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Use this Canvas-owned clean-room skill to guide collaborative writing for documentation, proposals, technical specs, decision records, PRDs, RFCs, operating guides, and structured memos. Trigger when the user wants help drafting, organizing, refining, reviewing, or testing a document before sharing it.

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[Code Quality] Use when you need to validate a PBI against Definition of Ready before grooming.

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[Content] Use when you need to build structured learning/teaching course material with Bloom taxonomy objectives, modules, lessons, exercises, and assessments.

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 a HyperFrames composition needs a punch-in, punch-out, zoom, reframe, Ken Burns treatment, camera move, visual match/whip handoff, or other seek-safe 2D/3D keyframes; also for GSAP, CSS keyframes, Anime.js, WAAPI, FLIP, paths, masks, SVG morph/draw, text trails, 3D depth, or `hyperframes keyframes` diagnostics. Don't use for broad scene strategy, brand design, media sourcing, captions, or general video planning.

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Author a complete both-seats coaching lesson for a BBA-NATIVE bidding scenario and produce all served artifacts in one consistent pass: curate the pool → tokenized coaching-curated/<scn>.pbn → gated promote to served coaching/<scn>.pbn → resolve to South=student coaching-non-rotated/<scn>.pbn (bridge-classroom) → update both toc.json files. Invoke as `/coach-lesson <Scenario>` (e.g. `/coach-lesson Fourth_Suit_Forcing`). Use ONLY for conventions BBA bids natively (its bba/<scn>.pbn [Auction] is the answer key — Basics, New_Minor_Forcing, Fourth_Suit_Forcing). For conventions BBA CANNOT bid (Spiral Raises), use the py/spiral_auction.py substitution path instead — not this skill.

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Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).

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。

Add Emacs as a channel. Opens an interactive chat buffer and org-mode integration so you can talk to NanoClaw from within Emacs (Doom, Spacemacs, or vanilla). Uses a local HTTP bridge — no bot token or external service needed.

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。

Add Emacs as a channel. Opens an interactive chat buffer and org-mode integration so you can talk to NanoClaw from within Emacs (Doom, Spacemacs, or vanilla). Uses a local HTTP bridge — no bot token or external service needed.

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。

Judge performance or resource claims on real hardware: benchmark design or review, latency, throughput, memory or headroom numbers, and accept-or-reject decisions on optimizations. Not for refactors with no resource claim.

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Reviews a proposal, business case, deck or plan in character as a Customer Advocate archetype, the seat for paying customers not in the room, and returns a DRAFT review in the chat with a verdict, findings that cite the exact passage, customer-side risks, what would change the verdict and five interrogation questions. Use when the user asks to "run a customer advocate review", "do a voice-of-the-customer pressure-test", "what would our customers say about this" or "what does this plan really ask of paying customers". Do not use for the product evidence or roadmap case, use cpo-reviewer instead; for revenue or pricing strategy, use cro-reviewer; for the staff who operate the change, use frontline-skeptic-reviewer. Drafts for human review; never approves, authorises or signs off.

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Use when the user asks to "run my launch day", "build a launch day runbook / war room", or "decide CONTINUE or ROLLBACK after the push"; produces a pre-conditions gate check (launch-readiness-auditor SHIP verdict + the authoritative date in launch-registry — missing either stops the skill), a dated hour-blocked runbook with owners (morning irreversible pushes, daytime monitoring loop, evening consolidation), a forced observation-window verdict after every irreversible action against pre-declared kill criteria, a P0-P3 incident ladder with rollback playbooks, and T-0 status lines for the registry proposal protocol. Not for channel submission content and platform rules — use community-launch-runner; not for media replies — use press-media-relations. 发布日runbook/作战室/观察窗/回滚裁决/发布日指挥

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.

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。

Ensure architecture docs capture availability targets, failover strategy, capacity headroom, and resilience trade-offs for critical services.

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。

Show the factory control room from live GitHub labels, open PRs, recent run records, gate health, and charter limits.

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 with Azure Boards Deep — deep Azure Boards management covering work items, sprints, backlogs, queries, and team velocity analytics. Use when performing deep audits of Azure DevOps Boards, analyzing sprint health, reviewing backlog grooming, or assessing team capacity and velocity across Azure DevOps projects.

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。

Control Philips Hue lights, scenes, rooms via OpenHue CLI.

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。

Home Artificial Intelligence by Lynn Greiner Many autonomous agents doomed by governance failures Ne

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。

Generate walkthrough videos from Stitch projects using Remotion with smooth transitions, zooming, and text overlays

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。

Multi-agent SWE team built on the Workshop model. Full-stack vertical workers, meeting room with @mention notification, private pipes, shared task board. Git worktree isolation, Leader-driven coordination. Mixed Codex/Claude Code engine. Use when a task needs engineering depth beyond a single agent. NOT for simple task parallelism (use agent-task-orchestration) or design discussions (use agent-brainstorm).

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。

Multi-agent brainstorming: async opinion collision with expert personas. Mixed Codex/Claude Code engine. Use when multiple AI agents need to collaboratively discuss, debate, and converge on solutions through structured dialog. Triggers: brainstorm, multi-perspective discussion, opinion collision, expert debate, war room, collective intelligence, 头脑风暴, 多视角讨论, 集思广益.

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。

Animation, morphing, and shader best practices for Liquid Glass and SwiftUI motion on iOS 26+/macOS 26+ through iOS 27. Use when animating glass or building UI motion: glassEffectID, @Namespace morphing, GlassEffectContainer spacing, glassEffectUnion, glassEffectTransition (.matchedGeometry/.materialize), withAnimation, springs, UnitCurve, PhaseAnimator, KeyframeAnimator, @Animatable, matchedTransitionSource, navigationTransition zoom, scrollTransition, visualEffect, Metal shaders (colorEffect, distortionEffect, layerEffect, [[stitchable]]), accessibilityReduceMotion gating, Instruments/xctrace profiling of hangs and animation hitches. Also fires on symptoms: glass morph not animating, view pops in instead of morphing, glass fades instead of materializing, morph flickers with Reduce Motion, menu glass morphs as a rectangle, animation runs on insert but not removal, PhaseAnimator loops forever or fires once, shader shows clipped edges (maxSampleOffset), animation hitches or main-thread hangs while scrolling glass chrome. For static glass, availability gating, toolbars, UIKit/AppKit glass, or accessibility of the material itself use the liquid-glass skill first.

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 on any Java or JVM project: plain Java, libraries, CLI tools, Maven/Gradle builds, Spring Boot, Jakarta EE, MyBatis/MyBatis-Plus, JPA/Hibernate, Lombok, Java tests, Java code review, API design, exception handling, security review, refactoring, dependencies, Java version upgrades, javax/jakarta migrations, or JVM runtime troubleshooting such as GC, OOM, thread dumps, deadlocks, high CPU, and performance issues.

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。

Control smart-home accessories and commission Matter devices using HomeKit and MatterSupport. Use when managing homes/rooms/accessories, creating action sets or triggers, reading accessory characteristics, onboarding Matter devices, or building a third-party smart-home ecosystem app.

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Fetch Airbnb income estimate (ADR, occupancy, monthly revenue) for any area using the official Airbnb GraphQL API — the same data the Airbnb host simulator shows. Input: station/area name. Output: per-night rate, avg nights, monthly estimate for entire_home and private_room.

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。