Skills
99,448 packaged capabilities and playbooks for agents. Install any of them with one command.
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Turn raw footage into polished, share-ready clips without downloading a single app. This free-video-trimmer-online skill lets you cut unwanted sections, isolate the best moments, and export clean video segments directly from your browser. Supports mp4, mov, avi, webm, and mkv formats. Perfect for content creators, students, and social media users who need fast, no-cost trimming without watermarks or paywalls.
Implement or extend a user-facing workflow in a web application, integrating with existing backend APIs. Use when the feature is primarily a UI/UX change backed by existing APIs, affects only the web frontend, and requires following design system, routing, and testing conventions.
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **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 R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, HonestDiD), (7) further analysis (interactions + marginaleffects, mediation::mediate, gsem via lavaan, dose-response splines, grf CATE), (8) publication-ready tables & figures (modelsummary, kableExtra, gt, stargazer, texreg, flextable to LaTeX/Word/HTML; ggplot2 + ggpubr + cowplot + binsreg + iplot for figures). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `WeightIt` / `gfoRmula` / `tmle` / `ltmle`, Mendelian randomization via `MendelianRandomization` / `TwoSampleMR` / `MRPRESSO`, KM / Cox / AFT / RMST survival via `survival` / `survminer` / `flexsurv`, E-value sensitivity via `EValue`, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `DoubleML`, S/T/X/R/DR meta-learners via `causalweight` / `grf`, causal forest via `grf::causal_forest`, BART/BCF via `bartCause` / `bcf`, matrix completion via `MCPanel`, CATE distribution + policy tree via `policytree`, off-policy evaluation, conformal causal via `conformalInference` / `cfcausal`, fairness audit via `fairmodels`, DAG learning via `pcalg` / `bnlearn` / LLM-assisted). Use when the user asks for a complete R empirical analysis, wants a tidyverse-style reproducible R script / Quarto workflow, prefers fixest over reghdfe, needs the R counterpart to StatsPAI / 00.1 / 00.2, or names a specific R step in isolation ("feols with cluster", "MatchIt nearest neighbor", "bacondecomp in R", "gtsummary table 1", "modelsummary to Word"). Mode A triggers on "target trial emulation R", "tmle ltmle", "MendelianRandomization", "TwoSampleMR", "MRPRESSO", "survival cox AFT", "STROBE R", "EValue R", "公共健康 R", "流行病学 R". Mode B triggers on "DoubleML R", "grf causal forest", "policytree", "bartCause bcf", "conformal causal R", "fairmodels", "pcalg NOTEARS", "因果机器学习 R".
A global integrated development and collaboration workflow skill covering the full chain of requirement assessment, development, testing, quality, documentation, commit and release, able to integrate all basic atomic skills and automate the PDTFC+ loop with optimized division of labour and collaboration.
Game feel consulting based on Steve Swink's "Game Feel: A Game Designer's Guide to Virtual Sensation" (Morgan Kaufmann, 2009). Use when helping with control responsiveness, input mapping, physics tuning, avatar movement, jump mechanics, polish effects, or analyzing why controls feel "floaty", "tight", "responsive", or "sluggish". Covers the psychology of real-time control, metrics for measuring feel, and practical tuning guidance. NOT for general game design - focused specifically on the tactile sensation of controlling virtual objects.
Generates commit messages following the gitmoji convention (https://gitmoji.dev) — picks the right emoji for the intent of the change and writes a well-formed message. Use when asked to "write a gitmoji commit", "add an emoji to my commit message", "which gitmoji should I use", "gitmoji this change", or when a project uses gitmoji-style commit messages. Works from a git diff, staged changes, or a plain description of the change. Generates the message only — does not run git commands.
GitOps deployment workflows with ArgoCD and Flux. Use this skill whenever the user mentions GitOps, ArgoCD, Flux, Flagger, Argo Rollouts, or continuous deployment to Kubernetes. Triggers include setting up ArgoCD or Flux from scratch, designing Git repository structures (monorepo vs polyrepo, app-of-apps), deploying to multiple clusters with ApplicationSets, managing secrets in Git (SOPS, Sealed Secrets, External Secrets Operator), implementing canary or blue-green deployments, troubleshooting sync or reconciliation issues, working with OCI artifacts, and comparing ArgoCD vs Flux.
Design and refactor Go service architecture for DDD-style systems. Use when a Go codebase needs explicit ports/app/domain/adapters boundaries, dependency rules, interface placement, composition-root dependency injection, separate transport and database models, or help deciding whether DDD, Clean Architecture, or CQRS are justified.
Build RAG systems, semantic search, and document clustering with Gemini embeddings API (gemini-embedding-001). Generate 768-3072 dimension embeddings for vector search, integrate with Cloudflare Vectorize, and use 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY) for optimized retrieval. Use when: implementing vector search with Google embeddings, building retrieval-augmented generation systems, creating semantic search features, clustering documents by meaning, integrating embeddings with Cloudflare Vectorize, optimizing dimension sizes (128-3072), or troubleshooting dimension mismatch errors, incorrect task type selections, rate limit issues (100 RPM free tier), vector normalization mistakes, or text truncation errors (2,048 token limit).
Generate videos with Google Veo models via inference.sh CLI. Models: Veo 3.1, Veo 3.1 Fast, Veo 3, Veo 3 Fast, Veo 2. Capabilities: text-to-video, cinematic output, high quality video generation. Triggers: veo, google veo, veo 3, veo 2, veo 3.1, vertex ai video, google video generation, google video ai, veo model, veo video
Apply the Fama-French three-factor model to decompose asset returns into market, size, and value factors. Use this skill when the user needs to explain cross-sectional return differences, evaluate fund performance beyond CAPM alpha, assess small-cap or value tilts in a portfolio, or when they ask 'why do small caps earn more', 'is value premium real', or 'what factors drive returns'.
Apply Grounded Theory (Glaser and Strauss) to build theory inductively from qualitative data through open, axial, and selective coding. Use this skill when the user needs to develop new theory from data rather than test existing hypotheses, conduct theoretical sampling and constant comparison, determine when theoretical saturation is reached, or when they ask 'what theory explains this phenomenon', 'how do I code qualitative data systematically', or 'when do I stop collecting data'.
Long-form writing for WeChat official accounts (1500–4000 characters). First diagnose what the author already has (an idea / a pile of material / half a draft / an outline / a finished draft they are unhappy with), then route to one of six corresponding writing approaches, produce a finished draft and run it through a quality check specific to WeChat official accounts. Triggers when the user says "write a WeChat official account post", "help me write an article", "how should I write about this topic", "I have a pile of material but don't know how to organize it", "help me expand this", "revise this piece", "I'm stuck", "long-form article", "in-depth article".
Use when users ask for a hand-drawn or illustrated image prompt, name one of the repository's 22 numbered styles or the 3.1 stable variant, or mention triggers such as 亲子手绘、家庭蜡笔画、蜡笔、吉卜力、水墨、像素、淡彩速写、动画概念、水粉、北欧绘本、纸雕、软胶潮玩、墨线绘本、暖色扁平绘本、圆头红线、暖黄墨线、family-crayon-card、warm-flat-storybook、roundhead-redline、warm-yellow-ink-story、朱墨写意、朱墨水墨漫画、vermilion-ink-wash、ink-comic-transfer、皮影、皮影彩染、shadow-puppet、backlit-puppet. Produces prompt text or a formal reference-bearing call bundle and does not generate images.
Audit plugin skills, commands, and agents for agentic output optimization compliance such as missing Agentic Optimizations tables, bare CLI commands without compact/JSON flags, and stale review dates. Use when the user wants to batch-check skill documentation quality, find skills lacking compact output flags, enforce agentic optimization standards across the repo, or identify skills with `modified` dates older than 90 days.
Design and audit help center, documentation, support knowledge base, FAQ, troubleshooting, refund/cancellation guides, onboarding docs, developer docs, game support articles, and self-service support architecture. Use when reducing support load, improving customer success, structuring support content, or creating help content tied to product flows.
When the user is considering whether to sell a position, evaluating a holding period, or struggling with "when to sell." Language signals: "when should I sell" / "how long is the right holding period" / "it's already up, should I sell" / "when should I take profit." Not applicable to: sell decisions for short-term trades or speculative positions.
Huashu Design — Create high-fidelity prototypes, slides, animations, visualizations, and expert reviews with HTML. For any new design, always produce three initial directions for the user to choose from before proceeding (this also applies when a style or brand is specified). Proceed only after a direction is selected. Triggers: create a prototype, PPT, slides, animation, design style, review, make an HTML page, UI mockup, export MP4/GIF, make it look good. Not suitable for production-grade web apps or systems requiring a backend.
Observability no-data diagnosis skill for Huawei Cloud AgentArts agent-ops platform. Systematically diagnoses missing metrics, logs, and traces/sessions in agent-ops by checking subscription metadata, data pipeline (APM/AOM/LTS), query parameters, and authentication. Generates root-cause analysis and fix recommendations. Triggers include: "AgentArts no data", "metrics no data", "logs no data", "traces no data", "session no data", "obs diagnosis", "可观测诊断", "无数据诊断", "查不到指标", "查不到日志", "调用链无数据", "agent-ops 无数据"
Explain machine learning, deep learning, generative AI, LLMs, AI agents, and speech modeling in a Hung-Yi Lee-inspired teaching style. Use this skill when the user wants 李宏毅式教學: roadmap-first structure, intuition before math, black-box-to-mechanism explanations, everyday analogies, anticipating student confusion, practical debugging, and research-grounded context.
Generates and customizes Ignite UI for Angular themes including color palettes, typography, elevations, and component-level styles using the Sass theming system and the igniteui-theming MCP server. WHEN TO USE: users ask to theme, restyle, or style Ignite UI components, change colors or the color palette, switch between light and dark themes, create or apply a global theme, customize typography or elevation shadows, adjust spacing, sizing, or roundness, or configure per-component design tokens. WHEN NOT TO USE: component behavior, APIs, or data binding (use igniteui-angular-components or igniteui-angular-grids); building whole views from a design image or Figma file (use igniteui-angular-generate-from-image-design or igniteui-angular-figma-to-app).
convert images into animated video clips with this skill. Works with JPG, PNG, WEBP, HEIC files up to 200MB. social media creators use it for turning still photos into shareable videos for social media — processing takes 30-60 seconds on cloud GPUs and you get 1080p MP4 files.
New to MCP?
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.