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概要と使いどころ
Analyze meeting transcripts to extract decisions, action items, owners, due dates, open questions, and risks. Use after recorded meetings, sales calls, customer interviews, or planning sessions, or to build a decision log.
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。
Context management engine for AI coding agents. Use when building agent memory systems, optimizing context windows, allocating token budgets, designing RAG pipelines for code, or managing persistent multi-session agent state.
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。
Prevent 此地无银三百两式 residue: finalize artifacts without echoing rejected session-only alternatives into labels, metadata, commits, PRs, or handoffs. Use after corrections or discarded proposals. Not for ordinary deletion, deprecation, migration, or exclusions materially required for safety, accuracy, compatibility, audit, quotation, or requested comparison.
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。
Warns before destructive commands (rm -rf, DROP TABLE, force-push, git reset --hard, kubectl delete, docker force-rm). Session-scoped. Exceptions for common temp dirs (node_modules, .next, __pycache__).
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。
Headless Chromium CLI for QA testing and site dogfooding. 100-200ms per command after startup. Persistent state with cookies, tabs, sessions. Snapshot system with element refs. Navigate, interact, verify page state, take screenshots, check responsive layouts, test forms, handle dialogs.
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。
Import cookies from your real browser (Comet, Chrome, Arc, Brave, Edge) into the headless browse session for testing authenticated pages. Interactive picker UI or direct domain import. macOS Keychain 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。
Multi-AI second opinion via OpenAI Codex CLI. Three modes: Review (pass/fail gate), Challenge (adversarial edge-case finder), Consult (freeform with session continuity). Read-only with streaming output and thinking traces. Cross-model analysis when review has already run.
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。
Give an AI agent persistent, cross-session long-term memory through MindMemOS. Covers installing and authenticating the mindmemos CLI, the full command interface (add / search / get / update / delete / feedback / dreaming) with parameters and examples, guidance on which capability to use when, plus a Python SDK example. To wire memory into a specific agent host (OpenClaw, DeepSeek Harness, Codex, Claude, etc.), see references/.
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。
Enforces staged execution discipline on large tasks: a written stage plan, parallel delegation where the runtime supports it, a failable verification check at each stage, and a skeptical self-review before delivery. Trigger when the user explicitly asks OR when the task objectively spans multiple files, sources, or sessions. Do NOT trigger on ordinary multi-step requests that a direct attempt handles fine (autonomous triggering only; explicit invocation always runs).
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。
Track performance across sessions and reduce scaffolding as competence grows. Makes fading visible — the learner knows when scaffolds are removed and why. Use for sustained learning engagement where independence is the goal.
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。
Generate a coaching conversation guide with questions, protocols, and follow-up actions for a teaching focus. Use when preparing for coaching sessions, mentoring, or peer feedback conversations.
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 this skill whenever a lawyer or mediator needs help analyzing a dispute for mediation purposes. This includes: reviewing case materials (pleadings, contracts, correspondence, evidence) to identify issues in dispute, summarizing each party's position and interests, conducting legal analysis of the key issues, proposing mediation strategies or settlement directions, and preparing for mediation sessions. Trigger this skill when the user mentions 'mediation', 'dispute analysis', 'settlement', 'dispute resolution', 'identify issues in dispute', 'party positions', 'mediation brief', 'case analysis for mediation', 'ADR', 'mediation preparation', 'caucus strategy', 'settlement options', or any request to analyze a conflict between two or more parties with the goal of finding resolution. Also trigger when the user uploads case files and asks for a structured breakdown of who wants what, what the core disagreements are, or how the case might settle. Even if the user doesn't explicitly say 'mediation', trigger when the context involves analyzing opposing positions in a dispute with a resolution-oriented (rather than litigation-oriented) goal.
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。
Helps a lawyer build and use a personalized Legal Guidance Vault — a local folder of structured, privilege-marked entries capturing legal guidance they've given, searchable later by topic, person, or product. Use this skill when the user says "set up my vault," "archive this meeting/email/thread/doc," "log the guidance I gave on [topic]," "save my notes from [meeting]," "what did I say about [topic]," "find the time I advised [person] on [topic]," or "search my vault for [topic]." Runs a setup interview to learn the user's tool stack (meeting notes, calendar, email, docs, messaging, vault location), then generates a customized vault prompt they can reuse in any future Claude session to start archiving and retrieving 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。
EU Digital Regulation Legal Analysis Forge — generates a tailored expert prompt for structured legal analysis of an EU digital-regulation document, optionally executes it in-session, and always produces a plain-English explainer alongside the formal output. Handles Regulations, Directives, Commission Guidelines, EDPB Opinions, CJEU and AG judgments, national DPA decisions, codes of conduct, and draft consultations across the EU digital stack.
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。
EU AI Act Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-session.
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 your bar comes asking "show me how you billed AI-assisted work" — and ABA 512, Florida 24-1, California, New York, and DC all have opinions out — you need an artifact that survives review. billable-time produces it. From your Claude Code session logs, it drafts reviewable time entries plus a printable HTML audit packet with: SHA-256 chain of evidence (source files + matter.yml + active disclosure pack + verifiable artifact self-hash), attorney identity and signature block, a bar-opinion disclosure pack with starter language for five jurisdictions, and content-aware deterministic narratives derived from filename and tool shape — never from prompt text by default. The tool refuses to bill on its own. --strict mode refuses to ship the artifact if any audit invariant fails (broad routes, missing attorney, missing/unverified disclosure). Comes as a Node CLI and a self-contained browser version (no backend; JSONL never leaves the page). 15 invariant tests verify the contract. AGPL-3.0.
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 optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," "new user experience," "users aren't activating," "nobody completes setup," "low activation rate," "users sign up but don't use the product," "time to value," or "first session experience." Use this whenever users are signing up but not sticking around. For signup/registration optimization, see signup. For ongoing email sequences, see emails.
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。
Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles
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。
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.
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。
Apply STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security documentation.
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 guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.
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。
Clean up translation-ese and AI-writing tics in Korean prose so it reads like a person wrote it — content (facts, numbers, proper nouns, quotes) stays 100% unchanged, only style and rhythm are touched. Single-pass, done in this session. Triggers — AI 티 없애줘, 번역투 고쳐줘, 한글 자연스럽게 윤문해줘, sounds like a machine translation, remove AI tells from this Korean text, make this Korean read naturally. Not for: marketing copy generation (write it clean the first time — see rules/korean-writing-quality.md), English text, plain spell-check (see korean-spell-check), or book-length manuscripts.
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。
Integrate the Temps Node.js SDKs for server-side platform access, KV storage, and Blob storage. Use when the user wants to: (1) Call the Temps platform API from Node.js (deployments, projects, analytics, session replay, etc.), (2) Use Temps KV (key-value) storage, (3) Use Temps Blob storage for files, (4) Server-side integration with a Temps project, (5) Backend access to Temps resources. Triggers: "temps node sdk", "temps kv", "temps blob", "backend integration", "node.js temps", "@temps-sdk/node-sdk".
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 Temps error tracking to applications using the Sentry-compatible SDK. Temps exposes a Sentry-compatible DSN that works with the official Sentry SDK for each language/framework — no code changes beyond initialization are required. Use when the user wants to: (1) Add error tracking to any app (React, Next.js, Vue, Svelte, Angular, Node.js, Python, Go, Rust, Ruby, Java, PHP, .NET, React Native, Flutter), (2) Wire up uncaught exception and unhandled promise rejection capture, (3) Configure session replay for errors, (4) Upload source maps for readable stack traces, (5) Report releases and environments, (6) Capture custom errors/messages. Triggers: "add error tracking", "add sentry", "track exceptions", "report errors", "temps error tracking", "wire up error monitoring".
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。