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Use when adding an unspecified data source to an Expo/React Native Power Apps mobile app; routes to Dataverse, SharePoint, or another connector.

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Use when the user wants to read or write SharePoint lists, manage documents in a SharePoint document library, or create a new SharePoint list from a Power Apps mobile app.

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。

Creates the Tamagui brand system for an Expo/React Native Power Apps mobile app, including design-system.md, tokens.ts, and an HTML gallery.

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 the user has finished building a mobile app, started Metro with `npm run dev`, and wants the running app monitored for runtime errors AND silent failures (empty lists, blank screens, swallowed network errors) and fixed autonomously. Accepts a free-text symptom (e.g., `/debug-app "todos not appearing on home screen"`) to drive persisted-log diagnostics — injects temporary console.log statements at data-path boundaries, reads the sanitized `.powernative/metro-logs/` log, and cleans up logs after the root cause is fixed. Supports port/platform filtering, configurable clean-cycle and timeout limits, and watch-only `--no-fix` mode. Otherwise polls with a durable byte cursor, fixes inline or routes appropriately, verifies each fix from new output, and exits after the configured clean checks (default 3). Foreground loop — blocks the conversation while running. Run only after the app is loaded.

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Internal mobile-app workflow read and executed only by /setup-offline-profile to enable Dataverse table prerequisites for a Mobile Offline Profile.

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Use when the user needs to bind users or teams to a Mobile Offline Profile so they actually receive offline sync on their devices. Without this, the profile exists in Dataverse but no one's app uses it.

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Use when the user wants to design or redesign the Dataverse schema and connector plan for an existing mobile app, or has an ER diagram (image, Mermaid, or text) to apply. Skip when the user is creating a brand-new app — /create-mobile-app handles the data model inline.

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Configure optional customer-owned Application Insights telemetry for a Power Apps mobile app — enable it against an existing Azure resource, change the resource, or disable it. Standalone, and invoked by /edit-app.

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 (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus, Reg. (EU) 2026/1744, in force July 27, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of August 2026. GPAI enforcement powers active since August 2, 2026.

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Framework for demonstrating AI capabilities in legal contexts. Provides detailed personas across tenant law, business contracts, startup disputes, employment claims, and consumer protection with progressive complexity scenarios. Use when: (1) Demonstrating AI-powered legal triage or intake systems, (2) Showcasing responsible AI-assisted client interactions, (3) Training staff on appropriate AI use in legal contexts, (4) Creating realistic scenarios for legal tech presentations, (5) Developing educational materials about AI in legal services, or (6) Testing AI-powered legal information systems in controlled environments.

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 (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus adopted June 29, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of July 2026. GPAI enforcement powers activate August 2, 2026.

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 knowledge for Azure Resource Graph development including troubleshooting, best practices, decision making, limits & quotas, configuration, and integrations & coding patterns. Use when querying via CLI/PowerShell/REST, using GET/LIST vs Query, handling paging/quotas, or Power BI ARG reports, and other Azure Resource Graph related development tasks. Not for Azure Monitor (use azure-monitor), Azure Policy (use azure-policy), Cost Management (use azure-cost-management), Azure Resource Manager (use azure-resource-manager).

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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.

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。

Find similar vulnerabilities and bugs across codebases using pattern-based analysis. Use when hunting bug variants, building CodeQL/Semgrep queries, analyzing security vulnerabilities, or performing systematic code audits after finding an initial issue.

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Perform a focused security review of pending git changes to identify high-confidence security vulnerabilities with real exploitation potential. Use this skill when the user asks for a security review, security audit, vulnerability scan, or wants to check pending changes on a branch for security issues before merging. This is NOT a general code review.

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Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.

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Audit, redesign, generate, and improve IEEE manuscript figures, tables, captions, result presentation, plotting scripts, visual polish, hybrid Python/R plus vector-editor workflows, publication-ready matplotlib house style, and layout using routed visual-evidence checks. Use when checking or creating plot readability, axes, legends, line overlap, table structure, metric reporting, captions, grayscale accessibility, IEEE one-column/two-column layout, figure/table placement, claim-evidence mapping, reviewer first-impression risks, matplotlib/seaborn or ggplot2 figure code, SVG/PDF/TIFF/PNG export, Illustrator/Inkscape/PowerPoint/Figma finishing plans, or IEEE-style engineering evidence graphics.

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Use when your human partner has explicitly opted into exploratory or feasibility mode - a compute-heavy simulation, an unproven pipeline, an unbenchmarked solver, an untested cluster job - or when whether the work can run at all is still unknown, when a plan's largest configuration has never been executed, when a memory or wall-clock ceiling is estimated rather than measured, or when someone proposes pre-registering over runs that have never happened

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Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

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Position an AI product when everyone claims AI and skeptics call it "just a wrapper." Find the real wedge (data, workflow, trust, domain), make reliability the differentiator, answer "won't the big labs just build this," and stop leading with "AI-powered." Use when your AI or dev tool blends into a sea of similar demos, buyers doubt the accuracy, or you can't say why you win when the model is a commodity.

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Decision framework for choosing between consent and legitimate interest as the lawful basis for processing. Covers power imbalance indicators, conditionality prohibition under Article 7(4), granularity requirements, the three-part LIA test (purpose, necessity, balancing), and practical decision trees for common scenarios.

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Use when designing organizational structure (team topologies, Conway's Law alignment), mapping stakeholders by power-interest for change initiatives, defining team interface contracts (APIs, SLAs, decision rights, handoffs), assessing capability maturity (DORA, CMMC, agile maturity models), planning org restructures (functional to product teams, platform teams, shared services), or when user mentions "org design", "team structure", "stakeholder map", "team interfaces", "capability maturity", "Conway's Law", or "RACI".

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Use when user asks to translate videos, dub video content, or localize videos into other languages. Translate videos with AI-powered dubbing using iFlytek (Xfei) Video Translation API. Supports video language translation, voice dubbing, multilingual video localization, 视频翻译, 视频配音, 视频本地化. Features task creation, task list, task details retrieval. Ideal for content creators and localization teams.

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Warren Buffett said he looks for "economic castles protected by unbreachable moats." A moat is a sustainable competitive advantage that protects your business from competitors. Without a moat, success attracts competition that erodes your margins to zero. This skill covers identifying, building, and deepening moats. Network effects, switching costs, brand, scale economies, and the rarer moats like regulatory capture and counter-positioning. Use when "moat, defensibility, competitive advantage, network effects, switching costs, barrier to entry, unfair advantage, protect from competition, sustainable advantage, winner take all, flywheel, lock-in, moat, defensibility, strategy, network-effects, switching-costs, competitive-advantage, seven-powers" mentioned.

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。