Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Design, schema-generate, and evaluate multi-agent systems with three deterministic tools. The scripts are the workflow — do not freehand an architecture when the planner can score one from requirements.
When NOT to use: Claude Code Workflow-tool automations → workflow-builder; single-agent workflow scaffolds → agent-workflow-designer; multi-agent fan-out at runtime → agenthub.
| Choose | When | Watch out for |
|---|---|---|
| Single agent | One bounded task, < ~5 tools | Don't add agents you don't need |
| Supervisor | Central decomposition, specialists report back | Supervisor becomes the bottleneck |
| Pipeline | Strictly sequential stages with handoffs | Rigid order; slowest stage gates throughput |
| Hierarchical | Multiple org layers, > ~8 agents | Communication overhead per level |
| Swarm | Parallel peers, fault tolerance over predictability | Hard to debug; needs consensus rules |
The planner applies this scoring deterministically — run it rather than picking by feel.
All paths relative to this skill folder. Each step's JSON output is the next step's design input.
Write a requirements JSON (copy assets/sample_system_requirements.json — keys: goal, tasks[], constraints{max_response_time, budget_per_task, concurrent_tasks}, team_size):
python3 agent_planner.py requirements.json --format json -o arch
Emits arch.json with architecture_design (pattern, agents, communication links), mermaid_diagram, and implementation_roadmap. Read architecture_design.pattern and the per-agent role list; present the mermaid diagram to the user.
Describe each agent's tools in plain JSON (copy assets/sample_tool_descriptions.json), then:
python3 tool_schema_generator.py tool_descriptions.json --validate -o tools
Emits tools.json (tool_schemas, validation_summary) plus provider-specific tools_anthropic.json / tools_openai.json. Gate: every tool must print ✓ Valid. Fix any invalid schema before proceeding — never hand an agent an unvalidated schema.
Once the system runs (or against assets/sample_execution_logs.json for a dry run):
python3 agent_evaluator.py execution_logs.json --detailed -o eval
Emits eval.json with summary, agent_metrics, bottleneck_analysis, error_analysis, cost_breakdown, sla_compliance, and optimization_recommendations, plus split files (eval_errors.json, eval_recommendations.json).
The design is not done until:
tool_schema_generator.py --validate reports 0 invalid schemas.agent_evaluator.py on a pilot run reports 0 critical issues (the tool prints CRITICAL: N critical issues when found). If N > 0, apply the top item in eval_recommendations.json, re-run the pilot, and re-evaluate.expected_outputs/ to confirm the schema shape you're consuming hasn't drifted.references/agent_architecture_patterns.md — pattern trade-offs in depthreferences/tool_design_best_practices.md — schema, idempotency, error-handling rulesreferences/evaluation_methodology.md — metric definitions the evaluator implementsまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
日本語の概要は準備中です。原文の説明を表示しています。
When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
日本語の概要は準備中です。原文の説明を表示しています。
Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
日本語の概要は準備中です。原文の説明を表示しています。
Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
日本語の概要は準備中です。原文の説明を表示しています。