1.6万 件 ・ 関連度順
概要と使いどころ
Establish hierarchy through size, weight, colour, spacing, and position so the eye lands in the intended order. Use when composing new work. For judging an existing screen, use `critique-visual-hierarchy` (visual-critique).
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 content structure, hierarchy, labelling, and the navigation model. Use when organising what exists. For the UI that exposes it use `navigation-patterns` (interaction-design); for user-generated grouping evidence use `card-sort-analysis` (design-research).
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。
科研分析策略规划助手。根据用户的科研分析需求，通过调研顶尖期刊/会议论文的分析方法，制定个性化、可落地的最优分析策略。适用于需要制定实验设计、数据分析流程、技术路线的场景；兼容旧名 make-research-plan 的 prompt 触发。
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。
当用户明确要求"从文件/图片/网页/描述中提取综述主题"、"生成主题+关键词+核心问题结构化输出"，或要求使用旧名 get-review-theme skill 时使用。支持文件（PDF/Word/Markdown/Tex）、文件夹、图片、自然语言描述、网页 URL 等多种输入源，自动识别输入类型并提取内容，生成可直接用于 research-literature-review 及其他文献综述技能的结构化输出。
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。
Queries S3 object metadata, tracks bucket activity, audits object changes, searches annotations, and analyzes storage metrics using S3 Metadata system tables (journal, inventory, annotation) and S3 Storage Lens tables via Athena SQL. Applies when counting objects, finding recent uploads or deletions, identifying who wrote to a prefix, breaking down storage classes, finding objects by tag, searching annotation content, analyzing storage lens metrics, or enabling S3 Metadata tracking. Prefers system tables over raw S3 APIs (list-objects-v2, head-object) at scale. Trigger phrases: bucket activity, object count, who uploaded, track deletions, storage class breakdown, find by tag, search annotations, storage lens metrics, audit bucket 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。
AWS Startups reference content — Activate FAQ, credits guide, programs, partner offers, sample architectures, and hundreds of learn articles spanning generative AI, cloud architecture, cost optimization, security, fundraising, go-to-market, and real-world startup case studies. Use when the user asks factual questions about AWS Activate (eligibility, credits, programs, providers), wants a sample architecture or solution guide, or needs an AWS-curated learn article on a specific startup topic. For copy-paste AI prompts (RAG chatbot, MVP scaffold, security baseline, GPU quota, etc.), see the prompt-library-for-startups skill. Do not use for: account-specific lookups (credits balance, Activate membership status, application status), real-time event listings beyond the events stub, or content not present in the bundled `references/` tree.
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。
Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).
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。
Manages Amazon DocumentDB end-to-end — serverless-on-8.0 cluster setup, TLS/VPC/driver config, flexible-schema and vector-search data modeling, MongoDB compatibility assessment, DMS-based migration, slow-query diagnosis, major version upgrades (4.0->5.0->8.0), Well-Architected reviews (41-check wa_review.py), cost estimation, and security hardening. Retrieve for every DocumentDB question and when the user asks to set up or migrate MongoDB to AWS — DocumentDB is AWS's MongoDB-compatible managed database. Triggers: JSON document store, document database, MongoDB on AWS, Nested fields, Lambda cannot connect, TLS handshake, VPC port 27017, IAM auth, Secrets Manager, encryption at rest, $graphLookup, flexible schema, COLLSCAN, compound index, DMS migration, CDC cutover, $vectorSearch, RAG, Global Clusters, DR replication, cost sizing, audit, health check, production-readiness.
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。
Analyze and design an AssemblyAI architecture spanning pre-recorded jobs, Streaming v3, LLM Gateway, callbacks, queues, retention, and audit evidence. Use when performing system design or review. Trigger with "AssemblyAI architecture" or "design AssemblyAI 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。
Reference architecture for Apple Notes automation systems. Trigger: "apple notes architecture".
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。
Reference architecture for AppFolio property management integration. Trigger: "appfolio architecture".
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。
Choose and implement Claude API architecture patterns for different scales: serverless, microservice, event-driven, and edge deployment. Trigger with phrases like "anthropic architecture", "claude serverless", "claude microservice design", "edge claude deployment".
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。
Diagnose and manage an Algolia-backed search incident with evidence, containment, and reversible recovery. Use when users see failed, stale, slow, or irrelevant search results. Trigger with "Algolia incident", "search outage", or "Algolia degraded".
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 a fast local development loop for Algolia record transforms and search behavior without contaminating shared indices. Use when iterating on search code, fixtures, or client wrappers. Trigger with "Algolia local development", "mock Algolia", or "test search locally".
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。
Org-grade attack-surface mapping: given a company's legal identity, discover its ENTIRE owned internet footprint — corporate family -> owned domains -> owned netblocks/ASN -> live assets — with attribution discipline, not just DNS breadth. The org-first attribution pyramid (legal entity -> LEI/registration -> corporate family -> owned domains -> owned netblocks/ASN -> live assets). Corporate-identity resolution via the GLEIF LEI API (legal name -> LEI, exact-LEI direct-children expansion, downward-only depth-capped BFS, NEVER a name re-resolution), SEC-EDGAR full-text search + Exhibit-21 subsidiary entity names, OpenCorporates entity corroboration, Wikidata SPARQL corporate graph (P856/P355/P749/P1830). Domain attribution via reverse-WHOIS (WhoisXML preview-then-purchase quota guard, SecurityTrails associated-domains — both paid), crt.sh O= certificate-transparency organization pivot (keyless), infrastructure correlation (shared NS/MX/SaaS-TXT, netblock membership, reverse-DNS PTR), and the independent-evidence combiner (1-prod(1-w_i), rule of three, OwnerTier NONE/WEAK/MODERATE/STRONG/CONFIRMED) — discover-only `related:` candidates are NEVER auto-scanned. Netblock/ASN attribution via org-first RIR queries (ARIN Whois-RWS org-handle search, RIPE DB organisation + inverse-org search) that recover 'dark netblocks' with no DNS link to the seed, ASN discovery (RIPEstat + BGPView union), the HYPERSCALER-SCOPE GUARD (never attribute a whole AWS/GCP/Azure/Cloudflare announced range to a tenant — keep only the seed-containing block, tag shared_hosting_cdn), and org-identity-seeded internet-scan-index queries (Shodan/Censys/ZoomEye/FOFA/BinaryEdge org: filters + an always-on keyless crt.sh fallback). Promote-to-scan triage ranks forgotten discover-only netblocks by remote-access exposure (gateway-vendor/KEV/control-plane/datastore port scoring) into an operator queue. Attribution confidence rubric + anti-patterns (namesake grafting via GLEIF name re-resolution, single-signal ownership, hyperscaler over-attribution, privacy-WHOIS pivot poisoning, RIR org-name collisions). Passive/keyless-first OSINT only — every paid-key dependency is enrichment on top of a keyless core, never a hard requirement. Use when mapping an organization's full corporate-family internet footprint, resolving a legal entity to its LEI/subsidiaries, discovering domains/netblocks/ASNs an org owns beyond its one seed domain, auditing M&A/shadow-IT sprawl, or scoping an engagement that starts from a company NAME rather than a domain.
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。
Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in. Use for /architect, 'architect this', 'design this', or non-trivial work where jumping to code would lock in the wrong shape.
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。
SEO best practices for the Grida project across Next.js pages, blog posts, and documentation. Covers Next.js metadata API, Open Graph / Twitter cards, sitemaps, image search optimization, structured data (JSON-LD), Docusaurus frontmatter, and content writing for search. Use when creating or editing public-facing pages under editor/app/(www), writing blog posts, authoring docs, or reviewing SEO-related metadata. Trigger phrases: "SEO", "metadata", "sitemap", "open graph", "og image", "meta tags", "search ranking", "structured data", "JSON-LD".
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。
Role profile — a reviewer who is fact-seeking, bedrock-seeking, honest, and rejecting. Demands that arguments rest on small facts that cannot be wrong (not on stacked assumptions dressed up as foundation), and that deliberately-unclear concepts be quarantined — never leaked into solid layers, never mixed with each other. Catches the failure mode where designs ship after partial grounding: most of the spec researched, the design feels finished, and the unresearched remainder holds the deal-breaker. Invoke with `/pedantic <target>` (e.g. `/pedantic review this design doc`, `/pedantic review the SDK design`, `/pedantic review this PR`). Probes cover logical structure (definitions, assumptions, boundaries, contradictions, unfalsifiability, vague quantifiers, counterexamples), epistemic honesty (researchable vs. discoverable-only-by-doing), bedrock integrity (assumed-bedrock, leaked uncertainty, mixed unclarity), and design pathology (YAGNI, wrong-layer abstraction, unclear responsibility, fragmentation, over-engineering). Use when you want a hard critique of a plan, design doc, technical doc, code change, or PR description — not a copy edit, not a vibe check.
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。
Guided journey from a large aged codebase everyone fears to touch to one that is safe to change, legible, bounded, and resilient - paid down in place without a rewrite. Orchestrates eight skills phase by phase - working-with-legacy-code, refactoring-patterns, clean-code, software-design-philosophy, clean-architecture, pragmatic-programmer, release-it, domain-driven-design - asking the user questions at every decision point and recording results in the project docs/ folder (TESTING.md, TECH-DEBT.md, REMOVE-TECHNICAL-DEBT-PLAN.md) so the journey resumes across sessions. Use when the user wants to tame a legacy codebase, pay down technical debt safely, avoid a big-bang rewrite, or says 'we are afraid to touch this code'. For a fresh vibe-coded prototype, improve-code-quality; for greenfield structure, design-code-architecture; for a product-and-UX pass, improve-app; to optimize a working codebase for speed, architecture-optimization. For one framework in isolation, invoke that 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。
Guided journey from a working-but-untested vibe-coded prototype to a production-ready product with tests, clean structure, a business-rules boundary, and resilience at scale. Orchestrates nine skills phase by phase - working-with-legacy-code, clean-code, refactoring-patterns, software-design-philosophy, clean-architecture, pragmatic-programmer, release-it, system-design, ddia-systems - asking the user questions at every decision point and recording results in the project docs/ folder (TESTING.md, TECH-DEBT.md, IMPROVE-CODE-QUALITY-PLAN.md) so the journey resumes across sessions. Use when the user wants to harden an AI-generated prototype, add tests before refactoring, make code safe to change, or says 'this works on my machine but I am scared to touch it'. For an aged codebase, remove-technical-debt; for greenfield structure, design-code-architecture; for a product and UX pass, improve-app; to optimize speed and structure, architecture-optimization. For one framework in isolation, invoke that 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。
Semantic topic cluster planning and automated execution engine for claude-blog. Performs SERP-based keyword research, groups keywords by search intent and SERP overlap, builds a hub-and-spoke cluster architecture, generates an interactive SVG cluster map, and executes the full cluster by orchestrating blog-write calls with shared cluster context and automatic internal-link injection. Fills the strategy-to-execution gap: blog-strategy plans the blueprint, blog-cluster builds the house. Use when user says "blog cluster", "topic cluster", "content cluster", "cluster plan", "cluster execute", "pillar content", "hub and spoke", "content ecosystem", "cluster map".
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。
SERP-informed outline generation with H2/H3 heading hierarchy, competitive content gap analysis, section-by-section word count targets, chart and image placement markers, optional FAQ question planning, and internal linking zones. Skeleton only: structure, H2/H3 hierarchy, word counts, optional FAQ slots. Use blog-brief instead if you need full competitive analysis, statistics research, and image suggestions. Lighter than a full content brief, generates article skeleton and structure only, ready for /blog write to consume. Use when user says "outline", "blog outline", "content outline", "structure blog", "plan sections", "article skeleton", "heading structure", "SERP analysis", "competitive outline", "plan article".
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。
Credentialed Google API calls for a blog: PageSpeed Insights, CrUX history, Search Console performance and URL Inspection, GA4 organic traffic, Cloud NLP entities, YouTube video search, Google Ads Keyword Planner, and the Indexing API (job and livestream pages only). Uses the user's Google credentials, quotas, and sometimes billing, and asks for consent before the first call. Runs only on an explicit request: /blog google, "run PageSpeed on this blog URL", "pull my Search Console data", "inspect this URL's index status", "set up Google API credentials". Shares config with claude-seo at ~/.config/claude-seo/google-api.json.
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。
Semantic topic cluster planning and automated execution engine for claude-blog. Performs SERP-based keyword research, groups keywords by search intent and SERP overlap, builds a hub-and-spoke cluster architecture, generates an interactive SVG cluster map, and executes the full cluster by orchestrating blog-write calls with shared cluster context and automatic internal-link injection. Fills the strategy-to-execution gap: blog-strategy plans the blueprint, blog-cluster builds the house. Use when user says "blog cluster", "topic cluster", "content cluster", "cluster plan", "cluster execute", "cluster map".
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。