1.2万 件 ・ 関連度順
概要と使いどころ
Design and evaluate context compression strategies for long-running agent sessions. Use when agents exhaust memory, need to summarize conversation history, or when optimizing tokens-per-task rather than tokens-per-request.
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 and build LLM-powered projects from ideation through deployment. Use when starting new agent projects, choosing between LLM and traditional approaches, or structuring batch processing pipelines.
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。
Facilitate design discussion — research context, clarify requirements, draft RFC/ADR
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。
Write effective Architecture Decision Records. Use when: (1) Creating a new ADR, (2) Recording a design decision, (3) User mentions ADR, decision, trade-off, or alternatives
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。
Expert SurrealDB 3 skill. Use when working with SurrealDB, SurrealQL queries, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, graph traversal, vector search, security and permissions, deployment and operations, performance tuning, SDK integration, Surrealism WASM extensions, SurrealMCP, Surrealist IDE, Surreal-Sync migrations, SurrealFS, SurrealKit, or ecosystem integrations such as n8n and CodeMirror.
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。
Expert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.
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。
Before committing any non-trivial change, dispatch the diff to a reviewer runtime via ATO (`ato dispatch <reviewer> --session <id>`), parse the numbered/severity-tagged findings, apply or defer each one with a recorded justification, then commit. Fights the "build passes therefore ship it" failure mode — what Garry Tan calls the AI agent complexity ratchet. Place in the v2.16 stack: this skill is the LAST gate. `ato-warroom` decides the design; `ato-mission` runs the multi-step work and produces the diff; `ato-review` checks the diff before commit. When the review is part of a Mission, dispatch the review with `--require-tools read_file,grep,git_diff,git_log` so the reviewer can walk the source itself instead of reasoning from a paraphrase (PR-1.5 tool surface). Receipts land in `execution_logs` and the Mission narrative. Fires automatically before commits touching public surface (CLI subcommands, Tauri commands, MCP tools, schema migrations, security boundaries) or whenever a diff exceeds ~50 LOC of behavior change.
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。
Verify a PCBA BOM and proposed components before circuit or placement freeze, including exact manufacturer part numbers, pin semantics, packages, footprints, CAD and 3D models, lifecycle, live stock, MOQ, assembly class, price, and approved substitutes. Use for sourcing locks, unavailable or expensive parts, JLCPCB/LCSC matching, BOM risk, or cost-aware substitution decisions.
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Audit, finish, or port PCB placement and routing using circuit intent, mechanical constraints, sourcing evidence, native DRC/connectivity, rendered views, and manufacturing feedback. Use before routing, after autorouting, before fabrication release, when adapting a proven board to another connector, enclosure, platform, or regional variant, or when reviewing layers, GND references, power distribution, decoupling, buses, controlled interfaces, RF keepouts, vias, cost-driving features, opens, or DRC findings.
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。
Reorganize label-heavy or netlist-like electronic schematics into human-readable functional diagrams with visible local wiring while preserving proven connectivity. Use for schematic readability correction, overlapping wires or symbols, visually floating connectors or support logic, functional-sheet restructuring, bus presentation, layout annotations, or multi-EDA handoff across KiCad, Altium, EasyEDA, EAGLE/Fusion, gEDA/Lepton, SPICE/netlist, and PDF/image sources.
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。
Operate and review a JLCPCB PCB/PCBA quote, optional physical-stencil request, or order using a frozen Gerber/drill, BOM, CPL, sourcing lock, constraints, and release manifest. Use for browser upload, board or stencil options, cost anomalies, coupons, component matching, substitutions, imported placement calibration, top/bottom preview inspection, cart preparation, or order records without silently accepting placement errors or payment.
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。
Coordinate an evidence-gated electronic product and PCBA workflow from an idea, circuit description, schematic, netlist, PCB, BOM, or fabrication package through a reviewed order. Use for end-to-end requests, multi-stage hardware projects, workflow recovery, gate tracking, change invalidation, or deciding which specialist skill is required next.
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 throwaway HTML mockups so the user can compare design variants.
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。
Turn a monitoring wish in plain language into a deterministic watcher rule 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。
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Use when: (1) creating a new SKILL.md, (2) initializing a skill directory, (3) packaging a .skill file, (4) improving or iterating on an existing skill, (5) understanding skill best practices and design patterns.
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 composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API 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。
Director-level AI art prompt creation for Eastern beauty series, including Eastern fantasy / gu feng Chinese beauty character art, classical Eastern beauty, realistic and modern Eastern beauty, Cosplay展会摄影 / official convention cosplay photography with AAA game character campaign and Vogue editorial quality, 古风闺蜜 / AncientFemaleCompanionship two-person ancient Chinese female companionship photography, 东方美学图鉴 / 小红书图鉴 cover and content systems for 四大美人、四大才女、十二花神、东方神女、敦煌飞天、朝代服饰、东方器物、东方神话, 甜系纯欲生活写真 / SweetHomeGirl lifestyle portraits built around realism, feminine charm, romantic feeling, story moment, and natural attraction, new-Chinese-style fashion editorials, magazine portraits, worldbuilding, mythic role design, costume/styling systems, cinematic composition, lighting, negative prompts, and model-specific prompt variants. Use when the user asks for 东方美人, 东方审美, 东方美学图鉴, 小红书图鉴, 图鉴封面, 图鉴正文, 四大美人, 四大才女, 十二花神, 东方神女, 敦煌飞天, 朝代服饰, 东方器物, 东方神话, Cosplay展会摄影, coser, Coser, 漫展摄影, 展会摄影, ChinaJoy, Bilibili World, Tokyo Game Show, 游戏展台, 官方展会摄影, AAA游戏角色Cosplay, 古风闺蜜, AncientFemaleCompanionship, Ancient Female Companionship, 闺阁夜话, 姐妹古风写真, 古代闺阁生活, 灯下梳发, 共读, 夏夜纳凉, 守夜, 江南同行, 湖亭共伞, 山寺同行, 灯会夜游, 柔美, 妩媚, 成熟, 优雅, 高贵, 清冷, 温婉, 英气, 神性, 慵懒, 东方幻想, 古风美女, 古典东方美人, 宋韵, 唐宫, 江南, 洛神, 青瓷, 昆曲, 国风人物, 汉服女子, 写实东方美人, 现代东方审美, 新中式, 东方高级感, SweetHomeGirl, 甜系纯欲生活写真, 甜系纯欲, 甜美女友感, 真实女生, 自然抓拍, 故事感, 女友视角, lifestyle girlfriend portrait, 仙侠/武侠/宫廷/江南/敦煌风 AI 绘画提示词, image prompt optimization, style word expansion, character series concepts, or prompt packs for Midjourney, Stable Diffusion, ComfyUI, DALL-E, Gemini, Seedream, or other image generation tools.
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。
Coordinate the custom Elementor Widget site workflow for kickoff, lightweight company intake, conditional reference research, new-versus-existing site style routing, uncertain next steps, project resumption, or continuous work across content, Design Systems, UI, HTML, and Widget implementation. Do not use for release, old-Widget troubleshooting, theme development, or remote WordPress 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。
Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.
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Design or improve the in-product NowStack Mobile onboarding and activation journey through focused discovery, implementation planning, and real mobile verification. Distinct from first-clone `ns-onboard`.
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Refresh the NowStack Mobile public landing page style. Use for `/ns web landing-page`, landing redesigns, CapWords/Screen Studio inspired references, and web-app/app/components/landing edits.
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。
Integrate or audit privacy-safe PostHog analytics across NowStack Mobile Expo and TanStack web surfaces. Use for event design, consent, identity, dev/prod separation, funnels, feature flags, or analytics verification.
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End-to-end social content campaign pipeline. Takes a marketing goal and produces scheduled, on-brand social posts with visuals across postiz-supported platforms, while keeping Typefully for X/thread specialist flows and fallback coverage. Chains: CMO strategy, content writing with voice calibration, AI slop audit, selective Paper MCP visual design, and routed scheduling via postiz or Typefully. Each phase has a human gate. Use this skill whenever someone wants to plan and execute a batch of social posts — not just one-off tweets. Triggers include: "social campaign", "schedule posts", "pre-launch content", "content calendar", "social content pipeline", "build up content", "I need social media content for my launch", "help me build social presence", "plan a week of posts", "batch social content", "social media strategy", or any request involving multiple scheduled posts with optional visuals.
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 viral referral programs with incentive structures, sharing mechanics, and tracking. Covers one-sided, two-sided, tiered, and milestone models with viral coefficient calculations. Use when someone wants word-of-mouth growth, viral loops, invite programs, or says 'referral', 'viral loop', 'word of mouth', 'invite program', 'refer a friend', 'growth loop', 'referral incentive', 'affiliate program', 'ambassador program', 'get users to invite friends', or 'organic growth'. Even casual mentions like 'how do I get my users to spread the word' or 'can users invite others' should trigger this. Includes anti-fraud measures and copy templates for both referrer and referee. Use this whenever user-driven acquisition or viral mechanics are discussed.
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。