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Found 4,647 Skills
Build or modify reusable registry content for board designers. Use when fulfilling a librarian request or when asked to add/fix a registry component, symbol, footprint, STEP model, datasheet, family selector, or datasheet-backed Zener reference circuit. Covers artifact import, symbol/footprint cleanup, package structure, sourceability, and validation.
Create and manage DESIGN.md files. Useful for capturing design direction, tokens, and visual rules in a single source of truth.
Use Parallel's parallel-cli to do live web search, URL extraction (clean markdown), deep research reports, bulk data enrichment (CSV/JSON), FindAll entity discovery, and web monitoring. Use when the user asks to look something up online, needs current sources/citations, provides URLs to read or summarise, requests deep/exhaustive research, wants to enrich a dataset with web-sourced fields, wants a list of entities (companies/people/places), or wants to monitor the web for changes over time.
Build and deploy applications on inference.sh. Use when getting started, understanding the platform, creating apps, configuring resources, or needing an overview of inference.sh app development. Supports both Python and Node.js. Triggers: inference.sh app, belt app, inf.yml, inference.py, inference.js, deploy app, app development, build app, create app, GPU app, VRAM, app resources, app secrets, app integrations, multi-function app
Industry valuation rank time series for a single stock via Longbridge — tracks how a stock's PE / PB / PS / dividend-yield rank within its sector has changed over time (rank N of total M). Answers "is my stock becoming relatively cheaper or more expensive vs peers?" Complements longbridge-valuation (single-stock percentile history) and longbridge-industry-valuation (current sector snapshot). Triggers: "行业排名变化", "估值排名", "PE排名历史", "行业估值位置", "排名走势", "估值相对同业", "行業排名變化", "估值排名", "PE排名歷史", "行業估值位置", "排名走勢", "valuation rank", "industry rank history", "PE rank trend", "relative valuation rank", "sector ranking over time", "how does AAPL rank in industry PE".
Multi-source literature search, citation verification, MeSH search strategy, citation file management (.nbib/.ris/.bib conversion), and reference management (BibTeX, related articles, ID conversion) via MCP tools (PubMed, CrossRef, arXiv). Use when the user needs coordinated multi-step literature workflows beyond a single MCP call.
Plan an Israeli wedding from engagement to chuppah, covering venue selection (ulmot, ganot aruim), vendor comparison via Israeli platforms (Celebrate, Engaged, Save A Date, Walla Wedding), budget planning (~100-140K NIS average), Rabbinate registration (tik nisuin, teudat ravakut), halachic requirements (mikveh, ketuba), guest management, per-plate cost optimization, seasonal pricing, and timeline creation. Use when user asks about "chatuna b'yisrael", Israeli wedding planning, wedding budget, "ulam aruim", "ulmot", "ganim", wedding vendors, Rabbinate requirements, "tik nisuin", ketuba, or wedding timeline. Prevents common mistakes like missing Rabbinate deadlines, overpaying on Thursday weddings, or forgetting AKUM fees. Do NOT use for destination weddings abroad, non-Jewish religious ceremonies, or divorce proceedings.
ARK-style single-stock disruptive-innovation diagnostic. Suitability gate on 4 dimensions (platform fit / innovation revenue / R&D intensity / management vision); if it passes, builds TAM (low/base/high), Wright's-Law cost curve with sourced learning rate, three-scenario 5-year target (Bull/Base/Bear, 15% discount), risks, conditional action frame. Data: Longbridge CLI first, MCP fallback, WebSearch only for TAM / learning rates / industry runway. Runs cross-statement reconciliation BEFORE analysis. Closes with a data-source appendix whose final row is the reconciliation summary. Independent implementation — not affiliated with ARK Invest. Triggers: "木头姐", "ARK", "ARKK", "颠覆式创新", "莱特定律", "TAM", "5年目标价", "情景分析", "AI 与大数据", "自动化与机器人", "能源存储", "基因革命", "区块链与金融科技", "木頭姐", "顛覆式創新", "萊特定律", "5年目標價", "Cathie Wood", "ARK Invest", "disruptive innovation", "Wright's Law", "learning rate", "bull base bear", "scenario analysis".
Plan and orchestrate end-to-end video production pipelines in ComfyUI with validation gates and error recovery. Handles img2vid, txt2vid, vid2vid, and multi-shot video production. Produces pipeline plans with correct step ordering (generate, validate, animate, validate, concat), model selection, retry strategies (seed randomization, parameter adjustment, model fallback), and VRAM-aware resource management. Use when asked to make a video, animate images, create a multi-shot video, set up a video pipeline, or orchestrate video production in ComfyUI. Does NOT cover still image generation, prompt writing, workflow building for non-video tasks, video editing in external tools, model training, installation, or hardware recommendations.
Exa.ai deep research and answer generation with citations. Use when building research automation, implementing Answer API for Q&A with sources, creating research reports, or using deep search with summaries. Triggers on: Exa Answer, answer endpoint, exa.answer, deep search, research API, Exa Research, async research, research report, citation extraction, summarization with sources, fact verification, streaming answers, research tasks.
Browser automation skill for controlling Google's NotebookLM. Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio Overview, infographics, slide decks, study guides, briefing docs, mind maps, timelines, FAQs), and creating new notebooks. Triggers on any phrase involving NotebookLM — 'open NotebookLM', 'check my [name] notebook', 'pull info from NotebookLM', 'ask my notebook about X', 'add [source] to NotebookLM', 'create an infographic in NotebookLM', 'use NotebookLM Studio', 'generate a slide deck from my notebook', or any variation where the goal involves NotebookLM. Requires browser automation environment — fails gracefully when unavailable.
Configures the rollout shape of a PostHog experiment — the variant split (50/50, 80/20, A/B/C ratios), the overall rollout percentage that gates how many users enter the experiment, and the disambiguation when a percentage like "roll out to 25%" could mean either. Use when the user mentions a rollout percentage, variant split, or traffic distribution; gives a ratio like 60/40, 70/30, or 80/20; asks "who sees the test variant?"; wants to increase, decrease, or change the rollout or split on a draft or running experiment; weighs equal vs uneven splits; or proposes a mid-experiment split change (often an anti-pattern that needs reset or end-and-restart).