2,916 件 ・ 関連度順
概要と使いどころ
Inspect failing GitHub Actions checks on PRs, fetch detailed logs, summarize failures, create a fix plan, and implement fixes after user approval. For external CI systems, report details only.
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 deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when deciding whether to maintain or sunset existing code.
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。
Language models exhibit predictable degradation patterns as context length increases. Understanding these patterns is essential for diagnosing failures and designing resilient systems.
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 and evolve design systems with design tokens, component architecture, accessibility guidelines, and documentation templates. Ensures consistent, scalable, and accessible UI across products.
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。
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory
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。
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.
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 designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
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 agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom 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。
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。
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
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。
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
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 Angular CLI and development tools effectively in Angular v20+ projects. Use for project setup, code generation, building, testing, and configuration. Triggers on creating new projects, generating components/services/modules, configuring builds, running tests, or optimizing production builds. Don't use for Nx workspace commands, custom Webpack configurations, or non-Angular CLI build systems like Vite standalone or esbuild direct usage.
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。
Continuous learning framework — learn from work, organize knowledge, build feedback loops. Use when: recording lessons, organizing knowledge, or setting up learning systems that persist across sessions.
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。
Master dark mode UI design with atmospheric theming, WCAG accessibility, and cross-platform best practices. Specializes in weather/sky/ocean-inspired color systems that adapt to time of day and environmental conditions. Use when the user explicitly needs dark mode design expert help. NOT for unrelated tasks.
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 proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
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 when designing autonomous agent systems or deciding agent freedom/safety/context management/collaboration infrastructure. 10 design principles distilled from Karpathy autoresearch (autonomous ML experiment loop) + AgentHub (agent-native Git DAG). Triggered by "autoresearch", "agenthub", "ratchet" ("래칫"), "context budget", "dumb platform", "program.md", "overnight", "autonomous experiment" ("자율 실험"), "branchless", "single-file constraint" ("단일 파일 제약"), "fixed time budget" ("고정 시간 예산") keywords.
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。
Puts the agent into a cautious production mode for live systems: read-only inspection may proceed, but credentials and out-of-scope personal data need permission first, every state-changing action needs explicit per-action approval with its resource risks and rollback stated, and anything unexpected halts work until the user decides. Use when the user invokes `/prod` or `$prod`, or says they are working on production, a live or customer-facing server, a mail server, or a production database, or asks for care on a system where mistakes affect real users.
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。
Drafts a Bring Your Own Device (BYOD) policy for U.S. employers governing personal device access to company systems. Covers MDM enrollment, encryption, remote wipe authority, privacy expectations, data classification, and regulatory overlays (HIPAA, GLBA, SOX, GDPR). Use when creating or updating BYOD policies, mobile device security policies, or personal device programs.
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 deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.
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。
Implementing the `Idempotency-Key` HTTP header pattern (Stripe-pioneered, IETF-standardizing) so retries don't double-charge or duplicate side effects — full Postgres schema with recovery-point state machine, fingerprint check, in-progress lock with 409 response, and 24h retention. Grounded in Stripe blog/docs, the IETF httpapi WG draft, and Brandur Leach's canonical 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。
IBM's autonomic computing vision for self-managing systems with self-configuration, healing, optimization, and protection
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。
Heuristic search-based temporal planning and scheduling for complex multi-constraint environments
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。
Applies insights from MetaGPT paper on how structured communication, role specialization, and SOPs prevent coordination failures in multi-agent LLM systems
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 web images for performance with Next.js Image, responsive srcset, AVIF/WebP, lazy loading, and blur placeholders. Activate on: image optimization, LCP improvement, responsive images, AVIF, blur placeholder, next/image. NOT for: image generation/editing (use image-gen skills), SVG icon systems (use design-system-creator).
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。