.md

Step 6: Populate frontmatter and generate the research document

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

!accelerator config template codebase-research --fail-safe

Before writing the artifact file, substitute every field below with the indicated value, using the helper output captured in step 5:

• type: ← codebase-research
• id: ← the filename stem (the file path computed above without .md), always quoted as a YAML string
• title: ← Research: {User's Question/Topic}
• date: ← the Current Date/Time (UTC): value
• author: ← the author resolved per the standard chain (config → VCS user → prompt)
• producer: ← research-codebase
• status: ← complete
• revision: ← the Current Revision: value
• repository: ← the Repository Name: value
• last_updated: ← the same Current Date/Time (UTC): value
• last_updated_by: ← the same value resolved for author
• schema_version: ← 1 (bare integer)

Optional linkage/foreign-ref keys are omit-by-default: the template shows each as ""/[], but write a key into the artifact only when it has a value, and omit it entirely otherwise (do not carry the empty placeholder through).

• parent: ← the work item this research supports, as a typed-linkage ref ("work-item:NNNN"). Fill when the research has an owning work item; otherwise omit the key.
• relates_to: ← list of typed-linkage refs to related research (["codebase-research:NNNN", ...]). Fill when related artifacts are explicit; otherwise omit the key.
• work_item_id: ← the linked work item's full ID (quoted). Fill when the research is linked to a work item; otherwise omit the key.

Validate the frontmatter: after writing, run corpus frontmatter validate over the research document you just wrote:

accelerator corpus frontmatter validate --file <path>

If it exits non-zero, the research document violates the canonical frontmatter standard; report the emitted violation and fix the frontmatter before completing.

Step 7: Add GitHub permalinks (if applicable)

• Check if on main branch or if commit is pushed: git branch --show-current and git status
• If on main/master or pushed, generate GitHub permalinks:
  • Get repo info: gh repo view --json owner,name
  • Create permalinks: https://github.com/{owner}/{repo}/blob/{commit}/{file}#L{line}
• Replace local file references with permalinks in the document

Step 8: 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 9: 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 to reflect the update
• Add last_updated_note: "Added follow-up research for [brief description]" to frontmatter
• 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 maximise efficiency and minimise context usage
• Always run fresh codebase research - never rely solely on existing research documents
• The configured document directories provide historical context to supplement live findings
• 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 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
• Explore all configured document directories (research, plans, decisions)
• 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 synthesising (step 4)
  • ALWAYS gather metadata before writing the document (step 5 before step 6)
  • 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

!accelerator config instructions research-codebase --fail-safe