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Found 9,300 Skills
Detect and exploit blind Server-Side Request Forgery vulnerabilities using out-of-band techniques, DNS interactions, and timing analysis to access internal services and cloud metadata endpoints.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
Equips engineering managers with persuasion techniques and positioning strategies for getting things done without direct authority — produces tactical methods (Nemawashi, Decoy Pricing, Reverse Psychology, LMDTFY, Engineered Serendipity), conversation techniques for disarming resistance (Label the Concern, Get to "That's Right"), a headcount argument framework, and a three-level visibility/trust model. Use when the user says "how do I convince," "persuade," "get buy-in," "stakeholder management," "influence without authority," "get approval," "calibration," "nobody takes me seriously," "how do I get headcount," or "organizational politics." Do NOT use when the issue is the user's relationship with their own manager (use managing-up).
Push and publish custom AI models to Replicate, and set up CI/CD for releasing new model versions safely. Use when running cog push, deploying a model to Replicate, releasing a new version, validating a model with cog-safe-push before publishing, configuring a Replicate deployment, setting up GitHub Actions for model releases, or porting a community model to an official one. Trigger on phrases like "push a model to Replicate", "publish a model", "deploy a model", "release a new version", "cog push", "cog-safe-push", "model CI", "r8.im", or "schema compatibility", and when referencing github.com/replicate/cog-safe-push or github.com/replicate/model-ci-template. Covers cog push, the full cog-safe-push config (test cases, fuzz, deployment, official_model), GitHub Actions patterns, multi-model matrix pushes, and post-publish monitoring. Assumes you already have a working Cog project; see build-models if you need to package one first.
Vision-driven desktop automation using Midscene. Control your desktop (macOS, Windows, Linux) with natural language commands. Operates entirely from screenshots — no DOM or accessibility labels required. Can interact with all visible elements on screen regardless of technology stack. ⚠️ Takes over the user's real mouse and keyboard. For web apps, prefer "Browser Automation" instead. Only use this for desktop-native apps (Electron, Qt, native macOS/Windows/Linux) that cannot run in a browser. Triggers: open app, press key, desktop, computer, click on screen, type text, screenshot desktop, launch application, switch window, desktop automation, control computer, mouse click, keyboard shortcut, screen capture, find on screen, read screen, verify window, close app, test Electron app Powered by Midscene.js (https://midscenejs.com)
ElevenLabs multi-speaker dialogue generation - create conversations with different voices in a single audio file via inference.sh CLI. Capabilities: multi-voice dialogue, script-based generation, voice direction, conversation audio. Use for: podcasts, audiobooks, explainers, tutorials, character dialogue, video scripts. Triggers: elevenlabs dialogue, eleven labs dialogue, multi speaker, conversation audio, dialogue generation, text to dialogue, multi voice, voice acting, podcast dialogue, character voices, script to audio, elevenlabs conversation, two speakers
Create talking head videos and lip sync audio to video via fal.ai. Useful for explainer avatars, multilingual dubbing previews, and social cuts.
Professional workflow for creating bespoke vector and complex raster icons. Professional icon design workflow including Design (visual), Path (vector), Desire (conceptual), and 3D chroma-key modes. Features AI prompt engineering, automated binarization, alpha cleanup, and SVG optimization.
WireGuard VPN server setup, peer configuration, key generation, split tunneling vs full tunnel routing, and remote access to a home network from mobile and laptop clients.
Graham cigar-butt (NCAV / net-net) single-stock diagnostic. Combines a 100-point static cheapness score (NCAV, PE, PB, dividend yield, debt coverage, earnings stability) with a dynamic adjustment layer (industry cycle, earnings trend, insider activity, NCAV trajectory) to separate real bargains from value traps. Pulls data from Longbridge CLI/MCP first, falls back to WebSearch only for gaps, runs cross-statement reconciliation (勾稽校验) before scoring, and footnotes every figure to its source. Triggers: "格雷厄姆", "捡烟蒂", "烟蒂股", "烟蒂投资", "NCAV", "净流动资产", "清算价值", "安全边际", "价值陷阱", "深度价值", "撿煙蒂", "煙蒂股", "煙蒂投資", "淨流動資產", "清算價值", "安全邊際", "價值陷阱", "深度價值", "Graham", "cigar butt", "net-net", "liquidation value", "value trap", "margin of safety", "deep value", "Benjamin Graham".
Company tear sheet / one-pager via Longbridge Securities — generates a high-density 1–2 page company snapshot: business overview, key financials (revenue / net income / EPS / ROE), valuation multiples (PE / PB / EV-EBITDA), price performance, major shareholders, and recent catalysts. Triggers: "公司单页", "公司快照", "公司简报", "公司画像", "一页纸分析", "公司概要", "股票简报", "公司單頁", "公司快照", "公司簡報", "公司畫像", "一頁紙分析", "company tearsheet", "company profile", "company snapshot", "one-pager", "company brief", "stock summary", "company factsheet".
Tech hype vs. fundamentals analysis via Longbridge — identifies valuation bubbles and fundamental disconnects in A-share / HK tech stocks. Compares PE / PS / EV-EBITDA historical percentile against actual revenue / profit growth. Analyses which AI / EV / semiconductor theme plays have fundamental support vs. pure sentiment-driven momentum. Triggers: "科技炒作", "AI泡沫", "估值泡沫", "科技估值", "概念股", "主题炒作", "基本面背离", "炒作识别", "科技泡沫", "科技炒作", "AI泡沫", "估值泡沫", "科技估值", "概念股", "主題炒作", "基本面背離", "tech hype", "AI bubble", "valuation bubble", "tech valuation", "theme stocks", "hype vs fundamentals", "concept stocks", "narrative vs reality", "AI concept", "semiconductor bubble".