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Found 7,010 Skills
Run any question, idea, or decision through a council of 5 AI advisors who independently analyze it, peer-review each other anonymously, and synthesize a final verdict. Based on Karpathy's LLM Council methodology. MANDATORY TRIGGERS: 'council this', 'run the council', 'war room this', 'pressure-test this', 'stress-test this', 'debate this'. STRONG TRIGGERS (use when combined with a real decision or tradeoff): 'should I X or Y', 'which option', 'what would you do', 'is this the right move', 'validate this', 'get multiple perspectives', 'I can't decide', 'I'm torn between'. Do NOT trigger on simple yes/no questions, factual lookups, or casual 'should I' without a meaningful tradeoff (e.g. 'should I use markdown' is not a council question). DO trigger when the user presents a genuine decision with stakes, multiple options, and context that suggests they want it pressure-tested from multiple angles.
ListenHub CLI skills router. Routes to the correct skill based on user intent. Triggers on: "make a podcast", "explainer video", "read aloud", "TTS", "generate image", "做播客", "解说视频", "朗读", "生成图片", "幻灯片", "slides", "音乐", "music", "generate music", "翻唱", "cover song", "parse URL", "解析链接", "提取内容".
An intelligent search router based on opencli commands. This skill must be used when users want to search, query, look up or research information, especially when it involves specified websites, social media, technical materials, news, shopping, travel, job hunting, finance or Chinese content.
Compound V3 (Comet) lending plugin: supply collateral, borrow/repay the base asset, and claim COMP rewards. Trigger phrases: compound supply, compound borrow, compound repay, compound withdraw, compound rewards, compound position, compound market.
Global writing style specification for Chinese technical writing. Applicable to all Chinese content production (WeChat Official Accounts, blogs, X, knowledge bases, AIW). Cited by gracker-writer and content-quality-gate.
Swap tokens and manage liquidity on PancakeSwap V3 on BNB Chain, Base, and Arbitrum
Use this skill whenever deciding what features to extract from raw marketplace assets — listing photos, owner-entered listing metadata, sitter wizard responses — to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders in a two-sided trust marketplace. Covers asset auditing, first-principles feature decomposition from the decision the user is making, vision-feature extraction (CLIP, room-type classification, amenity detection, aesthetic and quality scoring), listing text and metadata encoding (categoricals, multi-hot amenities, H3 geo-hashing, sentence-transformer description embeddings, structured pet triples), sitter wizard design (information-gain ordering, multiple-choice over free text, genuine skippability, hard constraint versus soft preference), derived-composition patterns for i2i / u2i / u2u (precomputed ANN shelves, multi-modal fusion, two-tower affinity, symmetric mutual-fit scoring, interpretable subscores), feature quality governance (single registry, training-serving parity, coverage and drift alarms, PII scrubbing, schema versioning), and incremental value proof (one feature at a time, ablation A/B, kill reviews, exploration slice, permanent feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.
Apply constructivist learning theory to design instruction based on active knowledge construction, scaffolding, and the zone of proximal development. Use this skill when the user needs to design learner-centered instruction, apply Vygotsky's ZPD or scaffolding principles, or evaluate learning environments for constructivist alignment — even if they say 'how do people really learn', 'student-centered design', or 'scaffolding for learners'.
Frontend full-chain performance optimization guide based on Web Vitals metrics. Provides metric thresholds, diagnostic methods, and optimization strategies for LCP, FCP, INP, CLS, TTFB, TBT. Use when optimizing frontend performance, analyzing Web Vitals, reducing page load time, fixing layout shifts, improving interaction responsiveness, or reviewing frontend code for performance issues.
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.
Process raw source documents into wiki pages. Use when the user adds files to raw/ and wants them ingested, says "process this source", "ingest this article", "I added something to raw/", or wants to incorporate new material into their wiki, second brain, or knowledge base.
Run a decision through 5 AI advisors with different thinking styles, anonymous peer review, and chairman synthesis. For genuine decisions with stakes and tradeoffs — not simple questions. Based on Karpathy's LLM Council.