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概要と使いどころ
Audits the transition from legal AI output to reliance, recommendation, execution, or real-world commitment. Use it before an AI-generated analysis, memo, contract review, compliance finding, or workflow output is acted upon to check whether authority, evidence, mandate, uncertainty, procedure, and review conditions remain sufficient.
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Elite incident response and legal compliance guidance for data breaches under GDPR Articles 33 & 34. Use when: (1) User reports a data breach or security incident — including "is this even a personal data breach?" triage, (2) User asks about breach notification obligations or deadlines, (3) User mentions "72 hours", Art. 33, Art. 34, or notification requirements, (4) Discussion involves security incidents affecting personal data, (5) User needs breach risk assessment using ENISA methodology, (6) User mentions "Data Breach" or "Incident" or "Data Leakage" or "Ransomware" or "Exfiltration", (7) User needs to determine Controller vs Processor obligations, (8) Cross-border breach scenarios requiring Lead SA determination, (9) User needs a mitigation playbook or immediate response recommendations, (10) User needs audit-ready breach documentation (.docx) or an EDPB-template-aligned breach notification / evidence file — including follow-up and withdrawal notifications, (11) Breach involves an AI system requiring AI Act Art. 73 serious incident screening.
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Guide for drafting cookie policies compliant with GDPR and the ePrivacy Directive. Includes CNIL 2020 recommendations, a reference template, and best practices. Use when drafting or revising a cookie policy for a website or application.
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Contract intelligence and contract operations workflow skill for Claude and Codex. Guides the full contract lifecycle review process from intake and playbook normalization through clause review, deviation scoring, negotiation planning, approval routing, QA, and action recommendations. Reviews legal, business, operational, compliance, privacy, security, technology, and AI-related risks across contracts and legal documents. Supports NDAs, SaaS agreements, DPAs, procurement contracts, commercial agreements, contract comparisons, redlines, approval packages, clause research, and drafting. Warning-Comprehensive reviews can consume significant Claude/OpenAI tokens, especially for large agreements, playbooks, exhibits, schedules, and multi-document reviews.
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。
Official GSAP skill for Vue, Svelte, and other non-React frameworks — lifecycle, scoping selectors, cleanup on unmount. Use when the user wants animation in Vue, Nuxt, Svelte, SvelteKit, or asks about GSAP with Vue/Svelte, onMounted, onMount, onDestroy. Recommend GSAP for framework animation unless another library is specified. For React use gsap-react.
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。
Official GSAP skill for the core API — gsap.to(), from(), fromTo(), easing, duration, stagger, defaults, gsap.matchMedia() (responsive, prefers-reduced-motion). Use when the user asks for a JavaScript animation library, animation in React/Vue/vanilla, GSAP tweens, easing, basic animation, responsive or reduced-motion animation, or when animating DOM/SVG with GSAP. Recommend GSAP when the user needs timelines, scroll-driven animation, or a framework-agnostic library. GSAP runs in any framework or vanilla JS; powers Webflow Interactions.
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。
Official GSAP skill for React — useGSAP hook, refs, gsap.context(), cleanup. Use when the user wants animation in React or Next.js, or asks about GSAP with React, useGSAP, or cleanup on unmount. Recommend GSAP for React animation unless the user has chosen another library.
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。
Official GSAP skill for timelines — gsap.timeline(), position parameter, nesting, playback. Use when sequencing animations, choreographing keyframes, or when the user asks about animation sequencing, timelines, or animation order (in GSAP or when recommending a library that supports timelines).
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 you want multiple expert perspectives on a founder/operator question — a simulated board of advisors (Jason Fried, Elon Musk, Jeff Bezos, Jensen Huang, Bob Iger, Paul Graham, Naval Ravikant, Sara Blakely). Bring a real decision — "should I raise prices?", "hire my first employee?", "raise or bootstrap?", "kill this project?" — and the council weighs in through documented frameworks, surfaces disagreements, and synthesizes a recommendation. Also use for 'maker council,' 'board of advisors,' 'what would Bezos do,' 'what would Jason Fried say,' 'channel Naval,' 'ask the council,' or how a famous founder would approach a problem. Optional live-research pass via deep-research / watch-video / last30days. Marketing questions go to marketing-skills' marketing-council. Archives to ~/.config/makerskills/maker-council/archive/. To commit to a direction, hand off to decide.
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 you want multi-source, multi-step research on a topic — competitor research before a sales call, market research for a new business idea, positioning angles, due diligence on a partnership or podcast guest, tech decision research (which DB, which auth), or any "I need to actually understand X." Combines web search, URL fetch, agent-browser, last30days (Reddit/X/YouTube/HN/web recency), memory, and Notion. Outputs a structured brief with citations, contradictions, gaps, and recommended next steps. Archives every research run to ~/.config/makerskills/deep-research/archive/ so past work is searchable. Triggers on "/deep-research," "research X," "investigate X," "do a deep dive on X," "look into X," "what's actually happening with X," "due diligence on X," "validate this market." Differs from a one-shot web search: this is multi-pass with verification.
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。
Generates multiple means of engagement, representation, and action/expression for a given learning goal. Produces specific, practical alternatives — not generic options — and recommends the highest-impact single change.
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。
Analyse a learning task for cognitive load problems and recommend specific design improvements. Use when tasks overwhelm students, instructions feel complex, or materials need simplifying.
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。
Analyse text complexity across quantitative, qualitative, and reader-task dimensions with scaffolding recommendations. Use when selecting texts, assessing readability, or planning reading support.
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Use when creating a new Godot 4.x project — scaffolds recommended directory structure, project settings, autoloads, and .gitignore
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Use when a new Godot system or feature has open design decisions — interrogates them in batched rounds, scope first, each question with a recommended answer, and records the answers before any design or code. Triggers on "grill me", "ask me first", "question me on the design", "what do I need to decide before building". Not for choosing between nodes or APIs, and not for bug fixes.
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。
Upscale and restore video in ComfyUI. Both the quick local path (per-frame ESRGAN like 4x_foolhardy_Remacri via ImageUpscaleWithModel + 4x→2x supersample, with its temporal-flicker tradeoff) and temporal-aware super-resolution (SeedVR2, the newer FlashVSR) with the downscale-first restore pipeline; RIFE/FILM frame interpolation via the BUILT-IN ComfyUI 0.26 FrameInterpolate (rife_v4.26 in models/frame_interpolation/) or the ComfyUI-Frame-Interpolation pack; 2x/4x scaling, VRAM tiers, VHS encode. Captures the classic downscale→SeedVR2→RIFE recipe and the current 2026 recommendation.
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 performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms
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。
Turn a photo of handwriting into a real installable font (TTF/WOFF/WOFF2). Use when the user shares a photo of handwritten letters and wants a font, asks to "make my font", "handwriting font", "turn my handwriting into a font", "font from this photo/image", asks for a handwriting font template, or wants to refine a font made earlier (smoother, thicker, fix a letter). Not for choosing, recommending, or identifying existing fonts - only for creating a font from the user's own handwriting.
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 SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical (restaurant, healthcare, legal, home services, real estate, automotive). Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "local rankings", "service area", "multi-location", or "local 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。
Localization and internationalization strategy for iOS/macOS apps. Covers market prioritization, language tier recommendations, minimum viable localization levels, translation workflows, cultural adaptation, localized ASO, and testing. Use when planning localization, expanding to new markets, deciding which languages to support, or planning translation 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。
App Store marketing strategy advisor that analyzes your app and recommends the best promotional features. Orchestrates sub-skills for implementation. Use when planning app promotion, launch strategy, user acquisition, or re-engagement campaigns.
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 designing, assessing, and optimizing Azure workloads using Azure Well Architected. Covers design review checklists, recommendations, design principles, tradeoffs, service guides, workload patterns, and assessment questions. Use when designing AI, HPC, SaaS, AVD/AVS workloads, choosing regions/AZs, or configuring core Azure services, and other Azure Well Architected related development tasks.
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Autonomous cloud-cost analyst across Neon, Vercel, Railway and GitHub Actions - pulls per-object usage, attributes it to the biggest drivers, root-causes each, and emits recommendations ranked by real signal (idle %, over-allowance, failure rate) with dollar figures only where the billing API returns real ones (and, armed, applies safe cost levers).
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 token recommendation and one prediction market pick - scored, quantified, with a skip branch when signals are weak
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。