3.3万 件 ・ 関連度順
Diagnoses LLM output failures including hallucinations, constraint violations, format errors, and reasoning issues. Provides root cause classification, prompt fixes, tool improvements, and new test cases. Use for "debugging AI", "fixing prompts", "quality issues", or "output errors".
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。
Converts Figma/design specifications into production-ready UI components with accurate spacing, typography, color tokens, responsive rules, and interaction states (hover, focus, disabled, active). Generates Tailwind/shadcn code with design system tokens mapping. Use when translating "Figma to code", "design specs to components", or "implement design system".
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。
Token architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing/typography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.
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。
Generates comprehensive documentation templates for open-source and internal projects including README, CONTRIBUTING, SECURITY, CODE_OF_CONDUCT, LICENSE, and other standard docs with suggested sections and best practices. Use when users request "create project docs", "add OSS documentation", "setup standard docs", or "make it open-source ready".
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。
Optimizes Dockerfiles for smaller images, faster builds, better caching, and security hardening using multi-stage builds and best practices. Use when users request "optimize Dockerfile", "reduce Docker image size", "Docker best practices", or "containerize application".
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 safe refactor plans for messy UI code including component splitting strategies, state simplification, performance optimizations, and accessibility improvements. Provides phased approach, risk assessment, and "done" criteria. Use when refactoring "legacy code", "messy components", "performance issues", or "large files".
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。
Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing "training data", "vector datasets", "document processing", or "embedding data".
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。
Designs GraphQL schemas with types, queries, mutations, subscriptions, resolvers, and DataLoader patterns for efficient data fetching. Use when users request "GraphQL API", "schema design", "GraphQL setup", "resolvers", or "Apollo Server".
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。
Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.
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 original v1 taste-skill, preserved for projects depending on its exact behavior. The current default is `design-taste-frontend` (v2 experimental), which is a substantial rewrite. Use this v1 install name only if you need exact backward compatibility.
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。
Diagnoses and fixes flaky tests by identifying root causes (timing issues, shared state, randomness, network dependencies) and provides stabilization strategies. Use for "flaky tests", "test stability", "intermittent failures", or "test debugging".
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 repeatable evaluation systems with golden datasets, scoring rubrics, pass/fail thresholds, and regression reports. Use for "LLM evaluation", "testing AI systems", "quality assurance", or "model benchmarking".
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。
Elite UX/UI & Advanced GSAP Motion Engineer. Enforces Python-driven true randomization for layout variance, strict AIDA page structure, wide editorial typography (bans 6-line wraps), gapless bento grids, strict GSAP ScrollTriggers (pinning, stacking, scrubbing), inline micro-images, and massive section spacing.
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 secure authentication patterns including login/registration, session management, JWT tokens, password hashing, cookie settings, and CSRF protection. Provides auth routes, middleware, security configurations, and threat model documentation. Use when building "authentication", "login system", "JWT auth", or "session management".
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。
Plans safe API evolution with versioning strategies, client migration guides, deprecation timelines, and backward compatibility considerations. Use for "API versioning", "deprecation planning", "API evolution", or "breaking changes".
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。
Configures Cross-Origin Resource Sharing with proper headers, preflight handling, and security best practices. Use when users request "CORS setup", "cross-origin requests", "API CORS", "preflight requests", or "CORS headers".
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 API contract testing to ensure provider-consumer compatibility using Pact or similar tools. Prevents breaking changes with contract specifications and bi-directional verification. Use for "contract testing", "API contracts", "Pact", or "consumer-driven contracts".
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。
Generates semantic commit messages following the Conventional Commits specification with proper types, scopes, breaking changes, and footers. Use when users request "write commit message", "conventional commit", "semantic commit", or "format commit".
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。
Generates changelogs and release notes from git commits, PR titles, and issue references. Organizes changes by impact type (breaking, features, fixes, improvements), formats according to Keep a Changelog standard, and provides version tagging and semantic versioning suggestions. Use when users request "create changelog", "write release notes", "document version changes", or "prepare release".
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 working Spark implementation should be simplified without changing its observed behavior, ownership, compatibility, or public contract.
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 before a tag, GitHub release, or Zenodo archive, when README, CATALOG, CHANGELOG, CITATION.cff, the disclosure aggregate, and skill discovery must agree, or when a go or no-go release verdict needs recorded evidence behind it.
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 paired tr.md and en.md files need a parity verdict, when titles, frontmatter, headings, or DOI sets may have drifted between languages, or when a bilingual document is approaching release and its translation coverage needs an audit.
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 authoring a new manuscript, thesis chapter, or abstract from scratch in Turkish and English in parallel — building the claim skeleton first, then drafting Turkish and re-authoring English — not for auditing parity between existing versions.
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 selecting a statistical test with AI consultation, when assumptions need checking against the actual data, when an analysis plan or decision log must be written before running anything, or when results need APA-style reporting with effect sizes.
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。