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概要と使いどころ
"Implement matrix factorization to decompose user-item interaction matrices into latent factor representations. Use this skill when the user needs scalable collaborative filtering, latent feature discovery, or dimensionality reduction for recommendation — even if they say 'SVD recommendations', 'latent factors', or 'factorize the rating matrix'.".
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。
Create user stories from feature descriptions with interactive Q&A and automated validation
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。
Validate code quality, test coverage, performance, and security. Use when verifying implemented features meet all standards and requirements before marking complete.
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。
Design system architecture, API contracts, and data flows. Use when translating analyzed requirements into technical design for feature implementation.
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。
Stack-specific security implementation guidance (backend, frontend, database, infrastructure, operations) with auto-detection from your changes. Use when implementing a feature securely or hardening code as you write it. Trigger on "how do I secure this", "security best practices", "is this secure", "harden this". For scanning existing code use /security-verify.
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 CHANGELOG entries following conventional commits format with semantic versioning. Use when finalizing features to maintain proper change history.
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。
Update all project documentation including implementation docs, user guides, API docs, and architecture diagrams. Use when finalizing features to ensure comprehensive documentation.
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。
Create comprehensive pull requests with detailed descriptions, test plans, and proper git workflow. Use when finalizing features to ensure high-quality PRs.
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。
Audits whether a machine-readable regelrecht law model is faithful to the LETTER of the wettekst — and strictly separates the wettekst (leading) from the toelichting (Nota/Memorie van Toelichting; explanatory, not a norm). Runs in critical passes per article/lid and detects four deviation classes: toelichting-bleed (a modeled criterion that the letter only states as an open norm, or not at all), missing verbatim elements (a word/condition such as a "tenzij" or a qualifier dropped from the model), wrong legal_basis anchoring (an endpoint hung on a "kapstok"-article while the operative norm lives elsewhere), and verbatim-drift in text: (an ingetrokken/older redaction). Also detects positief-lid vs uitsluitings-lid conflicts and checks the chapeau for an explicit derogation rule ("zo nodig in afwijking van het eerste lid") before judging precedence. Classifies every finding as modelfout (fix), wettekst-gevolg (report, do not fix), or letter-vs-toelichting-question (jurist decides what is leading + revision signal). Use when the user wants to know if a model is "dicht bij de letter", mentions letter vs toelichting/bedoeling, fidelity, "natuurgetrouw", or before trusting feature outcomes.
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。
Maakt regelrecht-scenario's vindbaar en hun norm-ketens leesbaar/geassert. Aan de invoerkant — vertaal een platte lijst leaf-parameters naar benoemde casus-assen + een herbruikbare persona-bibliotheek, met een reverse-lookup casus-matrix. Aan de uitvoerkant — maak de norm-keten zichtbaar via keten-checkpoints (assert elke knoop op het kritieke pad, niet alleen de endpoint) en golden-trace-snapshots + branch-coverage. Gebruik dit bij het schrijven of opschonen van scenario-/feature-files, het terugvinden of ontwerpen van een specifieke casus in een grote variabelenlijst, het reviewen van engine-traces, of het regressie-bestendig maken van cross-law-ketens zodat een ketenfout niet stil achter een correcte endpoint verdwijnt. Dossier-agnostisch; de regelrecht-methode is de vaste taal. Haakt aan op regelrecht-stelselanalyse (engine-tests / meta-check) en regelrecht-audit-products (testcase-scenario's).
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。
Plan, build, scaffold, or safely extend Atlassian Forge apps using current official documentation. Use for fresh Forge apps, existing-app feature work, module and manifest changes, UI Kit or Custom UI implementation, backend functions and events, Atlassian or external APIs, storage, permissions, environment configuration, distribution-affecting implementation, validation, and explicitly authorized deployment or installation; route standalone debugging and specialist reviews to their dedicated skills.
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。
Build a Go-to-Market (GTM) plan for new product launches, market entries, or feature rollouts. Covers market sizing (TAM/SAM/SOM), positioning, messaging, channel strategy, pricing approach, and launch timeline. Use when preparing to bring something new to market. Do not use for ongoing marketing operations or campaign execution.
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。
Design rigorous A/B test plans with hypothesis, sample size calculation, Minimum Detectable Effect (MDE), randomization strategy, and decision rules. Includes guardrail metrics and rollout playbook. Use when planning product experiments, conversion optimization, or data-driven feature decisions.
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。
Build competitive intelligence briefs following the CI cycle — Plan, Collect, Analyze, Disseminate. Profile competitors, create feature comparison matrices, analyze pricing, build positioning maps, and identify win/loss patterns. Use when preparing for product launches, sales enablement, or strategic 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。
Use when implementing a PlayCanvas feature the project does not use yet, to find and adapt the official example matching the installed version.
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。
Route a vague Prisma Next prompt to the right specific skill. Use for "help me with Prisma Next", "what is Prisma Next", "explain Prisma Next", "I'm new to PN", "where do I start", "what can I do with Prisma Next", "what can I do next with Prisma", "just ran createprisma", "tour of Prisma Next", "Prisma Next overview", and comparison questions like "Prisma Next vs Prisma 7", "PN vs Drizzle", "PN vs Kysely", "PN vs TypeORM". Do NOT use when the prompt clearly matches a workflow skill — adoption / quickstart / first-touch orientation / brownfield introspection, schema / contract editing, migration authoring (db update / migration plan / migrate), migration review on deploy / concurrent migrations, queries / db.orm / db.sql / TypedSQL, runtime / db.ts / middleware wiring, build / Vite plugin / Next.js plugin, debug / structured error envelopes / PN-* error codes, or feedback / bug report / feature request — load that sibling skill directly.
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。
Task Breakdown Command. Breaks a feature into discrete, dependency-ordered implementation tasks from a PRD and tech spec. Creates a task list and individual task files with no approval prompt. When run inside the `/start-job` pipeline, return control to that orchestrator so it can invoke the implementation phase. When invoked standalone, stop after generating tasks and prompting for the next step. Use when the user says "break this down into tasks", "create tasks", "task breakdown", or after the tech spec is complete and the user wants to plan implementation.
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 building Django applications - security hardening, authentication and permissions, ORM optimization, PostgreSQL features, Django 6.0, migrations, testing, and ecosystem libraries
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。
Implements features in vertical slices, always starting with homepage. Enforces anti-placeholder rules — no lorem ipsum, no TODO stubs, no gray boxes. Real content, real images, real interactions. TypeScript strict mode, Zod validation, and structured file organization.
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。
Full instrumentation from day one. PostHog consolidates product analytics + feature flags + error tracking (one platform, one bill). GA4 via GTM (14-step automation, custom dimensions over events, server-side tagging). Sentry (deep error tracking + performance). Stripe (webhook-first with idempotent processing). Listmonk on Coolify (newsletters via Amazon SES SMTP relay). PLG 7-layer framework. Programmatic SEO (5 page types). Incident auto-remediation via Sentry→Inngest pipeline. AI search (GEO) awareness. Local business conversions (phone_click, direction_click, form_submit, booking_click) with CRO patterns for both SaaS and local.
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。
Frappe Gameplan guidance for team discussions, projects, tasks, notes, decisions, async collaboration, permissions, and workspace workflows. Use when work touches Gameplan or planning/collaboration features in the Frappe ecosystem.
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。
NgRx SignalStore patterns for Angular state management. Use when managing client state with Angular Signals, composing store features, handling entities, or integrating RxJS effects.
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。
React Native mobile development patterns - New Architecture (Fabric, TurboModules, JSI), component architecture, React Navigation 7+, FlashList v2 optimization, gestures with Reanimated 4, platform-specific code, React 19 features
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。
PostHog analytics and feature flags setup
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。