1.1万 件 ・ 関連度順
概要と使いどころ
Expert in building browser extensions that solve real problems - Chrome, Firefox, and cross-browser extensions. Covers extension architecture, manifest v3, content scripts, popup UIs, monetization strategies, and Chrome Web Store publishing. Use when: browser extension, chrome extension, firefox addon, extension, manifest v3.
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 backend AI with Vercel AI SDK v6 stable. Covers Output API (replaces generateObject/streamObject), speech synthesis, transcription, embeddings, MCP tools with security guidance. Includes v4→v5 migration and 15 error solutions with workarounds. Use when: implementing AI SDK v5/v6, migrating versions, troubleshooting AI_APICallError, Workers startup issues, Output API errors, Gemini caching issues, Anthropic tool errors, MCP tools, or stream resumption failures.
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。
Search markdown knowledge bases efficiently using qmd. Use this when searching Obsidian vaults or markdown collections to find relevant content with minimal token 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。
Look up contacts from macOS Contacts.app. Use when resolving phone numbers to names, finding contact info, or searching the address 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。
Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store
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 designing REST or GraphQL APIs, creating OpenAPI specifications, or planning API architecture. Invoke for resource modeling, versioning strategies, pagination patterns, error handling standards.
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。
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
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. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
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。
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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。
Navigate and understand codebases using agentlens hierarchical documentation. Use when exploring new projects, finding modules, locating symbols in large files, finding TODOs/warnings, or understanding code structure.
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。
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_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。
Complete observability & reliability engineering system. Use when designing monitoring, implementing structured logging, setting up distributed tracing, building alerting systems, creating SLO/SLI frameworks, running incident response, conducting post-mortems, or auditing system reliability. Covers all three pillars (logs/metrics/traces), alert design, dashboard architecture, on-call operations, chaos engineering, and cost optimization.
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。
Enterprise-grade code review agent. Reviews PRs, diffs, or code files for security vulnerabilities, performance issues, error handling gaps, architecture smells, and test coverage. Works with any language, any repo, no dependencies required.
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。
Complete engineering management system — team building, 1:1s, performance, hiring, architecture decisions, incident management, and scaling. From IC-to-manager transition through director-level operations.
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。
Complete methodology for building production-grade React applications with architecture decisions, component design, state management, performance optimization, testing, and deployment.
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。
Complete startup operating system — from idea validation to Series A. Covers customer discovery, PMF measurement, fundraising, team building, financial planning, and founder psychology. Use when building, launching, pivoting, or scaling a startup.
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 your AI agent into a business automation architect. Design, document, implement, and monitor automated workflows across sales, ops, finance, HR, and support — no n8n or Zapier required.
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, build, test, document, and secure production-grade APIs. Covers the full lifecycle from schema design through deployment, monitoring, and versioning. Use when designing new APIs, reviewing existing ones, generating OpenAPI specs, building test suites, or debugging production issues.
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 beautiful, hand-drawn Mermaid diagrams with robust syntax (quoted labels, ELK layout). Use this skill when the user asks for "diagram", "flowchart", "sequence diagram", or "visualize this process".
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。
Unified search aggregator for Google, Wikipedia, Reddit, NewsAPI, and RSS feeds with optional Redis caching. Supports parallel querying and structured JSON output.
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, build, deploy, and operate production AI agent systems — single agents, multi-agent teams, and autonomous swarms. Complete methodology from agent architecture through orchestration, memory systems, safety guardrails, and operational excellence.
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。
Control and manage VirtualBox virtual machines directly from openclaw. Start, stop, snapshot, clone, configure and monitor VMs using VBoxManage CLI. Supports full lifecycle management including VM creation, network configuration, shared folders, and performance monitoring.
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 high-quality collaborative worlds in Doppel. Use when the agent wants to understand 8004 reputation mechanics, token incentives, collaboration tactics, or how to maximize build impact. Covers streaks, theme adherence, and the rep-to-token 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。
End-to-end MegaETH development playbook (Feb 2026). Covers wallet operations, token swaps (Kyber Network), eth_sendRawTransactionSync (EIP-7966) for instant receipts, JSON-RPC batching, real-time mini-block subscriptions, storage-aware contract patterns (Solady RedBlackTreeLib), MegaEVM gas model, WebSocket keepalive, bridging from Ethereum, and debugging with mega-evme. Use when building on MegaETH, managing wallets, sending transactions, or deploying contracts.
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。