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概要と使いどころ
Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.
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。
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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。
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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。
Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.
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。
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in 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。
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).
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。
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
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。
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。
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。
Local-only regression / benchmark skill for ui-clone-skills maintainers. Drives the standard ui-reverse-engineering pipeline against the canonical reference site (https://realfood.gov) and records AE/SSIM, iteration count, gate fail counts, and outcome to benchmark/history.csv so prompt / sub-doc / model-version drift surfaces as a trend. Trigger phrases: "run benchmark" / "regression benchmark" / "benchmark clone". The Makefile no longer has a `benchmark` target — setup is inline bash in this skill (Step 1 below). Internal: NOT registered in `.claude-plugin/plugin.json` `skills`. Not part of the public 3-skill marketplace surface. Maintainer tooling only.
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。
Run or resume a deliverable active-outreach workflow across lead discovery, screening, customer intel, and outreach email. Use when an operator or system pipeline needs durable run state, visible intermediate artifacts, evidence gates, report-bound human approval, and a final editable email draft that is never auto-sent.
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。
Add a new Dagster asset to the pipeline. Use when creating a Bronze, Silver, or Gold layer asset.
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。
Isolate the root cause of ML performance drops, inconsistent evaluations, prediction errors, and training-serving mismatches across data, labels, splits, pipelines, models, metrics, and runtime behavior. Use when investigating a reproducible failure or regression, not routine model selection or general performance validation.
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。
Plan and implement infrastructure, CI/CD, container, deployment, observability, and operational configuration changes with least privilege, staged validation, and rollback awareness. Use for pipelines, infrastructure as code, Kubernetes, containers, release automation, monitoring, and production-readiness work; do not use to perform destructive production actions without explicit authorization.
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。
Summarizes uncommitted git changes in a concise machine-readable format. Use in CI pipelines, scripts, or headless invocations where the output will be piped or captured.
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。
Expert Clay platform consultant for B2B data enrichment and workflow automation. Use when the user asks about Clay tables, waterfall enrichment, Clay credits, Clay AI columns, Claygent, enrichment workflows, Clay integrations, Clay exports, Clay table design, lead scoring in Clay, or building data pipelines in Clay. Also triggers on "Clay table", "enrichment waterfall", "Clay credits", "Claygent", "Clay AI column", "Clay export", "Clay providers", "enrich in Clay", "Clay column", "Clay formulas", "find emails", "email waterfall", "phone waterfall", "lead scoring", "Clay debugging", "Clay troubleshoot". Do NOT use for general CRM questions without Clay context, cold email copy (use cold-email-copy-playbook), or signal routing (use buying-window-signal-workflow).
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。
Expert-level computer architecture covering CPU design, instruction sets, pipelining, cache hierarchy, memory systems, parallel architectures, and GPU computing.
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。
Expert-level computer graphics covering the rendering pipeline, rasterization, ray tracing, shaders, transformations, lighting models, and GPU programming.
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。
OMC agent catalog, available tools, team pipeline routing, commit protocol, and skills registry. Auto-loads when delegating to agents, using OMC tools, orchestrating teams, making commits, or invoking skills.
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。
Run an infrastructure-first security audit of a repository - secrets archaeology, dependency supply chain, CI/CD pipeline, LLM/AI attack surface, agent skill supply chain, OWASP Top 10, STRIDE threat model, and data classification - with a confidence gate and a pre-emit verification gate that suppress speculative findings. Use when asked for a security audit, threat model, pentest-style review, OWASP pass, or a pre-release security check. Not for reviewing a single diff - use review-changes or requesting-code-review for that.
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。
Conception pipeline for Codex. Equivalent to the repository's Claude skill: detect epic/task/bug mode, then delegate to the existing repo agents and workflows. Usage: /analyse [<issue#>] [<description>]
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。
Execution pipeline for Codex. Detect epic/task/bug mode and implement with Codex models only. Usage: /implement <issue#>
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。
Review-fix pipeline for Codex. Equivalent to the repository's Claude skill: collect review feedback, apply fixes through the existing review workflow, and prepare replies. Usage: /review [<issue#>|<PR#>]
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。