1.3万 件 ・ 関連度順
概要と使いどころ
Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.
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。
Help founders and leaders define company values and culture for a minimalist, lean business. Use when setting up company culture, preparing to hire, or codifying what the company stands for.
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 robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint design, and performance optimization. Ensures data integrity, query performance, and maintainable data models.
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。
Map Azure architectures and operations to security and compliance controls. Use this skill when users ask for governance, policy enforcement, audit readiness, or regulatory alignment. Covers ISO, SOC2, HIPAA, PCI-DSS, and custom compliance frameworks.
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 OpenAI's brand colors, typography, and visual identity standards to artifacts. Ensure design consistency with OpenAI branding for presentations, documents, and UI components.
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。
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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。
Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation 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。
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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。
Implement routing in Angular v20+ applications with lazy loading, functional guards, resolvers, and route parameters. Use for navigation setup, protected routes, route-based data loading, and nested routing. Triggers on route configuration, adding authentication guards, implementing lazy loading, or reading route parameters with signals.
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。
Cycle horaire du coordinateur adjoint myia-po-2025:CoursIA-2. Réduit la file, répond aux ASK CoursIA-2, publie des preflights COMMENTED et dispatche les lanes CoursIA-2, sans merge ni arbitrage réservé à ai-01.
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 hand-drawn style diagrams and infographics for recovery education articles. Creates anatomist's notebook aesthetic visuals - brain diagrams, timelines, social comparisons, and process flows using continuous line art, semantic color coding, and margin annotations.
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 human-in-the-loop review points for DAG workflows. Determines what to present to the human, how to collect feedback, and how to route approve/reject/modify decisions back into the DAG. Use when adding approval gates, designing review UX, or handling human feedback in agent workflows. Activate on "human review", "approval gate", "human-in-the-loop", "human gate", "approval workflow", "user review step". NOT for executing human gates at runtime (use dag-runtime with Temporal signals), general UX design, or chatbot conversation design.
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。
Deep analysis of knowledge distillation techniques for compressing large models into smaller efficient ones
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 image generation pipelines with Stable Diffusion, FLUX, ControlNet, LoRA, and ComfyUI workflows. Activate on: image generation pipeline, ComfyUI workflow, ControlNet, LoRA training, diffusion model. NOT for: video generation (ai-video-production-master), image classification (computer-vision-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。
Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-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。
Diagnose and eliminate the GraphQL N+1 resolver explosion using Facebook DataLoader's per-request batching, plus the second-order defenses (persisted queries, depth limits, cost analysis). Use when a GraphQL query that returns N items in a list triggers N+1 database round trips, when designing nested resolvers that load associations, when a list query times out under load, or when you need to budget query cost before resolvers run. NOT for REST-style endpoint design (use api-versioning-strategy), generic database query optimization (use database-query-optimizer), or schema design (use graphql-server-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。
Build and extend pd-console — Port Daddy's GPU-native macOS operator console (GPUI 0.2.x, Zed's Rust UI). Covers the render-agnostic Block/Pane(Surface) contract, the two-thread reqwest↔smol refresh pipeline, Taffy flexbox layout, uniform_list virtual scroll, focus + keyboard nav, the OKLCH theme and ICS maritime flag badges, GPUI's missing text-input, and the real feature-gated cargo/CI gate. Use when adding panes, visual polish, or debugging GPUI rendering/layout/focus in core/pd-console. NOT for the TypeScript daemon, generic Rust toolchain/borrow-checker help (use rust-with-claude-code), or non-pd GPUI apps with a different theme/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。
Build production CI/CD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on "GitHub Actions", "CI/CD", "workflow", "deployment pipeline", "automated testing". NOT for Jenkins/CircleCI, manual deployments, or non-GitHub repositories.
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。
Advanced CV for infrastructure inspection including forest fire detection, wildfire precondition assessment, roof inspection, hail damage analysis, thermal imaging, and 3D Gaussian Splatting reconstruction. Expert in multi-modal detection, insurance risk modeling, and reinsurance data pipelines. Activate on "fire detection", "wildfire risk", "roof inspection", "hail damage", "thermal analysis", "Gaussian Splatting", "3DGS", "insurance inspection", "defensible space", "property assessment", "catastrophe modeling", "NDVI", "fuel load". NOT for general drone flight control, SLAM, path planning, or sensor fusion (use drone-cv-expert), GPU shader development (use metal-shader-expert), or generic object detection without inspection context (use clip-aware-embeddings).
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 face recognition systems with InsightFace, ArcFace, enrollment pipelines, HDBSCAN clustering, and privacy-compliant architecture. Activate on: face recognition, face enrollment, face clustering, identity verification, facial search. NOT for: general object detection (computer-vision-pipeline), emotion analysis (ai-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。
Draft professional emails for various contexts including business, technical, and customer communication. Use when the user needs help writing emails or composing professional messages.
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 introducing a feature flag system, choosing between release flags / experiment flags / operational flags / kill switches / permission flags, designing percentage rollouts, building kill-switch + circuit-breaker patterns, structuring evaluation contexts (user IDs, plan tier, region), or building flag-cleanup discipline. Triggers: LaunchDarkly / GrowthBook / Unleash / Flagsmith / OpenFeature, percentage ramp, sticky bucketing, kill switch wired to alerting, "we still have flags from 2 years ago", flag debt, OpenFeature provider, evaluation context, default value on provider failure. NOT for A/B test statistical analysis (separate skill), traffic-split routing at the edge, build-time bundler defines, or environment-based config.
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 in Git workflows, conventions, and patterns for modern development teams and AI agents. Activate on: branching strategy, commit conventions, git hooks, pre-commit, husky, lint-staged, trunk-based development, git worktree, monorepo git, PR review workflow, conventional commits, git workflow. NOT for: CI/CD pipelines (use github-actions-pipeline-builder), repository hosting (use devops-automator), multi-agent orchestration (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。
Metal/wgpu/WGSL shader surfaces for native Rust gpui apps (Zed-family, pd-console), stockpiled with beautiful copy-pasteable shader-toy examples. Use for custom GPU fragment passes behind/around gpui panes: ocean/water shaders, pixelated waves and boats, a living harbor, dithered chrome borders, sonar sweeps, aurora/starfields, CRT/scanline post. Trigger on: wgsl, wgpu, metal shader, gpui shader, shadertoy, fragment shader, SDF, noise/fbm, ordered dithering, pixelation, render-to-texture, "pixelated waves and boats", living harbor water. NOT for: web/GLSL/three.js shaders (use a web tool), non-shader gpui motion (use rust-gpui-motion), general GUI layout/color (use beautiful-gui-design), CLI/TUI (use beautiful-cli-design).
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。