6,206 件 ・ 関連度順
概要と使いどころ
Plan and diagnose FIPA-style ontology-service interactions while keeping declared ontology relations, provider capability, and application authorization separate. NOT for treating an ontology mapping as provider authority or guaranteed interoperability.
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 production-grade Dockerfiles, docker-compose configurations, multi-stage builds, and container optimization. Activate on "Dockerfile", "docker", "docker-compose", "container", "multi-stage build", "docker image", "container optimization", "docker security". NOT for Kubernetes orchestration, cloud-specific container services (ECS, Cloud Run), or CI/CD pipelines (use github-actions-pipeline-builder).
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 DevOps engineer for CI/CD, IaC, Kubernetes, and deployment automation. Activate on: CI/CD, GitHub Actions, Terraform, Docker, Kubernetes, Helm, ArgoCD, GitOps, deployment pipeline, infrastructure as code, container orchestration. NOT for: application code (use language skills), database schema (use data-pipeline-engineer), API design (use api-architect).
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。
Multi-stage Docker build optimizer for minimal, secure production images. Activate on: Dockerfile optimization, multi-stage build, distroless image, container size reduction, Docker security scanning, BuildKit features. NOT for: container orchestration (use kubernetes-manifest-generator), CI/CD pipelines (use github-actions-pipeline-builder), runtime container config (use environment-config-manager).
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。
Apply recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues. Use for situation assessment, RPD-style action selection, and decision-support design in operational settings. NOT for batch optimization, static planning with full information, or tasks where exhaustive search is cheap and harmless.
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。
Apply recognition-primed decision making to agents operating under time pressure, uncertainty, and ambiguous cues. Use for situation assessment, RPD-style action selection, and decision-support design in operational settings. NOT for batch optimization, static planning with full information, or tasks where exhaustive search is cheap and harmless.
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。
Long-running design anthropologist that builds comprehensive visual databases from 500-1000 real-world examples, extracting color palettes, typography patterns, layout systems, and interaction design across any domain (portfolios, e-commerce, SaaS, adult content, technical showcases). This skill should be used when users need exhaustive design research, pattern recognition across large example sets, or systematic visual analysis for competitive positioning.
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。
Generate, fill, and assemble PDF documents at scale. Handles legal forms, contracts, invoices, certificates. Supports form filling (pdf-lib), template rendering (Puppeteer, LaTeX), digital signatures (DocuSign), and document assembly. Use for legal tech, HR automation, invoice generation. Activate on "PDF generation", "form filling", "document automation", "digital signatures". NOT for simple PDF viewing, basic file conversion, or OCR text extraction.
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。
Synthesizes evidence-linked feedback from validation results, confidence evidence, and iteration triggers. Proposes bounded revision guidance for a stated acceptance failure. Activate on 'synthesize feedback', 'improvement suggestions', 'actionable feedback', 'iteration guidance', 'feedback generation'. NOT for iteration detection (use dag-iteration-detector) or convergence tracking (use dag-convergence-monitor).
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 undirected cycle structure within a valid DAG using the source paper's orientation and contraction method. Use for cycle-basis analysis with explicit graph and reduction choices. NOT for directed feedback loops, workflow deadlocks, or runtime resilience claims.
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。
Validates agent outputs against schemas and quality criteria, scores confidence, detects hallucinations, monitors convergence, decides when to iterate, and synthesizes actionable feedback. Use when checking if a node's output is acceptable, scoring confidence, detecting fabricated content, deciding whether to re-execute, or generating improvement feedback. Activate on "validate output", "check quality", "confidence score", "hallucination check", "should we iterate", "improvement feedback". NOT for executing DAGs (use dag-runtime), planning DAGs (use dag-planner), or matching skills (use dag-skills-matcher).
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。
Matches natural language task descriptions to appropriate skills using semantic similarity. Handles fuzzy matching, intent extraction, and capability alignment. Activate on 'find skill', 'match task', 'semantic search', 'skill lookup', 'what skill for'. NOT for ranking matches (use dag-capability-ranker) or skill catalog (use dag-skill-registry).
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。
Send and receive transactional emails with Cloudflare Email Service (Email Sending + Email Routing). Use when building email sending (Workers binding or REST API), email routing, Agents SDK email handling, or integrating email into any app — Workers, Node.js, Python, Go, etc. Also use for email deliverability, SPF/DKIM/DMARC, wrangler email setup, MCP email tools, or when a coding agent needs to send emails. Even for simple requests like "add email to my Worker" — this skill has critical config details.
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。
App Store and Google Play submission automator with Fastlane, screenshot automation, metadata management, and TestFlight/internal testing. Activate on: app store submission, Fastlane, TestFlight, Google Play Console, screenshot automation, metadata management, app review, code signing, provisioning profiles. NOT for: CI/CD pipeline setup (use github-actions-pipeline-builder), app architecture (use react-native-architect), analytics (use mobile-analytics-crash-reporting-expert).
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。
Blue-green and canary deployment orchestrator with traffic shifting and automated rollback. Activate on: blue-green deployment, canary release, rolling deployment, traffic shifting, rollback automation, progressive delivery, Argo Rollouts, Flagger. NOT for: K8s manifest generation (use kubernetes-manifest-generator), CI/CD pipeline setup (use github-actions-pipeline-builder), monitoring (use monitoring-stack-deployer).
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 beautiful, accessible graphical interfaces — web, desktop (Electron/Tauri), and native (iOS/macOS/Android). Use for visual hierarchy and layout, color and theming (light/dark, semantic tokens, WCAG contrast), typography systems, motion and micro-interactions, accessibility, component systems and design tokens, responsive/adaptive layout, and platform-native idioms. The GUI counterpart to beautiful-cli-design. NOT for terminal/CLI output (use beautiful-cli-design) or API/data schemas.
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 decisions for when and what sounds to use in web apps, mobile apps, and desktop native apps — plus how to create them with AI. Covers UI sound vocabulary (confirms, alerts, transitions, errors), platform conventions (iOS, macOS, Material), creating sounds with AI tools (ElevenLabs, AudioCraft, Stable Audio), Web Audio API for procedural sounds, haptic-audio pairing, and the psychology of satisfying vs. annoying UI sounds. Activate on 'app sounds', 'UI sounds', 'notification sound', 'sound design app', 'earcons', 'sonic branding', 'interaction sounds', 'feedback sounds', 'app audio', 'satisfying sounds'. NOT for professional audio engineering (use sound-engineer), Windows 3.1 retro sounds specifically (use win31-audio-design), music composition (use sound-engineer).
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 polished terminal UX for CLIs and TUIs. Use for setup flows, dashboards, tables, and actionable errors. NOT for web UIs, desktop windows, API schemas, or shell automation.
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。
Declare an agentic application's interaction disclosure, state, context, capabilities, and effect-control boundaries. Use for a reviewable design specification and static consistency audit before implementation. NOT for proving that controls are deployed or enforced, exposing private chain-of-thought, selecting a model/router, or designing a multi-agent protocol.
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。
Frame multi-agent designs through explicit environment, autonomy, information, coordination, commitment, incentive, and verification assumptions. NOT for endorsing multi-agent architecture without need or claiming runtime correctness from conceptual methods.
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。
Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use jury_rig-architect), specific tool implementation.
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。
Discipline an AI agent needs to work an issue/task tracker (Jira, Linear, GitHub Issues) as the shared source of truth: pull the right next item instead of inventing work, search before creating to avoid duplicates, write actionable items with checkable acceptance criteria, keep status transitions honest, and link work to items via branches/PRs/commits so progress is legible. Use when an agent picks up tracker work, files a new issue, updates status, or closes an item. NOT for authoring the PR itself (use agent-pr-authoring), roadmap-level planning and prioritization (use legible-roadmap-with-sidequests), or the mechanics of multiple agents coordinating on the same codebase (use multi-agent-coordination).
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-level idiomatic Rust API design — the patterns that make illegal states unrepresentable and abstractions zero-cost. Type-state programming (PhantomData state machines), newtype + sealed + extension traits, typestate builders, RAII guards & Drop ordering, the interior-mutability decision tree (Cell/RefCell/Mutex/RwLock/Atomic/OnceLock), enum-driven state machines, error architecture (thiserror vs anyhow), dyn vs generic dispatch & object safety, the impl Trait / GAT / RPITIT toolbox, and Deref-for-smart-pointers (and its abuse). Use when designing a Rust library's public API, encoding invariants in the type system, choosing a dispatch or error strategy, or reviewing Rust for idiom. NOT for borrow-checker firefighting / toolchain / test-runner workflow (use rust-development-workflow), NOT for pd-console GPUI rendering/layout/panes (use gpui-rust-console), NOT for app packaging/notarization (use rust-app-distribution).
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。
Derive a SET of simulation personas for an agent from product artifacts — backend payloads, UI screenshots, journey/product docs, and sample real user messages — instead of designing one persona by hand. Identifies who actually interacts with the agent and how they behave, then creates the personas via the CLI. Best for text/chat agents and for new agents with no interaction history. Use when the user says "make personas from these screenshots/payloads", "who are my users", "create a set of personas", "derive personas from my product", "build a persona library", or "I have backend data, turn it into personas".
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。