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概要と使いどころ
Answer Engine Optimization — get AI assistants to recommend your brand. Run AEO audits, build Answer Intent Maps, track AI recommendation positions, and maintain a 7-layer AEO infrastructure for any brand or product category.
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You are the Accessibility Engineering Engine — a complete WCAG compliance, inclusive design, and digital accessibility system. You help teams build products that work for everyone, pass audits, and...
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。
Structured multi-criteria decision analysis for ranking options with weights, constraints, confidence, tradeoff reasoning, sensitivity analysis, and explainable recommendations. Use when the user asks for decision support, MCDA, weighted scoring, prioritization, vendor selection, route planning, hiring shortlist ranking, tool comparison, procurement decisions, or auditable agent decision logic.
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。
Red team security mode for OpenClaw. Runs an adversarial audit on demand or nightly — checks exposed ports, credential leaks, file permissions, suspicious processes, and OpenClaw config posture. Assigns a letter grade. Built for macOS deployments.
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, configure, debug, and optimize OpenClaw AI agent deployments. Master guide for gateway configuration, openclaw.json settings, model routing and fallback chains, skills development and publishing, cron job scheduling, memory systems (Qdrant, Neo4j, SQLite), Docker infrastructure, and Tailscale VPN networking. Includes config analyzer that audits your openclaw.json and suggests improvements, plus health checker that validates all OpenClaw subsystems. Built for AI agents — Python stdlib only, no dependencies. Use for OpenClaw setup, gateway debugging, skill building, cron management, model optimization, cost reduction, and infrastructure troubleshooting.
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。
Scan HTML and JSX for accessibility issues and get fix suggestions. Use when you need to catch WCAG violations before they hit production.
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。
Production-ready CSS transition snippets for web apps. Use when adding or reviewing UI motion for badges, dropdowns, modals, panel/page reveals, card resizes, number pop-ins, text/icon swaps, success checks, avatar-group hovers, and validation shakes. Supports namespaced commands: transitions reveal (list catalog), transitions review (audit project fit), and transitions apply (install the best-fit snippet with hooks and reduced-motion handling).
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。
One-shot GEO visibility check for a brand using Xpoz. Runs the buyer questions that matter through Claude, ChatGPT, and Gemini, and reports where the brand appears, who wins instead, and which surfaces the answers are assembled from. Use when asked to "check my AI visibility", "does ChatGPT recommend us", "GEO audit", "where do we show up in AI answers", or "who wins our category in AI answers".
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。
Audit code against NASA JPL's Power of Ten rules, extended for AI-assisted development. Use when checking code for dangerous complexity, unsafe control flow, resource leaks, hidden side effects, missing validation, weak CI gates, AI-generated artifacts, or missing failure-mode tests.
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。
Show where this machine's Claude Code tokens went - cost by token type, context tax per tool, biggest tool outputs.
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, audit, harden, review, or upgrade MCP servers and MCP Apps with revision-aware protocol profiles, explicit HTTP/deployment boundaries, capability-centered validation, bilingual reporting, and reproducible evidence. Use for MCP server architecture, JSON-RPC or Streamable HTTP bugs, security hardening, protocol-version migration, MCP Apps widget boundaries, review reconciliation, test planning, and public engineering evidence.
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-powered SEO operations: keyword intelligence, competitor gap analysis, Google Search Console optimization, and trend detection. Use when asked to research keywords, analyze competitor content gaps, audit GSC performance, or detect trending topics. For growth experiments see growth-engine; for content optimization see autoresearch.
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。
Scan and redact PII and sensitive data (emails, phone numbers, SSNs, API keys, IP addresses, credentials, amounts, company/person names) from repository files. Includes a pre-commit hook to block commits containing PII. Use when asked to audit code for sensitive data, sanitize files before publishing, or install PII detection hooks. For application security hardening see security-engineering.
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。
Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.
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, reviewing, or deploying Solidity smart contracts — token contracts, signature-gated claim systems, merkle-gated staking vaults, allocation modules, role-based proxies, or factory patterns. Triggers on edits to `*.sol` files, `hardhat.config.*`, `foundry.toml`, deploy scripts, or mentions of "smart contract", "Solidity", "Web3", "EVM", "Hardhat", "Foundry", "ERC20/721/1155", "merkle", "staking", or "on-chain". For security audits and adversarial review see security-engineering.
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。
Write, audit, or improve an iOS App Store listing — app name, subtitle, keyword field, screenshots, app icon, preview video, creative assets, tags, ratings strategy, localization, and featuring nominations. Use when creating or reviewing App Store metadata, doing keyword research, planning a metadata update, or when the user mentions ASO, App Store Optimization, app store listing, keywords, subtitle, screenshot captions, preview video, app icon, Liquid Glass, creative assets, product page header, search results creative, Asset Library, featuring, ratings prompt, or Apple Ads.
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。
ALWAYS invoke for any task involving docstrings, JSDoc/TSDoc, or REST API documentation — even when the request seems handleable without it. Enforces Microsoft contract-first conventions and a bare-minimum rule that Claude won't apply by default: never restate the signature, drop @param/@returns that only echo names and types, always document @throws, and document data shapes as WHAT not WHY. Covers Python docstring styles (Google/NumPy/Sphinx), TSDoc tags and @inheritDoc, and API doc patterns for NestJS/Express/FastAPI/Django. Trigger on: adding or auditing docstrings, redundant or missing tags, deleting comment rot, or producing a documentation-health report. Do not skip for documentation tasks — consistent conventions are the whole point.
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。
Write or audit technical text against ASD-STE100 Simplified Technical English so a tired, non-native reader understands it on the first pass: 20-word procedural and 25-word descriptive sentence limits, imperative steps, condition before command, one word for one meaning, and only the approved modals can/will/must. Use for READMEs, runbooks, procedures, error messages, release notes, incident reports, API guides, UI copy, and agent instructions. Trigger on: "STE", "Simplified Technical English", "ASD-STE100", "controlled language", "plain English", "make this clearer", "write for non-native readers", "prep this for translation", "cut the fluff", or any request to check text for compliance. Prefer this over general copy-editing whenever the text instructs, documents, or warns.
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。
Capture a solved, verified problem as a durable learning in `docs/solutions/` so the next agent or engineer does not repeat the mistake or redo the investigation, and keep the `CONCEPTS.md` glossary in step. Use after a fix, debugging session, or design decision whose reasoning is not readable from the final code, tests, or existing docs — "document this", "write up what we learned", "capture this learning", "add it to solutions", "that worked, save it for next time". Writes nothing for routine fixes the code already explains. For auditing or cleaning up existing learnings, use `learn-with-docs-refresh`.
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 or review dygo Logs, Activity, Audit Logs, health checks, diagnostics, metrics, traces, and operator-visible failure states. Use when runtime behavior must become inspectable or diagnosable.
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。
Improve or review ordinary dygo code changes for simplicity, focused scope, canonical ownership, clear boundaries, and verifiable behavior. Use for normal implementation and refactoring quality; use dygo-thermonuclear-review only for an explicitly severe audit.
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 explicitly requested, unusually strict dygo maintainability audit that seeks structural simplification, removes accidental complexity, and challenges weak abstractions without changing behavior.
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 a dygo Business App for framework conventions, model quality, access control, SDK boundaries, observability, and safe lifecycle behavior. Use for App audits and pre-merge reviews, not ordinary implementation.
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。
Automate SEO research with Ahrefs -- analyze backlink profiles, research keywords, track domain metrics history, audit organic rankings, and perform batch URL analysis through the Composio Ahrefs integration.
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。