7,086 件 ・ 関連度順
End-to-end form handling with react-hook-form, Zod schemas, validation patterns, error messaging, field arrays, and multi-step wizards. Use for complex forms, validation architecture, autosave, field dependencies. Activate on "form validation", "react-hook-form", "Zod", "form error", "multi-step form", "wizard". NOT for simple HTML forms, backend validation only, or non-React 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。
Git branching strategies, conflict resolution, rebase workflows, monorepo patterns, and advanced operations like cherry-pick, bisect, and reflog recovery. Activate on "git", "branching strategy", "merge conflict", "rebase", "cherry-pick", "monorepo git", "git bisect", "git reflog", "squash commits". NOT for GitHub Actions CI/CD (use github-actions-pipeline-builder), GitHub API/webhooks, or PR review processes (use code-review-checklist).
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。
Saga patterns, compensating actions, and two-phase commit for distributed transactions. Activate on: distributed transaction, saga, compensating action, two-phase commit, eventual consistency, cross-service transaction. NOT for: single-database transactions (use database-connection-pool-manager), event sourcing (use cqrs-event-sourcing-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。
Domain-Driven Design methodology using bounded contexts, ubiquitous language, and strategic/tactical 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。
Cooperative vibe coding across machines — real-time collaborative development with humans and AI agents. Covers current tools (Live Share, Code With Me, Zed, Cursor multiplayer, tmux), git-based async patterns, AI agent coordination, and the aspirational future of multi-human multi-agent development. Activate on: "pair programming", "vibe coding together", "collaborative coding", "mob programming", "remote pairing", "code together", "multiplayer coding", "Live Share", "Code With Me", "ensemble programming", "cooperative development". NOT for: building a real-time collaboration ENGINE (use real-time-collaboration-engine), git workflow mechanics (use git-best-practices), single-user AI coding assistance (use prompt-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。
CQRS pattern, event stores, projections, eventual consistency. Activate on: CQRS, event sourcing, event store, read model, projection, aggregate, domain event, command handler. NOT for: message broker setup (use event-driven-architecture-expert), database optimization (use data-warehouse-optimizer).
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。
Building resilient distributed systems with circuit breakers, retries with full-jitter exponential backoff, retry budgets (per-request 3-attempt + per-client 10% ratio per Google SRE), deadline propagation, and the cascading-failure math (4 layers × 3 retries = 64x amplification). Grounded in Resilience4j, Microsoft Cloud Patterns, AWS Architecture Blog (Marc Brooker), and Google SRE Book.
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。
Redis caching patterns, cache-aside, write-through, TTL strategies, and invalidation. Activate on: caching, Redis, cache invalidation, cache-aside, write-through, TTL, CDN cache, stale-while-revalidate. NOT for: CDN/reverse proxy setup (use api-gateway-reverse-proxy-expert), database query optimization (use data-warehouse-optimizer).
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。
Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.
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。
Interprets GitHub Actions run status and logs, diagnoses CI failure patterns, and suggests targeted fixes. Handles dependency install failures, test timeouts, build OOM, flaky tests, and workflow misconfigurations. Activate on: 'CI failing', 'build broken', 'workflow error', 'GitHub Actions debug', 'flaky CI', 'pipeline timeout', 'CI red'. NOT for: writing new workflows from scratch (use github-actions-pipeline-builder), CI caching strategy (use ci-cache-optimizer), deployment orchestration (use devops-automator).
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。
Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system
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 digital experiences for ADHD brains using neuroscience research and UX principles. Expert in reducing cognitive load, time blindness solutions, dopamine-driven engagement, and compassionate design patterns. Activate on 'ADHD design', 'cognitive load', 'accessibility', 'neurodivergent UX', 'time blindness', 'dopamine-driven', 'executive function'. NOT for general accessibility (WCAG only), neurotypical UX design, or simple UI styling without ADHD context.
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。
Choosing and operating an HTTP API versioning strategy that doesn't break clients — Stripe's date-based pinned versions, the Deprecation/Sunset header pair (RFC 9745 + RFC 8594), URI vs header vs media-type approaches, and the version-transformer pattern. Grounded in Stripe's published architecture and IETF RFCs.
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。
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).
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。
Systematic methodology for eliciting, representing, and operationalizing expert cognitive skills—pattern recognition, situation assessment, mental simulation—from practitioners without requiring research training. Bridges academic rigor and practical usability.
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 API designer for REST, GraphQL, gRPC architectures. Activate on: API design, REST API, GraphQL schema, gRPC service, OpenAPI, Swagger, API versioning, endpoint design, rate limiting, OAuth flow. NOT for: database schema (use data-pipeline-engineer), frontend consumption (use web-design-expert), deployment (use devops-automator).
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。
Safety, privacy, cost management, and frank advice for building always-on AI agents with episodic memory. Covers data hygiene, privacy risk surfaces, runaway cost prevention, scope creep, psychological effects of persistent AI companions, and responsible deployment patterns. This is the skill that tells you what can go wrong and how to prevent it. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).
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 legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.
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。
Decompile a function to C-like pseudocode for human-readable analysis. Use to understand function logic, review control flow, or prepare for code pattern matching.
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。
Find and list functions in a binary by name, address, regex, or byte pattern. Use as the starting point for binary analysis, to locate specific functions, or to enumerate all functions matching criteria.
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。
Harness Engineering 設計模式 — 基於 Claude Code 原始碼逆向分析的 12 條可遷移原則。Use when: 設計 agent 系統架構、實作 tool orchestration、設計 context 管理策略、建構 agent loop。
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 predicting Threads post performance, analyzing post history patterns, estimating engagement ceiling for a draft, or deciding what content type to write next. Works with or without personal data — uses darkseoking benchmark as fallback.
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 安全設計模式 — 基於 Claude Code 七層縱深防禦架構的逆向分析。Use when: 設計工具安全機制、實作命令過濾、建構權限模型、設計 sandbox。
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。
Agent Skill design knowledge base — mechanisms, philosophy, patterns, pitfalls. Use when: designing new skills, reviewing skill quality, or deciding whether something should be a skill.
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。