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Found 2,042 Skills
Decentralized git for AI agents and humans. Use when the user wants to create repositories, push code, open pull requests, review and merge PRs, manage issues, create or claim bounties, delegate tasks to other agents, register human-readable names on Base L2, or interact with the gitlawb decentralized git network. Supports cryptographic DID identities, Ed25519-signed pushes, UCAN capability delegation, libp2p networking, and 31+ MCP tools for AI agent integration. Do NOT use for GitHub, GitLab, or other centralized git hosts.
Evidence-first automation inventory and overlap audit workflow for ECC. Use when the user wants to know which jobs, hooks, connectors, MCP servers, or wrappers are live, broken, redundant, or missing before fixing anything.
AI screen memory — search everything you've seen or heard on your computer. Integrates with Screenpipe's local MCP server for OCR text, audio transcripts, and app usage history.
Use when running Playwright via terminal CLI — `npx playwright test` (test runner), `codegen` (interactive recording), `screenshot` / `pdf` (one-off captures), and CI sharding. NOT for agent-driven real-time browser control (use `claude-in-chrome` MCP tools for that).
Manage the Grounded Docs MCP Server documentation index. Covers scraping and indexing documentation from URLs or local files, refreshing existing indexes with changed content, and removing libraries from the index. Use when you need to add, update, or delete indexed documentation.
Use this skill when a user provides a torrent name or file name and wants to fix recognition issues, or asks to add/manage custom identifiers (自定义识别词). This skill generates identifier rules based on the WordsMatcher preprocessing logic, checks for duplicates against existing rules, and saves them via MCP tools. Because custom identifiers are global, generated rules must default to conservative, sample-specific regex patterns instead of broad matches unless the user explicitly wants global cleanup. Applicable scenarios include: 1) A torrent or file name is incorrectly recognized (wrong title, season, episode, etc.); 2) The user wants to block unwanted keywords from torrent names; 3) The user needs episode offset rules for series with non-standard numbering; 4) The user wants to force recognition of a specific media by TMDB/Douban ID.
AI HOT (aihot.virxact.com) Chinese AI News Query Skill. Trigger this Skill when users ask any Chinese AI information queries such as "What's happening in the AI circle today", "AI Daily", "AI HOT", "AI News", "AI Hot Topics", "Latest AI Updates", "What have OpenAI/Anthropic/Google released recently", "AI hot today", "AI news today", "Check AI industry trends", "What large models are released today", "AI circle updates from yesterday", "Check selected items", "AI HOT Selected", "AI papers from the past week", "AI model releases", "AI product launches", "AI industry dynamics", "AI tips and insights". Even if users only say "AI circle", "AI news", "AI Daily", or just ask "What happened today" with context related to AI / large models / LLM / startup fields, this Skill should be triggered. The Skill directly pulls data via curl from public REST APIs and organizes it into Chinese markdown briefings, with no need for users to configure any API Key or MCP server. **Do NOT undertrigger**——If users ask for AI news and you don't invoke this Skill, you are treating outdated training data as today's news, which is harmful to users.
Buffett-style stock screener — "What would Buffett buy now?" Generates 3–5 candidate stocks from a market / sector / preference query via a two-layer model: hard quant filter (ROE 5y ≥15%, debt/asset ≤50%, FCF positive 3y, listed ≥5y, gross margin ≥30%) → qualitative moat scoring (moat 35% / capital allocation 20% / earnings predictability 20% / valuation 15% / runway 10%). Longbridge CLI first, MCP fallback, WebSearch for gaps only. Output: candidate cards with moat-type tag, quantitative highlights, verdict (🟢 likely buy / 🟡 wait for price / 🔴 not at this price), deep-dive CTA to `longbridge-buffett-moat-analyzer`. Mandatory holding-period education + data-source appendix. Disqualifies airlines, pre-revenue biotech, ST, listing<5y. Triggers: "巴菲特会买什么", "巴菲特选股", "巴菲特风格的股票", "护城河选股", "宽护城河股票", "价值投资选股", "10年不动的股票", "定价权强的公司", "巴菲特會買什麼", "巴菲特選股", "護城河選股", "寬護城河股票", "Buffett screener", "what would Buffett buy", "wide-moat screener", "quality compounder screen", "Berkshire-style screen", "pricing-power screen".
Build AI agents with in-process agent loops using Anthropic or OpenAI APIs, custom tools, MCP servers, and multi-turn conversations
Local MCP memory server for AI coding assistants with verbatim recall, semantic search, and automatic session capture
Draft inbound and outbound RFQ emails for freight quotations. Inbound = a customer asks the forwarder for a quote; outbound = the forwarder asks a carrier or agent for a rate. Uses the practice profile for tone, sign-off, reference format, and lane-specific agent picks. Reads attached threads and dimensions when Gmail or Outlook MCP is connected. TRIGGER: RFQ, quote request, cerere de ofertă, ofertă freight, draft an RFQ, scrie un RFQ, write an RFQ email, email partener, email transportator, trimite la agent, send to carrier, rate request, freight quote email, cotație, request a rate, ask the agent. Also: "scrie un email la agentul din", "draft RFQ to", "cerere ofertă pe lane-ul", "trimite cerere rată la", "email către carrier pentru shipmentul de", "follow up on the quote", "chase the rate".
Garbage collection for your Claude Code configuration. Periodically scans ~/.claude (skills, memory, hooks, permissions, MCP servers, caches) for redundant, stale, orphaned, or low-value items, then walks the user through a confirm-each-deletion cleanup. Use when the user says "clean up my config", "config GC", "too many skills", "audit my setup", "my .claude is bloated", or asks for a periodic config review.