1.2万 件 ・ 関連度順
概要と使いどころ
Drafts FDA 510(k) Premarket Notification submissions demonstrating substantial equivalence under 21 CFR Part 807. Supports Traditional, Special, and Abbreviated pathways. Use when preparing Class II medical device regulatory filings, substantial equivalence analyses, or FDA premarket submissions.
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 compliance analysis for EU AI Act, ISO 42001, NIST AI RMF, GDPR, OECD, financial services regulations (SEC, FCA, FINRA, DORA, MiFID II), and other frameworks. Use when asked to generate a compliance checklist for an AI tool or use case, determine if a risk assessment is required, score an AI tool's risk level, identify where an AI tool or use case could run afoul of regulatory requirements, perform a gap analysis, recommend remediation steps, assess a vendor, draft an acceptable use policy, map training requirements, or review jurisdiction-specific AI rules. Triggers on phrases like "compliance checklist", "risk assessment", "risk score", "does this comply", "EU AI Act", "ISO 42001", "NIST AI RMF", "AI governance", "gap analysis", "vendor assessment", "acceptable use policy", "MNPI", "AI training requirements", or any request to evaluate an AI tool or use case for regulatory compliance or risk.
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。
Produces structured U.S. adoption-law summaries for litigation and client-facing use. Use when asked to summarize adoption law, draft pre-filing research briefs, create jurisdiction-specific issue memoranda, or prepare client education documents. Triggers: adoption summary, domestic adoption, intercountry adoption, Hague adoption, foster care adoption, TPR in adoption, stepparent/relative adoption, post-adoption rights.
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。
Drafts U.S. commercial real estate access and indemnity (right-of-entry) agreements for pre-closing due diligence. Covers license grants, non-invasive vs invasive testing gates, insurance/endorsement requirements, indemnity with discovery carve-outs, restoration and lien remedies, confidentiality, and anti-indemnity guardrails. Trigger: access agreement, right of entry, due diligence access, Phase I/II, invasive testing, pre-PSA site inspection.
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 working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or choose between local planning and persistent project 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。
Use when morphir-scala work needs environment diagnostics or build/network/sandbox unblocking; reference repository management; branch lifecycle refresh after squash-merging a target branch into main; Morphir IR spec/schema sync or export; or task-tracking configuration and beads mode resolution.
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 receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
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Use for Six Thinking Hats or de Bono's parallel thinking, or when a decision or idea needs facts, gut feel, risks, benefits and new options kept apart: "look at this from all sides", "full perspective check". Skip for plain factual questions or single-angle hot takes.
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 for design thinking, or when a product or service problem needs user understanding before solutions: "what do users really need?", "how might we...", "plan a discovery sprint", "validate this concept". Skip when the spec is frozen or the task is code maintenance.
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。
Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to implementation-loop with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to implementation-loop. Requires implementation-loop and an authenticated backend CLI to execute.
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。
Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to cursor-implementation-loop, this plugin's kernel skill, with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to cursor-implementation-loop. Requires this plugin's cursor-implementation-loop skill; they ship together.
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 designing and operating the in-app purchase catalog of a game — SKU design, price-tier ladder, bundles, starter packs, battle pass tiering, A/B price tests, store config (App Store / Google Play / Steam DLC / web / web3), and per-region price localization. Triggers on "IAP catalog", "store SKUs", "pricing tiers", "starter pack", "bundle design", "battle pass tiering", "price test", "price localization", "App Store Connect", "Google Play Console", "Steam DLC", or when handed a monetization strategy with the catalog still open. Produces a catalog spec, per-region price tables, bundle compositions, and a price-test plan. For in-game economy curves see game-balancer.
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。
Autonomous growth experimentation framework: creates experiments with hypotheses, logs data points, runs statistical analysis (bootstrap CI + Mann-Whitney U), auto-promotes winners to a living playbook, and suggests next experiments. Use when asked to run growth experiments, analyze A/B tests, build experiment scorecards, or generate pacing alerts. For SEO-specific experiments see seo-ops; for outbound experiments see outbound-engine.
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 tuning the numbers in a game — economy curves, progression rates, difficulty pacing, drop tables, win/loss odds, time-to-X targets, and currency velocities. Triggers on "balance pass", "tune the economy", "progression curve", "difficulty curve", "drop rate", "XP curve", "TTK", "time-to-content", "balance the economy", "spreadsheet model", "economy sim", or a system spec from game-systems-designer with `TBD` placeholders. Produces an economy spreadsheet, balance pass plan, sim outputs, and per-system balance tables. Stops at the numbers — does not change system rules or pricing. For systems design see game-systems-designer; for store catalog see iap-manager.
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Internal utility for the skills library itself — opt-in, local-first, privacy-respecting usage telemetry and update checks. Provides version checking, usage logging, and usage reporting for the skills in this repo. Use only when asked to view skill usage stats, configure telemetry opt-in/out, wire a skill to log its own runs, or check for skill updates.
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。
Tests in real browsers. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data via Chrome DevTools MCP.
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 building games or interactive software in Godot 4 with C# — designing scenes and nodes, writing gameplay code, handling input, physics, animation, UI, rendering, save systems, performance work, WebSocket-based multiplayer, or exporting to platforms. Triggers on "Godot", "GDScript", "C# Godot", "scene tree", Godot node types (Node2D, Node3D, Control, CharacterBody), "_Process", "_PhysicsProcess", "signal", "autoload", "RPC", "WebSocketMultiplayerPeer", "shader", "export preset", or any Godot 4 project files. For game design see game-systems-designer; for balance see game-balancer; for game-specific intake see game-design-shaper.
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。
Breaks work into ordered, parallel-dispatchable tasks with an execution DAG. Output format is consumable by CI matrices and parallel agent runners — each task has a stable ID, declared file writes, conflict edges, and branch suffix. Use when you have a spec, brief, or shaper output and need to decompose it into implementable units. Use when a task feels too large, when scope spans multi-repo or multi-week work, or when parallel execution across multiple agents is on the table.
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 preparing or writing any committable deliverable (phase closeout, status report, skill file, wave spec, README section, summary document, CHANGELOG entry, or any markdown file declaring completion or pass/fail status). Applies before the write happens, not after. Without this discipline, agents ship 60% deliverables and declare closure prematurely.
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 dark mode UI design with atmospheric theming, WCAG accessibility, and cross-platform best practices. Specializes in weather/sky/ocean-inspired color systems that adapt to time of day and environmental conditions. Use when the user explicitly needs dark mode design expert help. NOT for unrelated tasks.
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This is the MistEye security gate skill. It is triggered by pre-installation risk checks (including Skill/MCP dependency manifests), pre-access security checks for domains or URLs, IoC malicious detection, and supply-chain risk blocking, especially for common requests such as "take a look at this address/website." It must use `https://app-api.misteye.io/functions/v1/detect`; in dependency and external-link scenarios it has the highest priority. If the API returns `status="malicious"` or any `matches`, the action must be blocked and explicitly reported as "blocked". Only after the first installation is complete should the user be reminded that daily patrols can be enabled with OpenClaw or Hermes (default once per day).
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Launch Marcus multi-agent experiments with independent CLI agents in tmux panes. Use when the user says "build X with Marcus", "run a Marcus experiment", "spawn N agents with Marcus", "/marcus", or references the Marcus multi-agent framework. This skill handles experiment setup, config generation, and spawning independent agent CLI processes that coordinate via the Marcus MCP server. Each agent runs in its own tmux pane with full autonomy. NOT for ClawTeam — this is specifically for the Marcus MCP-based multi-agent system. NOTE: "Marcus" is also the name of the MCP server that agents use for coordination. The /marcus skill launches experiments that USE the Marcus MCP server — they are complementary, not competing. The MCP server must be running before invoking this skill. The Marcus MCP server is agent-agnostic; this skill supports both Anthropic's claude CLI and OpenAI's codex CLI as agent harnesses (selected via --harness).
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。
Launch Marcus multi-agent experiments with independent CLI agents in tmux panes. Use when the user says "build X with Marcus", "run a Marcus experiment", "spawn N agents with Marcus", "/marcus", or references the Marcus multi-agent framework. This skill handles experiment setup, config generation, and spawning independent agent CLI processes that coordinate via the Marcus MCP server. Each agent runs in its own tmux pane with full autonomy. NOT for ClawTeam — this is specifically for the Marcus MCP-based multi-agent system. NOTE: "Marcus" is also the name of the MCP server that agents use for coordination. The /marcus skill launches experiments that USE the Marcus MCP server — they are complementary, not competing. The MCP server must be running before invoking this skill. The Marcus MCP server is agent-agnostic; this skill supports Anthropic's claude CLI, OpenAI's codex CLI, and Google's gemini CLI as agent harnesses (selected via --harness).
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。