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Found 2,167 Skills
Tavily: web search optimized for AI agents, answer synthesis, domain filtering, depth control
Manage X07 project dependencies and lockfiles for reproducible builds (lock, publish). Designed for autonomous agents.
Spawn 10 independent parallel agents to analyze source material from distinct perspectives, synthesize findings, and apply improvements to a target agent or skill. Use when source material is complex and multi-angle extraction justifies 3-5x token cost over inline analysis. Use for "parallel analysis", "multi-perspective", or "deep extraction". Do NOT use for routine improvements, simple source material, or when token budget is limited.
Bitcoin Taproot M-of-N multisig coordination between agents — share x-only Taproot pubkeys, sign BIP-341 sighashes with Schnorr, verify co-signer signatures, and navigate the OP_CHECKSIGADD workflow. Proven on mainnet (2-of-2 block 937,849 and 3-of-3 block 938,206).
MUST be used whenever building a chat UI with Atlas agents in a Dune app. Do NOT manually write useAtlasChat integration code — this skill handles installation, component structure, and hook wiring. Triggers: useAtlasChat, atlas chat, streaming chat, agent chat, chat interface, chat component, chat UI. For a full chat app, run skills in order: (1) integrate-atlas-chat, (2) create-client-tool (per tool), (3) setup-python-tools (if Python tools needed).
Clayton Christensen's Disruption Analysis applied to a company, market, or business idea. Spawns a team of specialist agents — Disruption Cartographer, RPV Diagnostician, Jobs Archaeologist, Trajectory Analyst, Incumbent's Advocate — who each apply a distinct lens from Christensen's framework to evaluate disruption risk and opportunity. The lead synthesizes into a disruption verdict: is this company vulnerable to disruption from below, is this startup on a genuine disruption trajectory, or is this a sustaining innovation that incumbents will crush? Use when the user says "christensen this", "disruption analysis", "is this disruptive", "vulnerable to disruption", or wants to evaluate whether a company/market faces disruption risk. Works as a standalone analysis or paired with /munger for a complete picture.
Discovers, enriches, and scores local businesses in any neighborhood using Nimble Web Search Agents (WSAs) and web data. Returns a structured, ranked list with confidence scores, reviews, social presence, and an interactive map. Use this skill when the user asks about local businesses, places, or neighborhood discovery. Common triggers: "find all coffee shops in", "map every bar in", "local businesses in", "discover gyms near", "what restaurants are in", "neighborhood guide for", "local places in", "find places near", "list all [business type] in [area]", "best [type] near [location]", "build a neighborhood guide", "local place search". Requires the Nimble CLI (nimble agent run, nimble search, nimble extract) for live web data via WSAs and fallback search. Do NOT use for competitor analysis or monitoring (use competitor-intel), company research or deep dives (use company-deep-dive), general web search or extraction (use nimble-web-expert).
Interact with the Gemini Enterprise Agent Platform Skill Registry to create and search for available skills. Use this skill to enable agents to register functionality or discover new capabilities.
Comprehensive Python/FastAPI backend code review with optional parallel agents
Guide for using Apollo MCP Server to connect AI agents with GraphQL APIs. Use this skill when: (1) setting up or configuring Apollo MCP Server, (2) defining MCP tools from GraphQL operations, (3) using introspection tools (introspect, search, validate, execute), (4) troubleshooting MCP server connectivity or tool execution issues.
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
Build agentic AI with OpenAI Responses API - stateful conversations with preserved reasoning, built-in tools (Code Interpreter, File Search, Web Search), and MCP integration. Prevents 11 documented errors. Use when: building agents with persistent reasoning, using server-side tools, or migrating from Chat Completions/Assistants for better multi-turn performance.