4,235 件 ・ 関連度順
概要と使いどころ
Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.
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。
Search and read locally-stored Claude documentation covering Claude Code CLI, Claude API (Messages, tool use, vision, streaming, batch), Agent SDK (Python and TypeScript), prompt engineering, and all Anthropic platform docs. Use this skill whenever the user asks about Claude Code features (hooks, MCP servers, skills, plugins, settings, permissions, keybindings, sub-agents), the Anthropic API or any of its SDKs (Python, TypeScript, Go, Java), the Agent SDK (sessions, hooks, custom tools, MCP), model capabilities (context windows, extended thinking, pricing, rate limits, vision), prompt engineering best practices, or troubleshooting any Claude-related error. This skill provides instant access to official documentation files without web searches — always prefer it over web lookups for Claude and Anthropic topics.
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。
Provides reverse engineering techniques. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, game clients, malware-like loaders, and anti-debug or anti-analysis logic. Do not use it when the vulnerability is already understood and the remaining task is exploitation; use pwn instead. Do not use it for pure web workflows, log or disk forensics, or standalone crypto problems unless reversing the implementation is the real blocker.
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。
可靠性工程时使用。适用于 SLO 定义 / 错误预算管理 / Chaos Engineering / 灾备演练。融合 Google SRE SLO + Netflix Chaos Monkey。
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。
[Deprecated] engineering bridge merged. Use `cm-mcp-engineering` instead.
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 you need to interact with the MCP engineering bridge tools (cm_plan, cm_review, cm_qa, cm_deploy, cm_search).
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。
Booking & Calendar CRO Engine — Packages all booking, scheduling, .ics download, and Google Calendar integration into a revenue-boosting system. Auto-detect industry → select matching pattern → generate calendar engine + booking UI + export logic. Integrates with cm-google-form (form → sheet) and cm-ads-tracker (conversion events). ALWAYS trigger for: booking, appointment, calendar, reminder, download ics, google calendar, add to calendar, book appointment, schedule, "create booking", "add scheduling", "appointment reminder system", "calendar CRO", "reduce no-show"
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。
axe-core is the industry-standard accessibility testing engine by Deque Systems that automatically detects WCAG 2.0, 2.1, and 2.2 violations in web interfaces. It integrates into any existing test framework — Playwright, Cypress, Selenium, Jest — to catch accessibility issues during development rather than after deployment.
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。
Anyquery is a SQL query engine that lets you run SQL against 40+ apps, files, and databases including GitHub, Notion, Chrome, and Apple Notes. Built on SQLite with MCP server support for connecting AI agents to structured data across services.
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 Engine is a WordPress plugin by Meow Apps that connects sites to OpenAI, Claude, Gemini, and other models while exposing WordPress actions through MCP and REST interfaces. This skill helps agents configure providers, enable the plugin's MCP capabilities, and automate content, chatbots, media, and site-management workflows from WordPress.
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。
Client-side initial access and social-engineering attack module. Use when the operator selects a human/client/browser/identity-delivery attack direction: ClickFix/FileFix, AiTM session phishing, OAuth device-code or consent abuse, Teams/helpdesk social engineering, HTML/file delivery, QR/mobile handoff, legacy HTA/OLE/Office paths. This is an Attack module: own the chain until host shell or remote interactive foothold. Credential/token/session are intermediate results, not the stop condition. If endpoint AV/EDR/AMSI/application-control blocks an otherwise-valid client execution path, the operator may temporarily select /edr-bypass and then resume this phishing chain until shell.
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 says 'run an seo aeo audit', 'improve my search visibility', 'optimize my site for search engines', or wants the site cited by AI answer engines. Audits on-page SEO and AEO, generates schema markup, and applies fixes on duplicates for review after a snapshot.
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。
Optimize for search engine visibility and ranking. Use when the task involves `improve SEO`, `optimize for search`, `fix meta tags`, `add structured data`, `sitemap optimization`, or `search engine 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。
Use when writing or modifying code for s&box, sbox, the Facepunch sandbox engine, or any Source 2 game project in C#. Triggers on mentions of s&box, sbox, sandbox game, Facepunch engine, Source 2 game, `.sbproj`, `.razor` with `PanelComponent`, `@inherits PanelComponent`, `Sandbox.Component`, `GameObject` + `Components.Get<T>`, `Scene.Trace`, `CharacterController` + `.Move()`, `SkinnedModelRenderer`, `NavMeshAgent`, `[Sync]`, `[Rpc.Broadcast]`, `[Property]`, `INetworkListener`, `ISceneEvent`, `PlayerController` in C#. Also triggers on any file with `using Sandbox;` or `using Sandbox.UI;` that isn't Unity/Godot/Unreal. Writes idiomatic s&box C# components, Razor UI, and networking code; prevents Unity-pattern leakage.
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。
Create or refine durable engineering documentation: docs structure, navigation, source-of-truth placement, and writing quality. Use when a repo’s docs are missing, stale, duplicated, or hard to navigate, or when adding new durable engineering guidance.
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。
Comprehensive 7-phase SEO optimization workflow covering technical SEO auditing, keyword research strategy, on-page optimization, content SEO and E-E-A-T, structured data implementation, link building, and AI search optimization. Use when auditing search engine visibility, diagnosing ranking issues, implementing schema markup, conducting keyword research, optimizing content for E-E-A-T signals, or adapting for AI-driven search (AI Overviews, SGE).
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。
Master network protocol reverse engineering including packet analysis, protocol dissection, and custom protocol documentation. Use when analyzing network traffic, understanding proprietary protocols, or debugging network communication.
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。
Fintech engineering expertise — payment processing (Stripe, Plaid), PCI DSS compliance, financial data modeling (double-entry bookkeeping), fraud detection patterns, bank-grade security (encryption, tokenization), open banking APIs, cryptocurrency/blockchain integration, regulatory compliance (KYC/AML), and idempotent financial transaction design. Use for payment systems, banking apps, trading platforms, and fintech infrastructure.
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。
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a benchmark case from this paper", "reverse-engineer raw materials", or "evaluate my pipeline against PaperWritingBench".
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 websites for AI search visibility (GEO/SEO) and generates deployable artifacts (llms.txt, schema JSON-LD, robots.txt). 7-dimension scorecard tailored by site type and size, grounded in Princeton KDD 2024, Toronto 2025, and ICLR 2026 research. This skill should be used when optimizing for ChatGPT, Perplexity, Google AI Overviews, or other AI search engines. Also triggered by queries about AI search optimization, generative engine optimization, llms.txt, AI crawler configuration, schema markup for AI, citation optimization, or Google AI Mode.
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 an engineering plan needs pressure-testing for architecture, complexity, data, risk, and operability.
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。
Specialized agentic and context engineering prompt methodology for Claude. Use when designing AI agents — system prompts, tool definitions, context window architectures, failure mode planning, multi-agent coordination, agent evaluation. Trigger on: "design an agent," "build a system prompt for an agent," "architect an agent's context," "design tool definitions," "agent system prompt," "context window architecture," "tool interface design," "evaluate agent behavior," "multi-agent coordination," "context engineering for agents," "ACI design." Provides 12 tested approaches for agent architecture (designing agents, not using them). Do NOT use for evaluating or scoring individual non-agent prompts (use rootnode-prompt-validation if available). Do NOT use for general software architecture that hosts agents (use standard technical reasoning 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。
Independent re-derivation gate for proposed changes during autonomous execution. Evaluates a change (code diff, config edit, engine evolution, schema change) against the work's authority matrix and a 4-check protocol: invariant compliance, scope authorization, detection narrowness, and regression risk. Returns structured JSON with pass/fail per check, an overall verdict (APPROVE / REQUEST_CHANGES / REJECT), and blockers. Profile-driven thresholds (strict for unattended runs, lenient for desk supervision). Use when an autonomous agent proposes an engine change, when reviewing a Claude Code-authored modification before merge, or when an unattended profile requires independent re-derivation. Trigger on: "review/approve this proposed change," "critic-gate this," "is this change safe," "is this safe to merge," "should I let this land," "run the critic." Do NOT use for handoff-readiness checks (use rootnode-handoff-trigger-check), general code review, or design-time correctness. Authority matrix required.
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 and execute chaos engineering experiments using Litmus or Chaos Mesh. Test system resilience through controlled fault injection, validate hypothesis-driven tests, and improve failure recovery. Use before major product launches, after architecture changes to validate resilience, during GameDays or disaster recovery drills, to validate assumptions about failure modes, or as part of an SRE maturity program.
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。