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Found 584 Skills
Produce distinctive, non-generic UI and design applications well, working strategy-first. Identify the project (landing page, SaaS app, dashboard, ecommerce, presentation, docs, portfolio...) and its positioning and personality, commit to brand adjectives, translate into a typography and color system, then apply the craft layer (layout, components and states, motion, iconography, imagery, dark mode and theming, accessibility), avoiding the AI-slop / Claude-esque default. This is both a de-slop and an expert app-design skill. Use this whenever building or styling any web frontend, app, dashboard, landing page, deck, or artifact, or when the user says "make it not look like AI", "de-slopify", "deslop", "less generic", "give it character", "design a UI for X", "design an app", "update DESIGN.md", or complains the output looks like every other AI site. Trigger even when the user just says "build a UI for X" without naming an aesthetic, because the default without this skill is slop.
Build compelling business cases to justify investments and secure funding. Quantify benefits, assess costs, manage risks, and present compelling ROI arguments to leadership.
Formulate and audit real strategy using Richard Rumelt's "Good Strategy Bad Strategy": an honest diagnosis, a guiding policy, and coherent action instead of goals, vision, and wishful thinking. Use when the user mentions "good strategy bad strategy", "strategy kernel", "diagnosis guiding policy coherent action", "our strategy is just goals", "strategic planning", "mission vs strategy", "annual plan", or "is this actually a strategy". Also trigger when auditing a strategy doc or pitch deck for fluff, turning a goal list into real strategy, formulating strategy for a product or company, or finding leverage and proximate objectives. Covers the kernel of strategy, bad-strategy detection, and sources of power. For product positioning, see obviously-awesome. For uncontested markets, see blue-ocean-strategy.
Progressive-disclosure workflow for pandapower power-system studies. Use whenever the user wants to load, build, or inspect a pandapower network, run AC power flow, check bus voltages or line and transformer loading, or screen N-1 contingencies — even when they just say "run a load flow", "check this case", "is anything overloaded", or name a case file like case39.json. Exposes a clean base-case solve before advanced outage studies. Reach for this instead of answering pandapower grid questions unaided.
[BETA] Dogfood the active branch end-to-end as a QA engineer. Diffs the branch against main, builds an exhaustive browser test matrix of every change (full user journeys, not just features), drives the app with agent-browser, then auto-fixes issues, adds regression tests, and commits each fix until the matrix is green. Use when you want a hands-off 'test everything we just built and make it actually work' pass before shipping.
Daily digest of 3-7 genuinely surprising items from newsletters and Telegram channels. Scores content for epistemic friction, not just relevance. Appends to daily note. Use when the user says "/wow-digest", "run the wow digest", "what's surprising today", "morning reading", or "digest my newsletters".
End-to-end epidemiological data analysis — from research question to statistical report. Covers study design assessment, dataset discovery and download, data wrangling, confounder adjustment, regression modeling, sensitivity analysis, visualization, and biological interpretation. Integrates ToolUniverse tools for dataset discovery, literature search, and biological context with Python code execution for data analysis. Use whenever users ask to analyze health data, study disease risk factors, assess exposure-outcome relationships, or conduct observational epidemiology. Also use when users want to run regression on clinical/survey data, calculate odds ratios or hazard ratios from a dataset, adjust for confounders, or produce a Table 1. If the task involves downloading a health dataset and running statistical analysis on it, this is the right skill.
Build good-looking web interfaces. Use when: - User asks you to build a web app, website, landing page, or HTML page - User asks for a one-off tool, utility, or demo app - User is starting a new frontend project - User wants to improve how something looks - User mentions UI, design, styling, or making something look better This applies to ANY frontend work, not just "design" tasks. Even simple apps benefit from basic design principles.
Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.
Run and control a user's app on a remote iOS/Android simulator hosted on EAS cloud. Always read before executing any `eas simulator:*` commands — it has the current syntax for this experimental API. Use whenever the user needs a simulator they can't run locally — 'run my app on a cloud simulator', 'use eas simulator to run/install/screenshot my app', 'I'm on Linux/Cursor and need an iOS device', 'no sim on this box / headless CI', 'let an agent click through my app and screenshot it', 'test my dev build on a remote sim with live reload', 'stream a sim's screen to my browser' — even when they don't say 'EAS Simulator' or 'cloud'. On a host WITHOUT a local simulator (Linux, CI, cloud sandbox) it's the default — just use it; on macOS, do NOT auto-trigger for a plain 'run on the simulator' — use it only for a cloud/remote/shareable sim, an iOS version they lack, or an agent-driven session. NOT for local sims (expo run:ios, Xcode, Android Studio), EAS Build/Update, web preview, or physical devices.
Risk-return optimisation for investment portfolios via Longbridge — builds risk-adjusted return-optimal portfolios based on fund size, risk preference (conservative / balanced / aggressive), and investment horizon. Asset allocation across equities / bonds / cash / commodities / alternatives. Evaluates current portfolio efficiency versus the efficient frontier. Triggers: "风险收益优化", "组合效率", "有效前沿", "风险偏好配置", "最优组合", "风险调整收益", "大类资产配置", "投资组合优化", "風險收益優化", "組合效率", "有效前沿", "風險偏好配置", "最優組合", "risk-return optimization", "portfolio efficiency", "efficient frontier", "risk preference", "optimal portfolio", "risk-adjusted return", "asset class allocation", "portfolio optimisation", "mean variance".
Run a final release checklist before shipping. Verifies no TODOs, no debug code, docs updated, tests passing, dependencies justified, and security reviewed.