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Draws the 12 Houses of the Zodiac Tarot spread to inject entropy into planning when prompts are vague, ambiguous, or casually delegated. Interprets the spread to guide next steps. Use when the user says 'let fate decide', 'YOLO', 'whatever', 'idk', or other nonchalant phrases, makes Yu-Gi-Oh references, or when you are about to arbitrarily pick between multiple reasonable approaches. Prefer over asking clarifying questions when the user's tone is casual or playful rather than precision-seeking.

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Use when reasoning about what survives compaction/context-loss/restart/seat-refresh, designing a high-fidelity restore packet, or distinguishing native runtime resume vs fork vs artifact-backed mental-model rebuild. Covers the 4 failure modes that prevent honest restore (compacted seat drops hot potato; restore packet preserves details but loses product intent; runtime resume mistaken for handover or fork; rebuilt seat starts with stale instructions).

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Use when replacing a seat's occupant (rebuild/handover/swap), reasoning about stable-seat-identity vs fluid-occupant-identity, choosing an old-occupant disposition (retire/advise/shadow), or recording provenance for an occupant change. Two independent outcomes (continuityOutcome + seatBindingOutcome) and the 5 failure modes that prevent silent dishonesty.

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Use when authoring or installing a rig bundle (packaged, shareable artifact that instantiates an opinionated OpenRig topology + workflow), reasoning about the bundle vs extension boundary, or auditing a bundle for portability. Covers the 4 failure modes that prevent bundles from working anywhere but the operator's machine, and the inspect-before-install discipline.

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Use when reasoning about the rig lifecycle operations family (create / start / stop / resume / restore / snapshot / release / unclaim / destroy), reading or trusting `rig ps` / lifecycle projections after recovery, or designing proof for a lifecycle scenario. Covers the 4 failure modes (auto-restore creates partial rig; projections report healthier than reality; provider auth treated as impl work; resume succeeds for one runtime fails another) plus the restore-honesty rule (failed resume is FAILED loudly — no auto fresh fallback).

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Activate when the user explicitly names the narrator skill or requests production-ready narration in either case: (1) numbered takes fitted to fixed video windows, or (2) one continuous long-form story read in a locked voice that must be measured and retried for timbre or internal pauses. Collect missing text, voice_id, or voice_type after activation; their absence is not a reason to skip this workflow. Own pacing, speech-duration gates, retries, and ready audio delivery; never time-stretch. Do not use for ordinary TTS, "read this aloud", an unconstrained voiceover, voice browsing, music, SFX, singing, dubbing existing speech, or voice cloning.

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Build data visualizations with declarative grammars. Use when the user needs Vega-Lite, Vega, Observable Plot, or grammar-of-graphics reasoning, especially for tabular charts that do not require bespoke rendering.

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Use when testing, debugging, or making targeted improvements to rendered frontend apps through the Build Web Apps or web dev plugin: local dev servers, UI regressions, interaction bugs, console errors, responsive layout, and visual QA. Check whether the Browser plugin is available and use it first when it is; otherwise use regular Playwright with the recorded reason.

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Creates Architecture Decision Records (ADRs) to document significant architectural choices and their rationale for future team members. Use when the user says "write an ADR", "document this decision", "record why we chose X", "add an architecture decision record", "create an ADR for", or wants to capture the reasoning behind a technical choice so the team understands it later. Do NOT use when the decision hasn't been made yet (use create-rfc instead), for implementation planning (use technical-design-doc-creator), or for general documentation.

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Put weather into a Three.js scene that reads as weather — rain anchored inside the frustum, a storm that is the rain leaned on rather than a second system, lightning on its own light with thunder scheduled by distance, snow that blows up into blizzards and keeps settling until the ground goes white, wet ground with puddles and splashes, and looping ambience that has no seam. Use for scene atmosphere, seasonal states, hero backdrops, or any world where clear/rain/storm/snow needs to be a control the viewer can turn.

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Etsy category page scraper: given an Etsy category URL (e.g. https://www.etsy.com/c/jewelry) and optional page number, returns paginated product listings with listingId, shopId, title, url, image, salePrice, originalPrice, currency, rating, reviewCount, shopName, isAd, freeShipping, badge from category and subcategory pages. Use when user mentions Etsy category, Etsy c/ URL, etsy.com category, browse Etsy categories, scrape Etsy category, Etsy category listing, extract Etsy category products, Etsy subcategory, Etsy jewelry, Etsy clothing, Etsy home decor, Etsy wedding, Etsy accessories, Etsy craft supplies, Etsy vintage category, Etsy category page scraper, Etsy taxonomy, get all products from an Etsy category, bulk category export from Etsy, Etsy niche scraping. Also applies to trend research within a specific Etsy category, competitor benchmarking within a category, sourcing suppliers by category, seasonal product monitoring, and paginated bulk collection driven by a category URL.

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Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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Design and verify a deterministic operational-value grader for any GitHub Agentic Workflow. Use when reasoning from workflow goals to measurable downstream outcomes, defining repository evidence, choosing outcome metrics, or creating an operational-value evaluator. Usage: /operational-value-designer OWNER/REPO WORKFLOW-NAME.

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Compare options with weighted scoring, pros and cons, pre-mortems, opportunity costs, and ICE prioritization. Use when a user wants to reason through a choice, expose assumptions, or rank alternatives.

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Defamiliarization audit for empirical output. Systematically interrogates every feature of a figure, table, or set of results — not just the main finding. Named for Jason Fletcher, who asked about the spike at t=1 when everyone else was looking at t=2. Use when you have output and are about to interpret or report it.

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Econometrics skill for time series analysis. Activates when the user asks about: "time series", "stationarity", "unit root test", "ADF test", "KPSS test", "ARIMA", "ARMA", "autocorrelation", "ACF", "PACF", "VAR model", "VECM", "Granger causality", "cointegration", "impulse response function", "forecast", "seasonal decomposition", "ARCH", "GARCH", "时间序列", "平稳性检验", "单位根", "自回归", "格兰杰因果", "协整", "脉冲响应", "预测", "向量自回归"

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Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?"

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Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", "systematic review", or mentions of writing academic reviews on deep learning for medical imaging.

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Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

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Projects your ROAS for the next 30, 60, and 90 days based on current performance trends, seasonality patterns from your historical data, and planned budget or campaign changes. Gives you a range with confidence intervals, not a single number pretending to be precise. Platform: Google and Meta.

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Use this skill when building real-time, bidirectional streaming applications with the Gemini Live API, or migrating legacy Live models (2.0/2.5/3.1) to Gemini 3.8 Live. Covers WebSocket-based audio/video/text streaming, voice activity detection (VAD), background reasoning (extended thinking), asynchronous function calling, session management, ephemeral tokens, live transcription, and live translation. SDKs covered - google-genai (Python), @google/genai (JavaScript/TypeScript).

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Design, build, debug, and optimize OpenRouter API integrations. Use for any OpenRouter API work, including models, reasoning, routing, media, tools, structured output, cost, or performance.

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AI-powered web search, research, and reasoning via Perplexity

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Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.

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