research

name	research
description	Answer structured research questions via targeted parallel analysis agents. Consumes question artifacts from discover. Produces research documents in thoughts/shared/research/. Second stage of the research pipeline — always requires a questions artifact.
argument-hint	["path to discover artifact"]

Questions Source

If the user has not already provided a specific discover artifact path, ask them for it before proceeding. Their input will appear as a follow-up paragraph after this skill body.

Research

You are tasked with answering structured research questions by spawning targeted analysis agents and synthesizing their findings into a comprehensive research document. This skill consumes questions artifacts produced by the discover skill.

Step 1: Read Questions Artifact

1. Determine input:

   Questions artifact provided (path to a .md file in thoughts/):

   • Read the questions artifact FULLY using the Read tool WITHOUT limit/offset
   • Extract: Discovery Summary, Questions (dense paragraphs), frontmatter metadata (topic, tags)
   • The Discovery Summary provides the file landscape overview — no need to re-discover

   No arguments provided:

   I'll answer research questions from a questions artifact. Please provide the path:
   `/skill:research thoughts/shared/questions/YYYY-MM-DD_HH-MM-SS_topic.md`
   
   This skill requires a questions artifact from discover.
   There is no standalone path — run /skill:discover first to produce a questions artifact.
   

   Then wait for input.

2. Read key shared files referenced across multiple questions into main context — especially shared utilities, type definitions, and integration points that multiple questions mention.

3. Analyze question overlap for grouping:

   • Parse all question paragraphs and extract file references from each
   • Identify questions that share 2+ file references — these are candidates for grouping
   • Group related questions together (2-3 questions per group max)
   • Questions with no significant file overlap remain standalone
   • Target: 3-6 agent dispatches total (grouped + standalone)

4. Report chained status:

   [Chained]: Found research questions for "[topic]". [N] questions in [G] groups, [M] shared files.
   

Step 2: Dispatch Analysis Agents

Spawn analysis agents using the Agent tool. All agents run in parallel.

Default agent: codebase-analyzer for all codebase questions. This agent has Read, Grep, Glob, LS — it can trace code paths, find patterns, and analyze integration points.

Exception: Questions that explicitly reference external documentation, web APIs, or third-party libraries → web-search-researcher.

Agent prompt — question-as-prompt:

Each agent receives the dense question paragraph(s) directly as its prompt. The question IS the instruction.

For standalone questions (no grouping):

Research topic: [topic from frontmatter]

Answer the following research question thoroughly with file:line references. Read the files mentioned, trace the code paths described, and provide a complete analysis.

[Full dense question paragraph]

Provide your analysis with exact file:line references. Focus on DEPTH — trace the actual code, don't just locate it.

For grouped questions:

Research topic: [topic from frontmatter]

Answer the following related research questions thoroughly with file:line references. These questions share overlapping code paths — use your cross-question context to provide deeper, more connected analysis.

Question 1: [Full dense question paragraph]

Question 2: [Full dense question paragraph]

For each question, provide your analysis with exact file:line references. Note connections between the questions where the same code serves multiple roles. Focus on DEPTH — trace the actual code, don't just locate it.

Precedent sweep (always spawn): Spawn one precedent-locator agent alongside the question agents: "Find similar past changes involving [list key files from Discovery Summary]. Search git log for commits that touched these files, similar commit messages, and follow-up fixes. Research topic: [original query]."

This agent runs with full knowledge of discovered files — its findings go into Precedents & Lessons, not tied to a specific question.

Wait for ALL agents to complete before proceeding.

Step 3: Synthesize and Checkpoint

1. Compile findings:

   • Match each agent's response to the question(s) it answered
   • Cross-reference findings across questions — look for patterns, conflicts, and connections
   • Prioritize live codebase findings as primary source of truth
   • Use thoughts/ findings as supplementary historical context
   • Include specific file paths and line numbers
   • Build Code References as jump-table entries for the planner, not narrative (file:startLine-endLine format)
   • No multi-line code blocks (>3 lines) — use file:line refs + prose. No implementation recipes — facts only.
   • No artifact summaries — link plans/designs in Historical Context, don't summarize their contents. Research describes current codebase state.

2. Developer checkpoint — grounded questions one at a time:

   Start with grounded questions referencing real findings with file:line evidence. Ask ONE question at a time, waiting for the answer before the next. Use a ❓ Question: prefix. Each question must pull NEW information from the developer — not confirm what you already found:

   Every question MUST embed at least one file:line reference in the question text — not just in surrounding context. Examples:

   • "❓ Question: src/events/orders.ts:45-67 has 3 event hooks but no error recovery path. Is there a retry mechanism elsewhere I'm not seeing?"
   • "❓ Question: Pattern-finder found manual mapping at src/services/OrderService.ts:45 (8 uses) vs AutoMapper at src/services/UserService.ts:12 (2 uses). Which should new code follow?"
   • "❓ Question: Precedent commit abc123 required a follow-up fix at src/handlers/key.ts:158 for connection leak. Should we account for that pattern in this design?"

   Anti-patterns — NEVER ask these:

   • "Is this research to understand X or prepare for Y?" — confirmatory, pulls zero new information
   • "Does this look correct?" / "Should I continue?" — asks developer to validate YOUR work instead of providing NEW context
   • Questions without file:line — if you can't ground it in code, it's not a research question

   Question patterns by finding type:

   • Pattern conflict: "Found 2 implementations of [X] — which is canonical?" with options citing file:line + occurrence count
   • Scope boundary: "Question [N] references files [A,B,C] but analysis shows [D] is the real integration point. Extend scope?" with yes/no + "describe what I missed"
   • Priority override: "Questions Q1 and Q2 have competing implications for [area]. Which is load-bearing?" with options
   • Integration ambiguity: "Found no connection between [X] and [Y]. Is there an indirect path?" (free-text — can't predict the answer)

   Choosing question format:

   • ask_user_question tool — when your question has 2-4 concrete options from code analysis (pattern conflicts, integration choices, scope boundaries, priority overrides). The user can always pick "Other" for free-text. Example:

     Use the ask_user_question tool with the following question: "Found 2 patterns for retry logic — which is canonical?". Header: "Pattern". Options: "Event-sourced retry (Recommended)" (src/events/orders.ts:45-67 — 3 hooks, matches precedent commit abc123); "Direct retry loop" (src/services/OrderService.ts:112 — single use, no event traceability).

   • Free-text with ❓ Question: prefix — when the question is open-ended and options can't be predicted (discovery, "what am I missing?", corrections). Example: "❓ Question: src/events/orders.ts:45-67 has 3 event hooks but no error recovery path. Is there a retry mechanism elsewhere I'm not seeing?"

   Anti-pattern — do NOT dump a verbose paragraph mixing analysis with a trailing question:

   ❌ "The premise inversion is load-bearing for prioritization — it means Site A is a no-op, and the real bloat only hits general-purpose dispatches. Given this, where is the bloat actually landing? Do your skills dispatch named bundled agents — in which case append-mode is irrelevant — or general-purpose — in which case it IS the dominant source?"

   ✅ Extract the 2 concrete options and call ask_user_question: "Where is the prompt bloat landing?". Header: "Bloat source". Options: "Named bundled agents (Recommended)" (Skills dispatch codebase-analyzer etc. — prompt_mode: "replace", no parent inheritance); "General-purpose agent" (default-agents.ts:11-28 — promptMode: "append", inherits full parent prompt).

   Batching: When you have 2-4 independent questions (answers don't depend on each other), you MAY batch them in a single ask_user_question call. Keep dependent questions sequential.

   CRITICAL: Ask ONE question at a time. Wait for the answer before asking the next. Lead with your most significant finding.

3. Present compiled scan (under 30 lines):

   Task: [one-line summary]
   Scope: [N files across M layers, K integration points]
   
   [Layer name] — [key files and what they do]
   [Layer name] — [key files and what they do]
   Integration — [N inbound, M outbound, K wiring. Top concern if any]
   History — [N relevant docs. Key insight if any]
   
   Best template: [implementation to model after]
   Precedents — [N similar changes found. Top lesson if any]
   Inconsistencies: [count] found ([short names])
   

   Wait for the developer's response before proceeding.

4. Incorporate developer input:

   Classify each response:

   Corrections (e.g., "skip the job scheduler", "use CreateProduct not GetUser"):

   • Incorporate directly into synthesis. Record in Developer Context.

   New areas (e.g., "you missed the events module"):

   • Spawn targeted rescan: codebase-locator + codebase-analyzer on the new area (max 2 agents).
   • Merge results into synthesis. Record in Developer Context.

   Decisions (e.g., "yes, hook into that event chain"):

   • Record in Developer Context. Remove corresponding item from Open Questions.

   Scope/focus (e.g., "focus on API layer, UI is out of scope"):

   • Record in Developer Context.

   After incorporating all input, proceed to Step 4.

Step 4: Write Research Document

1. Determine metadata:

   • Filename: thoughts/shared/research/YYYY-MM-DD_HH-MM-SS_[topic].md
     • YYYY-MM-DD_HH-MM-SS: Current date and time
     • topic: Brief kebab-case description
   • Repository name: from git root basename, or current directory basename if not a git repo
   • Use the git branch and commit from the git context injected at the start of the session (or run git branch --show-current / git rev-parse --short HEAD directly)
   • Researcher: use the User from the git context injected at the start of the session (fallback: "unknown")
   • If metadata unavailable: use "unknown" for commit/branch

2. Write the research document — this document is compressed context for a new session. Include everything the planner needs to make architectural decisions without re-researching:

   ---
   date: [Current date and time with timezone in ISO format]
   researcher: [User from injected git context]
   git_commit: [Current commit hash]
   branch: [Current branch name]
   repository: [Repository name]
   topic: "[User's Research Topic]"
   tags: [research, codebase, relevant-component-names]
   status: complete
   questions_source: "[path to questions artifact]"
   last_updated: [Current date in YYYY-MM-DD format]
   last_updated_by: [User from injected git context]
   ---
   
   # Research: [User's Research Topic]
   
   ## Research Question
   [Original user query from questions artifact]
   
   ## Summary
   [High-level findings answering the user's question]
   
   ## Detailed Findings
   
   ### [Component/Area 1]
   - Finding with reference (`file.ext:line`)
   - 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
   
   ## Integration Points
   [All connections to the researched area. Enumerate each consumer, dependency, and wiring point with file:line. Source from the questions artifact's Discovery Summary + new connections found by analysis agents.]
   
   ### Inbound References
   - `path/to/consumer.ext:line` — [What references the component and how]
   
   ### Outbound Dependencies
   - `path/to/dependency.ext:line` — [What the component depends on]
   
   ### Infrastructure Wiring
   - `path/to/config.ext:line` — [DI, routes, events, jobs, middleware]
   
   ## Architecture Insights
   [Patterns, conventions, and design decisions discovered]
   
   ## Precedents & Lessons
   [N] similar past changes analyzed. Key commits: `hash` (description).
   
   - [Composite lesson 1 — with relevant `commit hash` inline]
   - [Composite lesson 2]
   
   ## Historical Context (from thoughts/)
   [Links only — one line per doc, no summaries of their contents]
   - `thoughts/shared/something.md` — [one-line description of what this doc covers]
   ## Developer Context
   **Q (`file.ext:line`): [Question grounded in specific code reference]**
   A: [Developer's answer]
   
   ## Related Research
   - Questions source: `[path to questions artifact]`
   - [Links to other research documents]
   
   ## Open Questions
   [Only questions NOT resolved during checkpoint]
   

Step 5: Present and Chain

Research document written to:
`thoughts/shared/research/[filename].md`

[N] questions answered, [M] findings across [K] files.

Please review and let me know if you have follow-up questions.

When ready:
`/skill:design thoughts/shared/research/[filename].md`

Step 6: Handle Follow-ups

• If the user has follow-up questions, append to the same research document
• Update frontmatter: last_updated and last_updated_by
• Add last_updated_note: "Added follow-up research for [brief description]" to frontmatter
• Add section: ## Follow-up Research [timestamp]
• Spawn new analysis agents as needed

Important Notes

• Analysis only: This skill answers questions. It does NOT discover what to ask — that's discover's job.
• Always chained: This skill requires a questions artifact from discover. There is no standalone path.
• Grouped dispatch: Related questions are batched per agent based on file overlap. Default agent: codebase-analyzer. This reduces token waste from redundant file reads and lets agents build cross-question context.
• Downstream compatible: Research documents feed directly into design and plan — the same Code References / Integration Points / Architecture Insights sections they expect.
• File reading: Always read the questions artifact FULLY (no limit/offset) before dispatching agents
• Critical ordering: Follow the numbered steps exactly
  • ALWAYS read the questions artifact first (Step 1)
  • ALWAYS analyze question overlap for grouping (Step 1)
  • ALWAYS wait for all agents to complete (Step 2)
  • ALWAYS run developer checkpoint before writing (Step 3)
  • ALWAYS gather metadata before writing (Step 4)
  • NEVER write the document with placeholder values
• Frontmatter consistency: Always include frontmatter, use snake_case fields, include questions_source
• CC auto-loads CLAUDE.md files when agents read files in a directory — no need to scan for them explicitly