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Mandatory external verification workflow for ALL non-trivial factual claims before presenting them as true. This skill MUST be used whenever Claude is asked to research any topic, answer factual questions, provide current information, draft documents containing factual claims, give legal advice or cite legal authority, discuss current events or public figures, provide technical or scientific information, state statistics or data points, or answer ANY question where the answer could have changed over time. Also trigger when the user asks Claude to 'check', 'verify', 'confirm', 'research', 'look up', or 'find out' anything, or when the user needs information that requires freshness, precision, or source accuracy. This skill overrides any default tendency to answer from memory. If a task involves stating facts, citing sources, or providing current information — use this skill.

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Expert ITAR compliance advisor for US defense contractors, exporters, and manufacturers. Use this skill for any question about 22 CFR Parts 120-130, the United States Munitions List (USML), DDTC registration, export license applications (DSP-5/73/94), Technical Assistance Agreements (TAA), Manufacturing License Agreements (MLA), brokering regulations (Part 129), deemed export rules for foreign nationals, technology control plans, voluntary disclosures, violation mitigation, jurisdiction determination (ITAR vs EAR), or US Munitions List category scoping. Trigger even if the user doesn't say "skill" — any ITAR or US defense export control question should use this skill.

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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.

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Expert GDPR compliance assistant covering all four core workflows: (1) auditing code and systems for GDPR violations, (2) drafting GDPR-compliant documents such as privacy policies, Data Processing Agreements (DPAs), and consent notices, (3) answering GDPR compliance questions with authoritative article citations, and (4) reviewing data flows and PII handling practices. Use this skill whenever the user mentions GDPR, data protection, privacy compliance, lawful basis, data subject rights, DPA, privacy notices, consent management, data breaches, DPIAs, controller/ processor relationships, cross-border data transfers, or any EU/UK data privacy topic. Also trigger for questions like "is this GDPR compliant?", "how do I handle personal data?", "what does a privacy policy need?", or any request involving PII, personal data, or data retention in a regulatory context.

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Use this skill when the user wants an AI governance, legal-risk, privacy, compliance, procurement, or vendor-risk review of an internal AI use case, an AI product feature, an LLM workflow, or a third-party AI vendor. The skill asks intake and clarifying questions first when facts or evidence are missing, identifies required documentation and missing evidence, maps the use case to AI governance frameworks and applicable legal domains, and produces a preliminary or final governance review with scorecards, findings, owners, remediation actions, and follow-up questions.

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Assistant juridique expert en droit français. Recherches, consultations, rédaction d'actes, contre-argumentaires, analyses de contrats et de pièces, veille, vérification et harmonisation de références. Produit des documents Word (computer use) ou des réponses conversationnelles structurées. MCP : OpenLegi, Themia, LegalDataHunter. Déclencher pour toute question juridique, tout concept de droit français (contrat, responsabilité, jurisprudence, code civil, tribunal, indemnisation), toute question sur le droit de l'UE, la CJUE ou la CEDH, et toute demande de vérification ou harmonisation de références bibliographiques juridiques.

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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.

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Generate elaborative interrogation prompts that deepen encoding through targeted why and how questions. Use when students memorise without understanding or need deeper processing of content.

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Design a project-based learning brief with a driving question, milestones, and assessment criteria. Use when planning PBL units, inquiry projects, or extended investigations.

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Design a multi-round questioning sequence for interrogating AI chatbot answers, tracking how responses shift and distinguishing genuine updates from sycophantic capitulation. Use when teaching students to probe AI critically.

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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.

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Validates business ideas through customer conversations using Rob Fitzpatrick's Mom Test framework. Use when planning user interviews, getting product feedback, validating ideas, designing discovery questions, or extracting honest signal from biased customer responses. Covers good vs bad questions, deflecting compliments, anchoring fluff, pressing for commitment, and customer slicing.

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Use when the user mentions Marketing Studio, DTC Ads, ad video, UGC video, the marketing_studio_video MCP model, or wants to generate one of the 9 Marketing Studio ad presets (UGC, Tutorial, Unboxing, Hyper Motion, Product Review, TV Spot, Wild Card, UGC Virtual Try On, Pro Virtual Try On). Also triggers on hook+setting picklist questions, preset-avatar / custom-avatar / text-generated-avatar handling, 4–15s ad video questions, or any reference to Higgsfield's ad-video product surface. Cross-surface workflow handoffs (GPT Image 2.0 / Soul Cinema / Nano Banana Pro / ms_image image gen → Marketing Studio video) covered in the companion cross-surface-workflow.md. Also use for Ad Multiplier (`ad_multiplier` — 'multiply my ad', many independently edited versions of one supplied 4–30s ad) and for Genjutsu one-off edits of a finished ad (swap one object / product / garment → `hf_mult_replace_object`; transfer motion → `hf_mult_motion_control`).

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Any live-web task via the Firecrawl CLI — including ordinary web research: searching the web, reading or extracting pages, gathering sources, discovering site URLs, bulk extraction, downloading a site, change alerts, or pages needing clicks/login — web only; local files route to firecrawl-parse. For US legal or regulatory questions use gov; for papers use firecrawl-research-index; for library, API, error, or bug questions use firecrawl-developer-index.

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Customise the prd-taskmaster plugin workflow via curated brainstorm questions. The AI asks, the user answers in plain English, and the skill writes their preferences to .atlas-ai/config/atlas.json. Future runs of prd-taskmaster read that file and apply user preferences to phase gates, validation strictness, default provider, preferred execution mode, and template choice. For deeper tweaks beyond the curated questions, users can hand-edit files in .atlas-ai/customizations/. Use when the user says "customise workflow", "customize workflow", "adjust my PRD settings", "tune the skill", or wants to change how prd-taskmaster behaves.

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Phase 3 of the prd-taskmaster pipeline: smart mode selection and user handoff. Detects installed capabilities (superpowers, ralph-loop, task-master-ai, playwright, research providers), recommends ONE execution mode (A/B/C) with reasoned justification, appends the task-execution workflow to CLAUDE.md, surfaces a structured AskUserQuestion multi-option picker for user agency, and dispatches the chosen mode. Mode D (Atlas Fleet) is selectable only when detect_capabilities returns tier=premium (licensed atlas-launcher detected); otherwise it is a locked Atlas Pro teaser. Plan Mode is NOT used (spec section 13.5): AskUserQuestion is the sole user-agency mechanism. Declares HANDOFF complete so EXECUTE can follow.

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Use when a phase has unresolved decisions before planning and no CONTEXT.md exists. Asks adaptive questions on the phase's gray areas and writes {phase_num}-CONTEXT.md with locked decisions; --auto, --chain, --power change the questioning.

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Use this skill instead of WebSearch for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.

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维护中文毕业论文的 `codex_md/question_list.md`：把本轮问题、边界、优先级、协作方案和验收口径结构化，作为整条 thesis pipeline 的控制面。

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Retrieves up-to-date documentation, API references, and code examples for any developer technology. Use this skill whenever the user asks about a specific library, framework, SDK, CLI tool, or cloud service — even for well-known ones like React, Next.js, Prisma, Express, Tailwind, Django, or Spring Boot. Your training data may not reflect recent API changes or version updates. Always use for: API syntax questions, configuration options, version migration issues, "how do I" questions mentioning a library name, debugging that involves library-specific behavior, setup instructions, and CLI tool usage. Use even when you think you know the answer — do not rely on training data for API details, signatures, or configuration options as they are frequently outdated. Always verify against current docs. Prefer this over web search for library documentation and API details.

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Own concrete MSBuild ItemGroup and item-expression questions. USE FOR: Include, Remove, Update, item identity and metadata, transforms, filtering, batching, duplicate or overlapping items, and reviews that verify whether those operations are correct. Generated items stay in scope when the central defect is item identity, metadata, batching, duplicate declarations, or glob/Remove/Update semantics. For a generated artifact missing from compilation or output, wrong target timing/path, or FileWrites clean tracking without a broader item-semantics defect, use including-generated-files. The item operation may be broken, suspected, or already correct. Exclude property-only issues, general incrementality with no item question, broad reviews with no concrete item concern, and non-MSBuild systems.

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Guidance for implementing CollectionView in .NET MAUI apps — data display, layouts (list & grid), selection, grouping, scrolling, empty views, templates, incremental loading, swipe actions, and pull-to-refresh. USE FOR: "CollectionView", "list view", "grid layout", "data template", "item template", "grouping", "pull to refresh", "incremental loading", "swipe actions", "empty view", "selection mode", "scroll to item", displaying scrollable data, replacing ListView. DO NOT USE FOR: simple static layouts without scrollable data (use Grid or StackLayout), map pin lists (use Microsoft.Maui.Controls.Maps), table-based data entry forms, non-MAUI list controls, CarouselView or BindableLayout questions, platform-specific handler or renderer customization, diagnosing CollectionView bugs in the MAUI framework itself, or general MVVM/binding questions that merely happen to mention a list (use maui-data-binding).

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Query and answer questions about Volcengine API specifications. Trigger this skill whenever a user asks about Volcengine API parameters, error codes, request methods, enum values, required fields, response structures, pagination, parameter dependencies, or API comparisons — even if they don't explicitly say "API". Typical triggers include questions like "What parameters does DescribeInstances have?", "What values does Status support?", "What does InvalidInstanceId.NotFound mean?", "Does Volcengine have a batch tag deletion API?", "Which APIs does ECS support?", "How do I pass parameters to CreateDatabase?", "Is ChargeType required?", "What fields does DescribeInstances return?", "How do I paginate instance lists?", "What's the difference between these two APIs?", "RunInstances returns InvalidParameterValue". When the user needs runnable SDK code, hand off to the volcengine-sdk-generator skill. When the user needs CLI-based operations, hand off to the volcengine-cli skill. Supports both Chinese and English prompts.

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Answer a hard question by fanning it out to a PANEL of models running in parallel — each answering independently with web search and bash, none seeing the others' work — then having Opus 4.8 judge every response into a structured analysis (consensus, contradictions, partial coverage, unique insights, blind spots) and write a final answer grounded in it. The panel is two independent Opus 4.8 runs (slug opus4.8-4.8), Opus 4.8 + GPT-5.5 via codex (opus4.8-gpt5.5), Opus 4.8 + Gemini 3.1 Pro via agy (opus4.8-gemini3.1pro), or all three (opus4.8-gpt5.5-gemini3.1pro). Opus always judges and writes the final answer — the pipeline can't be reversed. Runs on local CLI subscriptions (no metered API), and saves a timestamped provenance .md per run. Use this whenever the user asks to "run it through Fusion", says /fusion, wants a multi-model / panel / ensemble answer, wants a question cross-checked across models, or wants a higher-confidence answer with consensus and blind spots surfaced — even if they don't say "fusion". General-purpose: any topic (research, law, strategy, technical, personal). Best for high-stakes research, design calls, and debugging where being confidently wrong is expensive.

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。