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
    ↓
Gate 1: Verify categories are distinct and complete
    ↓
Stage 2: Research → Parallel agents populate references/{category}/
    ↓
Gate 2: Anti-hallucination checkpoint (verify all claims cited)
    ↓
Stage 3: Integrate → Update SKILL.md, validate structure
    ↓
Gate 3: Final validation (links work, quality standards met)

Pre-Requisites

1. Activate skill-creator for structure guidance:

   Skill(skill: "plugin-creator:skill-creator")

2. Read CLAUDE.md for verification requirements

Stage 1: Initialize Skill Structure

Objective: Create base skill directory and identify documentation categories.

Steps

1. Initialize skill directory:

   plugins/plugin-creator/skills/skill-creator/scripts/init_skill.py <skill-name> --path <output-directory>

2. Launch categorization agent - see Agent Prompts

3. Output: {skill-name}.TODO.md with 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

1. Read TODO categories from {skill-name}.TODO.md
2. Launch concurrent Task agents (one per category) with run_in_background: true
3. 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

1. Update ./SKILL.md with links to each category's index.md
2. Verify SKILL.md body ≤5k words
3. 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:

1. WebFetch + WebSearch: For discovery and overview
2. GitHub CLI (gh): For repository metadata, issues, releases
3. Clone + Read: Clone repo locally, use Read tool for code analysis

Research Agent Fails

If an agent fails or times out:

1. Check output file with Read tool (background agents write to file)
2. Resume with remaining work if partial results exist
3. Re-launch with narrower scope if timeout

Incomplete Source Documentation

If official docs are incomplete:

1. Document what IS available with citations
2. Mark gaps explicitly: "Official documentation does not cover [topic]"
3. Use GitHub issues/discussions as secondary sources (with citation)
4. Never fill gaps with training data assumptions

MCP Tool Selection

See MCP Tool Usage Guide for details.

Key Principles

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.md with 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:

1. 3+ independent categories to research (enough parallelism to justify coordination overhead)
2. Categories benefit from cross-communication (findings from one category inform or challenge another)
3. No shared file mutations (each teammate owns different category files)
4. 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