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Found 4,745 Skills
Choose and refactor visionOS app architecture across surfaces, scene boundaries, state ownership, and file layout. Use when deciding window vs volume vs immersive space, splitting a feature across scenes, cleaning up a monolithic spatial root, or defining the ownership map before implementing SwiftUI or RealityKit details.
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
Use this skill when generating higher-level synthesis notes such as literature reviews, comparison matrices, project summaries, or other cross-note summaries inside the project knowledge base.
Transync AI platform help — real-time AI meeting translation with near-zero latency (<0.5s) dual-screen bilingual display across 60+ languages, AI voice broadcast, AI meeting notes with summaries, custom domain terminology via AI Assistant, system audio sharing (no bot/plugin) for Zoom/Teams/Meet/WhatsApp. Use when setting up Transync AI for multilingual meetings, Transync AI translation accuracy dropping with accents or technical terms, Transync AI dual-screen display not showing translations, Transync AI meeting notes not generating summaries, choosing between Transync AI Personal Premium vs Enterprise plan, Transync AI time card top-up running out mid-meeting, Transync AI voice broadcast not working, or Transync AI vs JotMe or Talo or Langfinity for real-time meeting translation. Do NOT use for general note-taker comparison across all platforms (use /sales-note-taker) or enterprise RSI with human interpreters (use /sales-kudo or /sales-interprefy).
Create and run orq.ai experiments — compare configurations against datasets using evaluators, analyze results, and generate prioritized action plans. Use when evaluating LLM agents, deployments, conversations, or RAG pipelines end-to-end. Do NOT use without a dataset and evaluators. Do NOT use for cross-framework comparisons with external agents (use compare-agents).
You are **Performance Benchmarker**, an expert performance testing and optimization specialist who measures, analyzes, and improves system performance across all applications and infrastructure. Yo...
Expert legal and compliance specialist ensuring business operations, data handling, and content creation comply with relevant laws, regulations, and industry standards across multiple jurisdictions.
Core technical-indicator signal engine for stocks listed in HK / US / A-share / Singapore via Longbridge Securities. Computes and interprets MACD, KDJ, RSI, Bollinger Bands, EMA, ADX, and OBV from OHLCV data; combines multi-dimensional votes (trend / mean-reversion / volume-price) to produce a composite buy / sell / neutral signal. Triggers: "技术指标", "MACD", "KDJ", "RSI", "布林带", "布林线", "EMA", "ADX", "OBV", "金叉", "死叉", "超买", "超卖", "技术分析", "趋势指标", "量价", "技術指標", "布林帶", "技術分析", "超買", "超賣", "technical indicator", "MACD signal", "KDJ overbought", "RSI oversold", "Bollinger Bands", "moving average", "golden cross", "death cross", "technical analysis".
Generate deep research reports on prediction market events using the Octagon Prediction Markets Agent. Combines real-time Kalshi market data with AI-driven analysis to surface price drivers, compare market vs. model probabilities, and identify potential mispricings across 120+ active markets.
Use when the user asks for a literature review, academic deep dive, research report, state-of-the-art survey, topic scoping, comparative analysis of methods/papers, grant background, or any request that needs multi-source scholarly evidence with citations. Also trigger proactively when a user question clearly requires academic grounding (e.g. "what's known about X", "compare approach A vs B in the literature", "summarize the field of Y"). Runs an 8-phase (Phase 0..7), script-driven research workflow across 7 federated sources (OpenAlex, arXiv, Crossref, PubMed, DBLP, bioRxiv, Exa) with optional Semantic Scholar / Brave MCP enrichment, with deduplication, transparent ranking, dual-backend citation chasing (OpenAlex + Semantic Scholar), self-critique, and structured report output with verifiable citations.
Graham cigar-butt batch screener — runs Benjamin Graham's NCAV / net-net / defensive-investor hard filters across an index or market universe and returns a ranked candidate list with NCAV ratio, PE, PB, dividend yield, debt coverage, 5y earnings stability, Graham buy price, and a dynamic value-trap warning. Longbridge CLI/MCP first; WebSearch fills genuine gaps (PMI, sector outlook). Every figure footnoted to its source. Auto-switches model for banks / insurance / REITs and flags <2y IPOs and suspended names. Triggers: "格雷厄姆筛选", "格雷厄姆选股", "捡烟蒂榜单", "烟蒂股榜", "NCAV筛选", "NCAV排行榜", "净流动资产筛选", "防御型投资者选股", "撿煙蒂榜單", "煙蒂股榜", "NCAV篩選", "淨流動資產篩選", "防禦型投資者選股", "Graham screen", "Graham screener", "NCAV screen", "net-net screen", "net-net list", "cigar-butt screen", "defensive investor screen", "liquidation value screen", "Benjamin Graham screen".
Summarizes WeChat group chat highlights into a structured digest using the local wx-cli binary (https://github.com/jackwener/wx-cli). Generates a normal digest by default; a roast (毒舌) version is opt-in. Maintains per-group history (history.json + history-digests.jsonl) and per-user profiles across runs, with privacy guardrails baked in. Use when the user asks to "总结群聊", "群聊精华", "群聊摘要", "summarize group chat", "group chat digest", mentions a WeChat group name with a time range, says "帮我看看 XX 群最近聊了什么", "XX 群有什么值得看的", or asks to "回溯画像" / "初始化画像" / "backfill profiles". Adds the roast version when the user says "毒舌版", "roast 版", "再来个毒舌的", or similar.