Skill Research Process
Systematic, scalable approach for building comprehensive Claude Code skills using parallel research agents. Use this when a skill requires extensive documentation gathering from official sources.
Process Overview
Stage 1: Initialize → Categorization agent creates TODO checklist
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Gate 1: Verify categories are distinct and complete
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Stage 2: Research → Parallel agents populate references/{category}/
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Gate 2: Anti-hallucination checkpoint (verify all claims cited)
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Stage 3: Integrate → Update SKILL.md, validate structure
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Gate 3: Final validation (links work, quality standards met)
Pre-Requisites
-
Activate skill-creator for structure guidance:
Skill(skill: "plugin-creator:skill-creator") -
Read CLAUDE.md for verification requirements
Stage 1: Initialize Skill Structure
Objective: Create base skill directory and identify documentation categories.
Steps
-
Initialize skill directory:
plugins/plugin-creator/skills/skill-creator/scripts/init_skill.py <skill-name> --path <output-directory> -
Launch categorization agent - see Agent Prompts
-
Output:
{skill-name}.TODO.mdwith categorized checklist
Quality Gate 1: Category Verification
Before proceeding, verify:
- [ ] Categories are distinct (no overlap)
- [ ] Each category is specific enough to guide focused research
- [ ] 5-10 categories total (fewer for simple tools, more for complex)
- [ ] Categories cover the tool's full scope
If categories overlap: Merge or redefine boundaries before Stage 2.
Stage 2: Parallel Category Research
Objective: Launch concurrent research agents to build reference documentation.
Steps
- Read TODO categories from
{skill-name}.TODO.md - Launch concurrent Task agents (one per category) with
run_in_background: true - Each agent outputs to
./references/{category}/
See Research Agent Prompt for template.
Parallel Execution
Launch all agents in a single message with multiple Task calls:
Agent(subagent_type: "general-purpose", description: "Research Category A", run_in_background: true, ...)
Agent(subagent_type: "general-purpose", description: "Research Category B", run_in_background: true, ...)
Quality Gate 2: Anti-Hallucination Checkpoint
MANDATORY before Stage 3. For each category, verify:
- [ ] Every factual claim has a cited source (URL + access date)
- [ ] No claims based on training data knowledge
- [ ] Sources are authoritative (official docs > blogs > forums)
- [ ] Code examples are from official sources or tested
- [ ] Uncertain information marked explicitly as "unverified"
Citation Format Required:
According to the official documentation (https://example.com/docs, accessed 2026-02-01), ...
If citation missing: Research agent must add source or mark as "NOT_VERIFIED: [claim]".
Stage 3: Integration
Objective: Update SKILL.md with category links and finalize.
Steps
- Update
./SKILL.mdwith links to each category'sindex.md - Verify SKILL.md body ≤5k words
- Validate structure
Quality Gate 3: Final Validation
Run validation:
plugins/plugin-creator/skills/skill-creator/scripts/package_skill.py <skill-path>
Verify:
- [ ] All markdown links resolve (use Read tool to verify)
- [ ] All index.md files contain working links
- [ ] All TODO items from Stage 1 have corresponding reference files
- [ ] Skill follows skill-creator guidelines
Error Recovery
MCP Tools Unavailable
Fallback strategy when MCP tools not available:
- WebFetch + WebSearch: For discovery and overview
- GitHub CLI (
gh): For repository metadata, issues, releases - Clone + Read: Clone repo locally, use Read tool for code analysis
Research Agent Fails
If an agent fails or times out:
- Check output file with Read tool (background agents write to file)
- Resume with remaining work if partial results exist
- Re-launch with narrower scope if timeout
Incomplete Source Documentation
If official docs are incomplete:
- Document what IS available with citations
- Mark gaps explicitly: "Official documentation does not cover [topic]"
- Use GitHub issues/discussions as secondary sources (with citation)
- Never fill gaps with training data assumptions
MCP Tool Selection
| Tool | Fidelity | Use When | | ------------ | -------- | ---------------------------------------------- | | WebFetch | Low | Scoping only. NEVER for implementation details | | mcpexa* | Medium | Code snippets, documentation extraction | | mcpRef* | High | Authoritative, verbatim documentation |
See MCP Tool Usage Guide for details.
Key Principles
| Principle | Rule |
| ---------------------- | -------------------------------------------- |
| Progressive Disclosure | SKILL.md ≤5k words; details in references/ |
| Parallel Execution | Launch all category agents in single message |
| Citation Required | Every claim needs source + access date |
| No Training Data | Only document what sources confirm |
| Relative Paths | All links use ./ prefix |
Success Checklist
Before finalizing:
- [ ] All quality gates passed (1, 2, 3)
- [ ] Every factual claim has citation with access date
- [ ] No speculation or training-data-based claims
- [ ] All links use
./relative paths - [ ] Categories are distinct (no overlap)
- [ ] SKILL.md ≤5k words
- [ ] Each category has
index.mdwith working links - [ ] Validation script passes
Agent Team Alternative for Stage 2
When Stage 2 (category research) involves 3+ independent categories where findings from one category inform or challenge another, consider agent teams instead of sequential subagents.
When Agent Teams Apply
A category research workflow is a candidate for agent teams when ALL of these are true:
- 3+ independent categories to research (enough parallelism to justify coordination overhead)
- Categories benefit from cross-communication (findings from one category inform or challenge another)
- No shared file mutations (each teammate owns different category files)
- Result is a synthesis, not a concatenation (value comes from combining, deduplicating, or reconciling findings across categories)
When Subagents Suffice
A category research workflow is NOT a candidate for agent teams when:
- Only 1-2 categories (subagent overhead is lower)
- Categories are fully independent with no cross-communication need (subagents suffice)
- Result is just collecting N outputs (no synthesis step)
- Work is sequential (each step depends on the previous)
Reference
See Agent Teams Documentation for complete criteria, architecture, and usage patterns.
SOURCE: Lines 27-39 of agent-teams.md (accessed 2026-02-06)
References
- Agent Prompt Templates
- MCP Tool Usage Guide
- Gaps Analysis - Known limitations and improvement opportunities