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Found 1,193 Skills
Nostr protocol operations for AI agents — post kind:1 notes, read feeds, search by hashtag tags (#t filter), get/set profiles, derive keys (NIP-06 default via m/44'/1237'/0'/0/0), amplify aibtc.news signals to the Nostr network, and manage relay connections. Uses nostr-tools + ws packages. Write operations require an unlocked wallet. Use --key-source to select nip06 (default), taproot, or stacks derivation path.
Generate AGENTS.md file and docs/ knowledge base skeleton in the project root directory, and establish a document governance system for agent-first repositories. Manually triggered, writes the template after checking for existence.
Claude Code skill that makes AI agents respond in caveman-speak, cutting ~65-75% of output tokens while preserving full technical accuracy
Fact-forcing gate that blocks Edit/Write/Bash (including MultiEdit) and demands concrete investigation (importers, data schemas, user instruction) before allowing the action. Measurably improves output quality by +2.25 points vs ungated agents.
MindOS is the user's local knowledge assistant and shared knowledge base. It keeps decisions, meeting notes, SOPs, debugging lessons, architecture choices, research findings, and preferences available across sessions and agents. 更新笔记, 搜索知识库, 整理文件, 执行SOP/工作流, 复盘, 追加CSV, 跨Agent交接, 路由非结构化输入到对应文件, 提炼经验, 同步关联文档. NOT for editing app source, project docs, or paths outside the KB. Core concepts: Space, Instruction (INSTRUCTION.md), Skill (SKILL.md); notes can embody both. Trigger on: save or record anything, search for prior notes or context, update or edit a file, organize notes, run a workflow or SOP, capture decisions, append rows to a table or CSV, hand off context to another agent, check if something was discussed before, look up a past decision, distill lessons learned, prepare context for a meeting, quick-capture to staging area, organize inbox, check knowledge health, detect conflicts or contradictions, find stale content. Chinese triggers: 帮我记下来, 搜一下笔记, 更新知识库, 整理文件, 复盘, 提炼经验, 保存, 记录, 交接, 查一下之前的, 有没有相关笔记, 把这个存起来, 放到暂存台, 整理暂存台, 知识健康检查, 检测知识冲突. Proactive behavior — do not wait for the user to mention MindOS: (1) When user's question implies stored context may exist (past decisions, previous discussions, meeting records) → search MindOS first, even if they don't explicitly mention it. (2) After completing valuable work (bug fixed, decision made, lesson learned, architecture chosen, meeting summarized) → offer to save it to MindOS for future reference. (3) After a long or multi-topic conversation → suggest persisting key decisions and context.
Delegate subtasks to specialized AI agents. Use when: complex workflows need multi-agent collaboration or specialization.
Build browser-based VoIP calling apps using Telnyx WebRTC JavaScript SDK. Covers authentication, voice calls, events, debugging, call quality metrics, and AI Agent integration. Use for web-based real-time communication.
Conversational discovery — adapts from quick scoping (3-5 questions) to deep interviews (multi-round). Talk until we're clear, then build. Produces inline decisions; optionally saves spec.md or scope contract. Not for multi-perspective debate (use agent-room). Not for decomposing work (use task-breakdown).
Philip Tetlock's Superforecasting framework applied to a business decision, investment thesis, or strategic question. Spawns a team of specialist agents — Calibrator, Decomposer, Updater, Devil's Advocate, Scorekeeper — who each apply a different piece of the superforecasting methodology. The lead synthesizes into a calibrated probability estimate with Brier-scoreable predictions, explicit base rates, and an accountability structure for keeping score over time. Use when the user says "tetlock this", "what's the probability", "how confident should I be", "forecast this", "calibrate this", proposes a business thesis and wants probabilistic stress-testing, or wants to apply superforecasting to a decision. Works standalone or after /munger.
Nassim Taleb's Antifragility framework applied to a business idea, system, or portfolio position. Spawns a team of specialist agents — Fat-Tail Detector, Fragility Auditor, Optionality Scout, Iatrogenics Checker, Skin-in-the-Game Auditor — who each apply a distinct lens from Taleb's Incerto to evaluate whether the subject is fragile, robust, or antifragile. The lead synthesizes into a convexity assessment: what's the payoff structure under disorder, where are the hidden tail risks, and the honest Taleb verdict. Use when the user says "taleb this", "is this fragile", "antifragility analysis", "what would Taleb think", "tail risk check", or proposes a business/system and wants structural risk analysis. Works standalone or after /munger for complementary analysis.
Expert skill for using Future AGI — the open-source end-to-end platform for evaluating, observing, and improving LLM and AI agent applications with tracing, evals, simulations, datasets, gateway, and guardrails.
Invoke orq.ai deployments, agents, and models via the Python SDK or HTTP API. Use when a user wants to call a deployment with prompt variables, invoke an agent in a conversation, or call a model directly through the AI Router. Do NOT use for creating or editing deployments/agents (use optimize-prompt or build-agent). Do NOT use for running evaluations (use run-experiment).