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When the user wants to submit their product to startup, SaaS, AI, agent, MCP, no-code, or review directories for backlinks, domain rating, and discovery. Also use when the user mentions "directory submissions," "submit to directories," "backlinks from directories," "list my product," "submit to Product Hunt," "BetaList," "TAAFT," "Futurepedia," "G2 listing," "Capterra listing," "AlternativeTo," "SaaSHub," "AI directories," "MCP registry," "agent directory," "dofollow backlinks," "launch directories," or "directory tracker." Use this whenever someone is planning the directory layer of a product launch or an ongoing backlink campaign. For the broader launch moment, see launch. For programmatic SEO pages that should live behind these backlinks, see programmatic-seo. For AI citation optimization, see ai-seo.

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Query Etsy stores with multi-dimensional filters (sales, favorites, reviews, store opening date, country, primary category, Raving/Star ratings). Trigger when user mentions Etsy store, Etsy shop search, Etsy seller, Etsy shop ranking, Etsy weekly sales stores, Etsy stores, or Etsy store query — even if the tool name is not mentioned, as long as the need is to find stores, filter store data, or analyze store performance on Etsy.

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🇺🇸 UGC Matrix Growth Playbook — Scale user-generated content with AI + real creators. Complete SOP for UGC flywheel, creator recruitment, incentive design, multi-platform publishing. Proven at CPM $0.5, $10M ARR in 60 days with 70M impressions. Includes a full anonymized client case study. 🇨🇳 UGC矩阵号增长实操手册 — AI+真人创作者规模化用户生成内容。完整的UGC飞轮SOP、创作者招募、激励机制设计、多平台分发策略。已验证CPM $0.5，60天$1000万ARR，7000万曝光。含完整客户案例拆解（已脱敏）。 🇯🇵 UGCマトリックス成長プレイブック — AI＋リアルクリエイターでUGCをスケール。フライホイールSOP、クリエイター採用、インセンティブ設計、マルチプラットフォーム配信。CPM $0.5、60日で$10M ARR、7000万インプレッション実証済み。匿名化クライアント事例付き。 🇰🇷 UGC 매트릭스 성장 플레이북 — AI + 실제 크리에이터로 UGC 스케일링. 플라이휠 SOP, 크리에이터 모집, 인센티브 설계, 멀티플랫폼 배포. CPM $0.5, 60일 $10M ARR, 7000만 노출 검증 완료. 익명화된 클라이언트 사례 포함. Triggers: "UGC" | "UGC matrix" | "user generated content" | "creator program" | "creator economy" | "AI UGC" | "matrix accounts" | "矩阵号" | "UGC运营" | "创作者招募" | "UGC Program" | "content matrix" | "TikTok UGC" | "Instagram UGC"

🇺🇸 GitHub Star Sustained Growth Playbook — Monthly 300+ star growth SOP. Content rhythm, community ops, event-driven growth, contributor & ambassador systems. Battle-tested: AFFiNE 0→60K+, Client Project A 0→10K stars. 🇨🇳 GitHub Star 持续增长实操手册 — 月增 300+ Star 的完整 SOP。内容节奏、社区运营、活动驱动增长、Contributor 与 Ambassador 体系。实战验证：AFFiNE 0→60K+、Client Project A 0→10K Star。 🇯🇵 GitHub Star 持続成長プレイブック — 月間300+ Star成長SOP。コンテンツリズム、コミュニティ運営、イベント駆動成長、Contributor＆Ambassadorシステム。実戦検証：AFFiNE 0→60K+、Client Project A 0→10K Star。 🇰🇷 GitHub Star 지속 성장 플레이북 — 월 300+ 스타 성장 SOP. 콘텐츠 리듬, 커뮤니티 운영, 이벤트 기반 성장, Contributor & Ambassador 시스템. 실전 검증: AFFiNE 0→60K+, Client Project A 0→10K 스타. Triggers: "GitHub star growth" | "sustained star growth" | "monthly star growth" | "star growth SOP" | "contributor system" | "ambassador program" | "open source community" | "community growth" | "developer community growth" | "star持续增长" | "Star增长" | "开源社区运营" | "贡献者体系"

Historical argumentation, evidence-based writing, and oral history methods. Covers thesis development for historical arguments, evidence selection and deployment, counterargument construction and rebuttal, historical writing conventions, and the methodology of oral history as both source and practice. Use when constructing historical arguments, writing analytical essays, conducting oral history interviews, or assessing the quality of historical writing.

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Use this skill when architecting new Claude Code agents following Anthropic best practices. Guides through YAML frontmatter creation, agent structure definition, role boundaries, and validation. Critical for creating minimal agent files (~150 lines) with proper skill invocations. Examples: <example>Context: User wants to create a new agent for security auditing user: "Create a security-auditor agent" assistant: "I'll use the agent-creator skill to build this agent following Anthropic best practices" <commentary>New agent creation requires the agent-creator skill to ensure proper structure and YAML frontmatter.</commentary></example> <example>Context: Existing agent file is too verbose and needs refactoring user: "The architect agent is 2000 lines, refactor it to minimal structure" assistant: "I'll use the agent-creator skill to refactor this into a minimal ~150 line agent with a separate skill file" <commentary>Agent refactoring requires the agent-creator skill to separate identity from technical procedures.</commentary></example>

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App Store / Google Play のメタデータ最適化（ASO）を、4つの専門エージェントチームで実行するスキル。 キーワード調査、宣伝テキスト作成、競合分析、多言語ローカライゼーションの4観点から App Store 向けテキスト一式（タイトル、サブタイトル、キーワード、説明文、宣伝テキスト、What's New）を 生成し、最終レポート(MD)を出力する。多言語対応（日本語・英語+他言語）。 Use when: App Store の説明文を書きたい、ASO対策したい、キーワード選定したい、 宣伝文章を作りたい、App Store テキストを多言語化したい、What's New を書きたい。 Triggers: "ASO", "App Store", "宣伝文", "説明文", "キーワード", "サブタイトル", "What's New", "promotional text", "app description", "keyword optimization", "ストア掲載", "メタデータ", "アプリ紹介文", "Google Play", "多言語ASO"

Work with the @upstash/redis TypeScript/JavaScript SDK, a serverless HTTP-based Redis client for Next.js, Vercel, Cloudflare Workers, edge runtimes, and Node.js. Use when adding a cache (cache-aside, write-through, TTL and expiration strategies), session storage and user sessions, a key-value store, leaderboards and rankings with sorted sets, counters, distributed locks, queues with lists, streams and consumer groups, sparse index-addressed arrays and ring buffers (ARSET, ARINSERT, ARRING, ARGREP, AROP), embeddings and nearest-neighbour vector search stored inside Redis (VECTOR commands via redis.vector, separate from @upstash/vector), JSON documents, pipelines and MULTI/EXEC transactions, Lua scripting, read replicas, or full-text search, typo-tolerant search, facets, aggregations, and search over Redis stream entries with Upstash Redis Search (different from regular FT.SEARCH; also available for TCP clients via @upstash/search-redis and @upstash/search-ioredis). Also use when migrating from ioredis or node-redis, when a Redis connection is needed from a serverless function without connection pooling, when integrating @upstash/ratelimit, or when the user says Redis cache, KV store, session store, serverless Redis, or Upstash Redis. Also use when building AI agents on Redis with Upstash AgentKit (agent memory, chat history, RAG tools, tool caching, chat persistence, resumable streams, distributed locks) for the Vercel AI SDK, TanStack AI, or Vercel Eve. Supports automatic serialization/deserialization of JavaScript types.

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Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure.

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Use this skill when authoring INVEST-compliant Salesforce user stories from gathered requirements: shaping the As-A/I-Want/So-That stem, writing Given-When-Then acceptance criteria, sizing complexity (S/M/L/XL), splitting stories that are too large for a sprint, and emitting handoff metadata that names the next downstream agent. Trigger keywords: user story, INVEST, story splitting, story sizing, As-A I-Want So-That, story handoff, recommended_agents, backlog item shape. NOT for requirements elicitation or stakeholder interviews (use admin/requirements-gathering-for-sf). NOT for the Given-When-Then technique itself in depth (use admin/acceptance-criteria-given-when-then). NOT for UAT test case design (use admin/uat-test-case-design). NOT for backlog prioritization (use admin/moscow-prioritization-for-sf-backlog).

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Use this skill to configure Field Service Lightning service territories — including territory types, operating hours, time slots, territory hierarchy, member types, and polygon boundaries. NOT for Enterprise Territory Management (Territory2) in Sales Cloud — use admin/enterprise-territory-management. NOT for bulk-loading territory polygons or members — use data/fsl-territory-data-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。

When the user wants to submit their product to startup, SaaS, AI, agent, MCP, no-code, or review directories for backlinks, domain rating, and discovery. Also use when the user mentions "directory submissions," "submit to directories," "backlinks from directories," "list my product," "submit to Product Hunt," "BetaList," "TAAFT," "Futurepedia," "G2 listing," "Capterra listing," "AlternativeTo," "SaaSHub," "AI directories," "MCP registry," "agent directory," "dofollow backlinks," "launch directories," or "directory tracker." Use this whenever someone is planning the directory layer of a product launch or an ongoing backlink campaign. For the broader launch moment, see launch. For programmatic SEO pages that should live behind these backlinks, see programmatic-seo. For AI citation optimization, see ai-seo.

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。

KiCad 10.0 (オープンソース EDA スイート) リファレンス。 Project Manager、Schematic Editor (Eeschema)、PCB Editor (PCBnew)、 Gerber Viewer (GerbView)、Drawing Sheet Editor (pl_editor)、 PCB Calculator、Command-Line Interface (kicad-cli)。 回路図設計、基板設計、ガーバー出力、フットプリント、シンボル、3D ビューア。

投資家向けの説明資料や財務モデル、資金使途表、応募書類を作成・更新し、事業実績や収益の前提、調達額、チーム情報の食い違いを複数の資料で確認するスキル。

• 投資家向け説明資料を作成・改訂したいとき
• 投資家向け概要資料やメモを作りたいとき
• 収益予測と資金使途表を作りたいとき

資金調達に向けた投資家への初回メール、紹介依頼、面談後の連絡を作成するスキル。相手の投資方針や具体的な実績を踏まえ、簡潔で次の行動が明確な文面に整えます。

• 投資家への初回メールを作りたいとき
• 転送用の説明を添えた紹介依頼
• 返信がない場合の連絡計画

Measure Storybook startup time from spawning `storybook dev` until the first story renders in the browser. Use when the user asks about Storybook boot time, server-ready timing, first story render timing, startup regressions, benchmarking with repeat runs, or comparing Storybook versions or feature flags.

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When the user wants to run influencer, creator, or ambassador partnerships to promote their product — finding and vetting partners, structuring deals, briefing creators, disclosure compliance, and measuring ROI. Also use when the user mentions 'influencer marketing,' 'creator partnerships,' 'sponsorships,' 'YouTube sponsorships,' 'podcast sponsorships,' 'brand ambassador,' 'ambassador program,' 'creator program,' 'UGC creators,' 'tech UGC,' 'UGC creator program,' 'creator network,' 'B2B influencers,' 'thought leader ads,' 'gifting,' 'product seeding,' 'whitelisting creator content,' 'how much to pay an influencer,' or 'FTC disclosure.' For affiliate/referral payout mechanics, see referrals. For community-led advocacy, see community-marketing. For turning creator content into paid ads, see ad-creative.

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。

Competitive analysis two ways - the in-deal play against a named competitor, and win/loss patterns, competitor mentions, and battlecards across the book. Use when the user asks "competitive intel", "research competitors", "how do we compare to [competitor]", "what's new with [competitor]", "battlecard for [competitor]", "how do we beat [competitor]", "what's the play against [competitor] in [deal]", "where are we losing to [competitor]", "win/loss patterns", "competitive analysis", "refresh the battlecards", or on a weekly schedule.

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Mounts Cloud Storage buckets on Google Cloud as a POSIX file system using Cloud Storage FUSE (and the gcsfuse CLI tool). Use when interacting with the gcsfuse CLI or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags (--implicit-dirs) or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre; use gke-storage).

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Diagnoses GKE persistent-storage failures — volume attach/mount errors (Regional PD on optimized VMs, fsGroup mount timeouts), disk-performance and node storage-pressure issues, slow-disk Pod-creation failures, volume-expansion problems, Local SSD / Hyperdisk Storage Pool creation errors, and Cloud Storage FUSE OOM. Use when Pods are stuck in ContainerCreating, volumes fail to attach or mount, or nodes report storage pressure. Don't use for routine storage provisioning or StorageClass/PVC authoring (see the gke-storage skill).

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Manages GKE storage, including PVCs, PersistentVolumes, and Filestore. Use when configuring GKE storage or creating PVCs. For GCS FUSE mounts, use google-cloud-storage-fuse. For diagnosing storage failures (volume attach/mount errors, disk performance/node storage pressure, or Cloud Storage FUSE OOM), use gke-storage-troubleshooting. Don't use for database administration or replication strategies outside volume provisioning context.

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Scaffolding that helps a writer build a story they already want to tell — fills in structure, hidden wound, theme, and prose from the writer's own material across seven narrative layers, deriving the spine from their notes as proposals they ratify. Bans generic AI patterns; scales short story to multi-book series. USE WHEN write story, fiction, novel, chapter, story bible, character arc, plot outline, creative writing, worldbuilding, draft story, help me write my story, develop my novel, layer my story, build out my book, I have notes for a story. NOT FOR narrative summaries of real content.

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Manage Apify datasets, key-value stores, and request queues programmatically, and orchestrate multi-Actor pipelines. Use when you need to read or write Apify datasets, export scraped data to CSV/JSON/XLSX, store config or binary artifacts in a key-value store, manage a resumable request queue, chain Actors into a scrape → transform → export pipeline, or monitor Actor run status and cost. Trigger with "apify dataset", "apify key-value store", "apify storage", "export apify data", "apify pipeline", "apify request queue".

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Run and control a user's app on a remote iOS/Android simulator hosted on EAS cloud. Read before running any `eas simulator:*` commands - it has the current syntax for this experimental API. Use whenever the user needs a simulator they can't run locally - 'run my app on a cloud simulator', 'use eas simulator to run/install/screenshot my app', 'I'm on Linux/Cursor and need an iOS device', 'no sim on this box / headless CI', 'let an agent click through my app and screenshot it', 'test my dev build on a remote sim with live reload', 'stream a sim to my browser' - even when they don't say 'EAS Simulator' or 'cloud'. On a host WITHOUT a local simulator (Linux, CI, cloud sandbox) it's the default; on macOS, do NOT auto-trigger for a plain 'run on the simulator' - use it only for a cloud/remote/shareable sim, an iOS version they lack, or an agent-driven session. NOT for local sims (expo run:ios, Xcode, Android Studio), EAS Build/Update, web preview, or physical devices.

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。