961 件 ・ 関連度順
概要と使いどころ
Reading assistant for saving, organizing, and recalling web content. Use when the user says "save this article", "read this link", "summarize this", "check my feeds", "add to my library", or asks about previously saved content. Handles articles, tweets, YouTube videos, GitHub repos, PDFs, and RSS feeds. Articles are stored with their metadata and indexed for fast search.
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。
Connect agents and IDE assistants to Shopify docs, API schemas, code validation, and store-management workflows through Shopify's official toolkit.
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。
The official Brave Search MCP server integrates the Brave Search API with AI assistants, providing comprehensive web search, local business search, image search, video search, news search, and AI-powered summarization capabilities through the Model Context 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。
This skill uses OpenClaw's healthcheck workflow to inspect the host running the assistant, surface risky exposure, and turn the findings into a staged hardening plan. It is for operator-style audits with explicit approval gates, not a generic software listing or a replacement for OS administration.
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。
mcp-atlassian is a Model Context Protocol server that connects AI assistants to Atlassian Jira and Confluence. It enables searching and managing Jira issues, reading and editing Confluence pages, and performing project management tasks through natural language via any MCP-compatible client.
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。
AppFlowy brings documents, projects, wikis, and AI-assisted collaboration into a self-hosted or desktop-friendly workspace. This skill helps agents work from the real AppFlowy project, docs, and deployment methods when users need an open source Notion-style environment with local control.
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。
AIChat is a comprehensive LLM command-line tool written in Rust that combines chat-REPL, shell command generation, RAG, AI tools, and multi-provider support into a single binary. It connects to 20+ LLM providers including OpenAI, Claude, Gemini, and Ollama.
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 routes requests to the correct skill (Planner, Debugger, Advisor, etc.) based on first message content.
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 the user says 'set up respira', 'respira not connecting', 'check my respira connection', or 'fix respira'. Walks through first-time setup and diagnoses connection failures: timeouts, 401s, 403s, and plugin or server version mismatches.
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-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis. Implements Cursor Debug Mode methodology — structured hypothesis ranking, targeted code instrumentation, human-in-the-loop reproduction gate, log-confirmed root cause, and mandatory cleanup.
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。
Comprehensive scientific research toolkit with 139 specialized skills for biology, chemistry, medicine, data science, and computational research. Transforms Claude into an AI research assistant with access to scientific databases, analysis tools, and domain-specific workflows.
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 code review checklist and pre-commit quality gates for PRs. Use when: reviewing pull requests, checking code quality, before committing code. Keywords: review, PR, pull request, checklist, quality, commit, 審查, 檢查, 簽入.
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。
Guide requirement writing, user story creation, and feature specification. Use when: writing requirements, user stories, issues, feature planning. Keywords: requirement, user story, issue, feature, specification, 需求, 功能規劃, 規格.
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 and order products online. Compares prices across Polish marketplaces (Allegro, Ceneo, Google Shopping), presents options in a comparison table, and assists with checkout. Handles the full flow: priority selection, research, comparison, cart, and checkout handoff. Triggers on: "order", "buy", "find product", "compare prices", "cheapest", "zamow", "kup", "znajdz produkt", "porownaj ceny", "najtaniej".
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。
Hunt vulnerabilities in LLM-powered applications — direct/indirect prompt injection, system prompt extraction, ASCII smuggling, RCE via code tools, agentic AI exploits (ASI01-ASI10), data exfiltration via response channels, IDOR in chat history, jailbreak chains, RAG poisoning. Use when target has a chatbot, AI assistant, copilot, or agentic AI 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。
Systematically gather comprehensive travel itinerary requirements through structured discovery questions, MCP-powered research (Perplexity, Exa), and expert detail gathering. Use when users request travel planning, itinerary creation, or trip assistance requiring deep research and personalized requirements gathering. Outputs detailed requirements specification with day-by-day itineraries.
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。
Channel Tom di Mino's daimonic voice for writing. Invoke when Tom asks to write prose, essays, tweets, poetry, or technical docs in his own voice—possessed by the daimon, not assisted by it. Triggers on: 'write as me', 'in my voice', 'channel', 'daimon', 'tamarru', 'waltz', or any request for Tom's authentic writing voice.
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。
Full AI-assisted workflow for long-form SEO content (2,000+ words). Covers JTBD strategy, E-E-A-T quality standards, and 5 production phases: research, outline, draft, editing, and SEO/publishing. Built for B2B marketing agency context. Use when: user wants to write or optimize an SEO article, mentions "SEO content", "blog post", "GTM article", "long-form content", or asks to create content with keyword strategy.
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 monochrome ornamental patterns grounded in Alexander Speltz's classical ornament taxonomy. Covers historical period selection, motif structural analysis, prompt construction for line art and silhouette rendering, and AI-assisted image generation via Z-Image. Use when creating decorative borders, medallions, or friezes in a single color, exploring historical ornament styles through generative AI, producing line art or pen-and-ink renderings of classical motifs, or generating reference imagery for design or educational materials.
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 a new agent definition file following the agent-almanac agent template and registry conventions. Covers persona design, tool selection, skill assignment, model choice, frontmatter schema, required sections, registry integration, and discovery symlink verification. Use when adding a new specialized agent to the library, defining a persona for a Claude Code subagent, or creating a domain-specific assistant with curated skills and tools.
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 a custom MCP (Model Context Protocol) server that exposes domain-specific tools to AI assistants. Covers server implementation in Node.js or R, tool definitions, the resources and prompts primitives, transport configuration, and testing with Claude Code. Use when you need to expose custom functionality beyond what mcptools provides, when building specialized domain-specific AI integrations, or when wrapping existing APIs or services as MCP tools.
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。
Connects the user with a LegalZoom attorney for legal consultation. Use when a user asks about attorneys, lawyers, or legal help, or when contract review reveals high risks or low-confidence findings.
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。
Detect and fix AI-generated writing patterns (slop). Comprehensive detection with 50+ patterns across 3 severity tiers, structure weighted above lexical tells, scoring system, and editor mode that directly fixes problems. Use when scanning any content for AI tells, auditing drafts before publishing, checking if writing "sounds like AI", humanizing AI-generated text, or verifying content authenticity. Trigger on "check for slop", "does this sound like AI", "humanize this", "AI audit", "slop check", "clean up AI writing", or any request to detect/remove artificial-sounding patterns. Also use proactively before publishing any AI-assisted content.
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。
重采样：把已有多轮对话数据的每条 assistant 回答丢弃，用 verifier 模型/vLLM 服务逐轮重生成，产出可直接进训练的预分词样本（每行含 input_ids/loss_mask）。依赖支持 return_token_ids 的 OpenAI 兼容 chat 服务（服务需由用户提前就绪，本技能不负责拉起服务）。触发：用某模型对某个数据集做重采样/重生成（如“对这个数据集用这个模型做重采样”）、把对话回答用模型重写、生成预分词样本/on-policy/MTP 训练数据。
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。