Skills
100,344 packaged capabilities and playbooks for agents. Install any of them with one command.
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Search a codebase or UI for places that don't animate but should, and reject everything that shouldn't. Read-only; it proposes motion with exact values, it does not implement it. Use when the user asks "what could be animated here?" or wants to "make this feel more alive". For fixing existing animations, use improve-animations or review-animations instead.
Interactive quiz that maps your AI/ML knowledge to a starting point in the 523-lesson, 20-phase AI Engineering from Scratch curriculum. Triggers on: "where should I start", "find my level", "what do I know", "which phase", "assess my knowledge", "placement test", "skip ahead".
Apply first-principles thinking to break down complex problems into fundamental truths and rebuild solutions from the ground up. Use when solving strategic decisions, breaking through blockers, or challenging assumptions. Triggers on first principles, Elon Musk methodology, fundamental truths, strip assumptions, physics of the problem, rebuild from scratch.
INVOKE THIS SKILL at the START of any LangChain/LangGraph/Deep Agents project, before writing any agent code. Determines which framework layer is right for the task: LangChain, LangGraph, Deep Agents, or a combination. Must be consulted before other agent skills.
Use when writing, reviewing, or refactoring TypeScript frontend code, including React components, Astro or Next.js apps, shared UI packages, client state, server-state fetching, forms, styling systems, routing, analytics, frontend tests, and frontend architecture decisions.
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics
Diagnose a codebase's frontend design maturity and route the work through the right sequence of frontend-design skills. Use when the user makes a broad or vague design request — 'make this look better', 'polish the UI', 'this feels like an MVP', 'our frontend is a mess', 'where do I start with design', 'make it feel professional', 'plan a design system' — or when a request spans multiple design concerns and it's unclear which single skill applies. Assesses maturity by inspecting the actual code (Tailwind config, CSS variables, component structure, hex-code drift), picks one of five implementation paths, and produces a design roadmap: an evidence-backed maturity diagnosis plus an ordered skill sequence with the artifact each step passes to the next. If the request clearly maps to one concern (only spacing, only color, only type), skip this and invoke that skill directly.
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".
Modern web development expertise covering React, Node.js, databases, and full-stack architecture. Use when: building web applications, developing APIs, creating frontends, setting up databases, deploying web apps, or when user mentions React, Next.js, Express, REST API, GraphQL, MongoDB, PostgreSQL, or full-stack development.
Rankup's dedicated skill for mini-game opportunities. Used to monitor game platform sitemaps, discover new game keywords within 24 hours, build multilingual keyword demand clusters, verify KD/SERP/7-day trends/standalone demand/playable supply, and generate a daily decision report; shared rules and scripts are committed with Rankup, while project-private results go into the Git-ignored .rankup/.
Turn a game idea into a focused design problem, risk-first prototype plan, and iteration loop. Use when starting a game, choosing what to build first, scoping an MVP, defining design questions, planning prototypes, or deciding whether an idea is ready for production.
Design and review game spatial query code for raycasts, picking, collision predicates, containment tests, orientation tests, and point/line/plane distances. Use when implementing or debugging ray-triangle hits, point-in-triangle checks, barycentric constraints, signed distance tests, or geometry predicates.
Design and debug game transform systems, coordinate spaces, local/world/view/projection matrices, homogeneous coordinates, camera constraints, and inverse transforms. Use when implementing object transforms, camera math, parent-child transforms, projection/unprojection, or coordinate-space conversions.
Deploy containerised applications to Google Cloud Run from source using gcloud CLI. Use when the user asks to "deploy to GCP", "deploy to Cloud Run", "ship to Google Cloud", "gcloud run deploy", or needs to set up, redeploy, configure env vars/secrets, view logs, or troubleshoot a Cloud Run service.
[Hyper] Use when the user wants to invoke Google Gemini CLI (`gemini`) for reasoning, research, or AI assistance. Trigger phrases: "use gemini", "ask gemini", "run gemini", "call gemini", "gemini cli", "Google AI", "Gemini reasoning", or when users request Google's Gemini models, research with web search, plan-mode review, or want to resume a previous Gemini session. Do not use for generic writing, runbook cleanup, or local edits that do not require the Gemini CLI.
Generate bilingual GitHub release documentation (README.md + README.zh.md) from repo metadata and user input, and guide release prep with git add/commit/push. Use when the user asks to write or polish README files, create bilingual docs, prepare a GitHub release, or mentions release assistant/README generation.
Use when refactoring existing code toward a known GoF pattern — phrases like "refactor this to use Strategy", "extract a Decorator from these flags", "this switch should be State", "replace conditional with polymorphism", "introduce a Factory here". Sequences small behavior-preserving refactor steps and links to the destination pattern skill.
Google Analytics 4 implementation and analysis reference. Use when querying live GA4 report data, setting up a GA4 property or data stream, installing gtag.js, designing event or ecommerce tracking, registering custom dimensions or audiences, sending server-side events via Measurement Protocol, linking GA4 to BigQuery and understanding the export schema, debugging with DebugView, or configuring Consent Mode and data retention. Do NOT use for general GTM container work such as triggers, variables, custom templates or server-side containers - use google-tagmanager; Google Ads scripting or ad reporting - use google-ads-scripts; writing or running SQL against GA4 export tables - use the bigquery skill; Universal Analytics or non-Google analytics tools.
A one-question version of grilling. Used to sharpen the user's plan, decision or idea through follow-up questions, but asks only one question per round to avoid throwing multiple questions at once. Suited to users who explicitly ask to "ask only one question at a time" or who want a slower, more focused design interview.
Strategic analysis of Google Search Console data for startup founders and marketing leaders. Transforms GSC exports into actionable growth insights with revenue impact estimates, competitive positioning, and prioritized action plans. Use when a user provides a GSC zip/CSV for (1) Brand vs non-brand analysis with revenue implications, (2) SEO opportunities with business impact sizing, (3) Content gap analysis with funnel mapping, (4) Paid search strategy with budget allocation, or (5) Strategic summaries with prioritized recommendations.
Proactively harden a cloud account or organization before an incident — prioritizing IAM and identity risk over checkbox findings, closing the exposures that become attack paths (public storage, over-broad roles, missing audit logging, unencrypted data), reading CSPM output critically, and enforcing guardrails at the org level. Use when reviewing a cloud environment's security posture, triaging a Prowler/ScoutSuite/Security Hub report, deciding which misconfigurations actually matter, or setting preventive controls across AWS, Azure, or GCP.
This skill provides guidance for implementing headless terminal interfaces that programmatically control shell sessions. Use this skill when implementing terminal emulation, pseudo-terminal wrappers, or interfaces like BaseTerminal that require sending keystrokes and reading output from shell processes.
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From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.