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概要と使いどころ
Android Jetpack Compose Expert workflow skill. Use this skill when the user needs Expert guidance for building modern Android UIs with Jetpack Compose, covering state management, navigation, performance, and Material Design 3 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Extract a founder's or expert's point of view — core beliefs, contrarian takes, origin stories, taste — and synthesize a recommended "one big idea" (OBI) that anchors thought leadership and brand voice. Writes to marketing/expert-pov/expert-pov.md. Triggers - "expert pov", "founder point of view", "thought leadership angle", "one big idea", "OBI", "founder beliefs", "founder narrative"
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。
Modern PHP idioms for any PHP project (PHP 8.1+): readonly properties, enums, match expressions, constructor property promotion, typed properties, named arguments, nullsafe operator, first-class callable syntax, strict_types, PSR-12 style, and null discipline. Apply when the project is a PHP 8.1+ project. Stack-agnostic — referenced by every PHP plugin in the marketplace. Use this skill to: - Write strongly-typed, immutable-by-default value objects with readonly properties and enums. - Replace switch ladders and magic constants with match expressions and backed enums. - Cut constructor boilerplate with property promotion and express optionality with typed nullable returns. - Keep code PSR-12 compliant with declare(strict_types=1) in every file. Do NOT use this skill for: - Framework-specific idioms (Eloquent, Doctrine, Symfony services, Laravel facades — those live in framework plugin skills). - Composer / autoloading / dependency management — see php-foundation:composer-tooling. - Testing patterns — see php-foundation:php-testing.
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。
Builds comprehensive design systems and design bibles with production-ready CSS. Expert in design tokens, component libraries, CSS architecture. Use for design system creation, token architecture, component documentation, style guide generation. Activate on "design system", "design tokens", "CSS architecture", "component library", "style guide", "design bible". NOT for typography deep-dives (use typography-expert), color theory mathematics (use color-theory-palette-harmony-expert), brand identity strategy (use web-design-expert), or actual UI implementation (use web-design-expert or native-app-designer).
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。
Determines whether to screen a patient with hypertension for primary aldosteronism based solely on the presence of hypertension. Use when a clinician asks whether to screen a hypertensive patient for PA, triggered by phrases such as "should we screen for aldosteronism" or "evaluate for secondary hypertension".
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。
This skill should be used when the user asks to "calculate property division", "離婚の財産分与", "財産分与計算", "清算金", "夫婦共有財産の分与", or wants deterministic computation of divorce property division under 民法 768.
Builds comprehensive design systems and design bibles with production-ready CSS. Expert in design tokens, component libraries, CSS architecture. Use for design system creation, token architecture, component documentation, style guide generation. Activate on "design system", "design tokens", "CSS architecture", "component library", "style guide", "design bible". NOT for typography deep-dives (use typography-expert), color theory mathematics (use color-theory-palette-harmony-expert), brand identity strategy (use web-design-expert), or actual UI implementation (use web-design-expert or native-app-designer).
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。
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
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。
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.
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。
Writes, reviews, and debugs property-based tests — Hypothesis, fast-check, proptest, jqwik, rapid, and Echidna or Medusa for Solidity invariants. Use whenever tests should cover a whole input domain instead of a hand-picked list of examples: encode/decode and serialize/deserialize pairs, parsers, canonicalizers and normalizers, validators, numeric and Decimal types, comparators and sort order, data structures, and smart-contract state invariants. Also use when adding cases to an existing @given, fast-check, or proptest suite, when judging whether existing property tests assert anything real, and when a generator has shrunk a counterexample and you need to tell a wrong property from a genuine bug. Not for coverage-guided binary fuzzing (libFuzzer, AFL), mutation-testing campaigns, static analysis, benchmarking, or end-to-end UI tests.
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。
Fetches complete Airbnb listing details for a given numeric listing ID via the internal GraphQL API, returning title, room type, description, amenities, photos, coordinates, city, house rules, highlights, ratings, review count, bedroom configuration, and property overview. Use when user mentions Airbnb listing details, Airbnb property info, Airbnb room details, get Airbnb listing data, Airbnb amenities list, Airbnb house rules, Airbnb property description, Airbnb detail page scraper, Airbnb rooms detail, Airbnb property page data, Airbnb listing info, fetch Airbnb room details, pull Airbnb listing.
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。
Property-based testing with fast-check (TypeScript/JavaScript) and Hypothesis (Python). Generate test cases automatically, find edge cases, and test mathematical properties. Use when user mentions property-based testing, fast-check, Hypothesis, generating test data, QuickCheck-style testing, or finding edge cases automatically.
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 this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.
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。
Weekdays, browser only when the property has no publishing route but a screen. Takes the oldest ready draft, resolves its property, and publishes it by that property's own route: a post file plus a registry entry plus a sitemap update plus a build plus a push, or a publishing surface driven through the flow file it owns. It verifies the live URL by loading it, records the published line, and leaves the draft ready on any failure so tomorrow retries it. It publishes one article to one named property and does nothing else outward, ever.
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。
Assemble 2-3 complementary experts to collaboratively analyze anything. Experts work together to explore topics from multiple expert angles.
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。
Automatically derives TLA+ properties (invariants, safety, liveness) from natural-language requirements or structured requirement documents. Resolves ambiguities, asks clarifying questions for underspecified requirements, and outputs TLA+-compatible property definitions with semantic explanations. Use when translating system requirements, specifications, or behavioral constraints into formal TLA+ temporal logic properties for verification with TLC model checker.
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。
Automatically identify metamorphic properties (symmetry, linearity, additivity, input invariances) from programs or functions. Use when generating metamorphic tests, discovering program properties, validating transformations, or creating test oracles without explicit specifications. Analyzes control flow, data flow, and sample executions to output structured properties for metamorphic test generation and verification.
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。
Launches 5 parallel AI agents to produce a comprehensive property analysis with composite Property Score (0-100), investment grade, and actionable recommendations
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。
@arm-cortex-expert workflow skill. Use this skill when the user needs Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Antigravity UI & Motion Design Expert workflow skill. Use this skill when the user needs Core UI/UX engineering skill for building highly interactive, spatial, weightless, and glassmorphism-based web interfaces using GSAP and 3D CSS and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Convenes expert panels for problem-solving. Use when user mentions panel, experts, multiple perspectives, MECE, DMAIC, RAPID, Six Sigma, root cause analysis, strategic decisions, process improvement, or asks for philosophers/ancients (Socratic, Aristotelian, Stoic method experts).
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。
Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).
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。
The domain expert's product manager in a project repository run by the Boring Factory. Use when the expert brings a need or an idea, wants to clarify, change or resume a feature ("reprends la fiche"), asks what is happening with a feature, has a builder's question to answer, or has a preview to try and accept. Interviews one question at a time, writes the contract and mockups on the contract pull request, and submits the expert's approval only after the expert confirms. Never writes code.
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。
Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.
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。