Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
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Create, set up, or update the personal context portfolio: structured markdown files describing who you are, how you work, your teams, and your tool/ADO configuration. Runs the interview workflow for first-time setup and targeted edits for updates. Trigger this skill when the user asks to: set up their context, create or update their context portfolio, "create my IQ", "set up my IQ", edit their profile, add/remove a stakeholder, update ADO config, change team info, update pillars, or set up any plugin configuration. Trigger when another skill fails to find context (missing files or TODO markers) and needs context populated. Also trigger when the user mentions a context change in passing (e.g., "my manager changed", "we added someone to the team") to offer a context file update. Do NOT trigger for read-only questions like "who's on my team?" or "what's my ADO config?". Those are answered directly from the context files referenced in the loaded custom instructions; no skill is needed.
Integrates ShaderGradient moving 3D gradients in React/Next.js apps with safe defaults and performance-aware setup. Use when users mention shadergradient, animated gradients, GLSL backgrounds, @shadergradient/react, or gradient hero backgrounds.
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
Execute an accepted engineering plan through implementation, integrated validation, code review, pull request creation, active review monitoring, and verified closeout. Use after Kickoff approval/review or when the user explicitly asks to ship an existing plan.
Used to hide or restore skills; supports single-skill or batch operations; manages skill parent folder paths; prevents hiding itself; supports multi-directory search and operating on a specified directory; supports one-click hide/restore of all skills. Use this skill when the user needs to [hide or restore skills].
Use this skill to audit, review, or validate Claude Code skills (.md files in .claude/commands/). Invoke when user wants to check skill quality, cross-platform compatibility, cross-agent compatibility, prerequisite declarations, or description triggering accuracy. Triggers on: 审查 skill, review skill, 检查 skill 质量, skill 兼容性检查, validate skill, audit skill. This skill delegates structure validation to validate.sh, content quality to skill-creator, and handles compatibility auditing itself. Do NOT use for general code review, reviewing PRs, or reviewing CLAUDE.md.
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
Conduct Exploratory Data Analysis (EDA) using descriptive statistics, visualizations, and data quality checks. Use this skill when the user has a dataset and needs to understand its structure, find patterns, detect anomalies, or prepare data for further analysis — even if they say 'what does this data look like', 'find interesting patterns', 'clean this data', or 'summarize this dataset'.
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
Apply Philip Fisher's growth stock investment framework from Common Stocks and Uncommon Profits. Use this skill whenever a user wants to evaluate whether a company is a worthy long-term growth investment, assess management quality, gather competitive intelligence via scuttlebutt, decide when to buy an outstanding stock at the right moment, or decide whether to hold or sell a current position. Covers the 15 Points checklist, management depth analysis, scuttlebutt intelligence gathering, entry timing, and hold/sell decision logic. Trigger phrases: "is X a growth stock", "Fisher framework", "15 points", "scuttlebutt", "should I buy X", "should I sell X", "management quality of X", "growth stock analysis".
Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.
Profile a new tabular dataset before modeling. Find target leakage, missing data patterns, high-cardinality categoricals, near-constant features, redundant pairs, and non-linear relationships that Pearson correlation misses. Use whenever the user hands you a CSV or parquet and asks "what should I do with this?" Always run this skill before training any model on data you haven't seen before.
Unified task execution protocol for Codex-only work. Supports Single Task, Epic Task, and Batch Task while preserving CSV truth-source, validation gates, context recovery, and Debug-First failure exposure. WHEN TO USE: user asks to "track tasks", "create todo list", "make a plan", "track progress", "long task", "big project", "build from scratch", "autonomous session", "跟踪任务", "自主执行", "长时任务", "从零开始", "任务管理", "做个计划", "大工程", or when a task clearly requires 3+ ordered steps that produce file changes. DO NOT USE: single-step fixes, pure Q&A, code review, explaining code, search/research tasks, tasks with fewer than 3 steps, or tasks that do not produce file changes.
Provides comprehensive guidance for generating teaching resources including courseware, exercises, case studies, and learning guides. Use when the user asks about generating teaching materials, creating courseware, designing exercises, or developing educational content.
Use when integrating or debugging tiun — the web SDK (@tiun/sdk) or the React Native SDK (@tiun/react-native-sdk) — for authentication, subscription billing, one-time purchases, and time-based paywalls. Triggers on imports of `@tiun/sdk` or `@tiun/react-native-sdk`, calls like `tiun.init`, `tiun.checkout`, `tiun.login`, `tiun.start`, `TiunProvider`, `useTiun`, `useTiunEvent`, or questions about tiun products, entitlements, one-time purchases, lifetime access, fixed fees, time-based sessions, checkout return deep links, or server-side verification.
Stretch Claude Code context. Use when sessions are running long, hitting context/usage limits, working through large repos, doing multi-day work, or burning through tokens faster than expected. Apply these habits proactively on any non-trivial task.
Handles TOML configuration file operations in Python using tomlkit for comment-preserving read-modify-write cycles. Use when reading or writing pyproject.toml or any .toml config file, selecting between tomlkit and tomllib, modifying TOML while preserving comments and whitespace, implementing atomic config file updates, integrating TOML with Python dataclasses, handling TOML parse errors, or applying XDG base directory patterns for config file locations.
PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.
TRIZ systematic innovation methodology with AI-enhanced prompts. Use when: (1) Technical contradiction - improve A but B worsens, (2) Physical contradiction - need opposite properties, (3) Cross-industry solutions via FOS/MOS, (4) Technology evolution prediction, (5) Complex engineering problems. Triggers: "TRIZ", "contradiction", "inventive", "trade-off", "improve without worsening", "ข้อขัดแย้งทางเทคนิค", "innovation breakthrough"
Attempts ONE alternative fix for a bug, tests it empirically, and reports results. ALWAYS explores a DIFFERENT approach from existing PR fixes. Use when CI or an agent needs to try independent fix alternatives. Invoke with problem description, test command, target files, and optional hints.
Design readable advanced TypeScript types. Use when creating reusable type utilities, generics, conditional types, mapped types, template literal types, infer helpers, branded types, type guards, assertion functions, typed libraries, API clients, configs, or state helpers. Prefer simple types until complexity pays off. For broad code-quality typing guidance use typescript-best-practices.
TypeScript best practices and patterns for writing type-safe, maintainable code. Use when working with TypeScript files, configuring tsconfig, defining interfaces/types, implementing error handling, writing generics, or setting up type-safe communication patterns. Includes patterns for discriminated unions, type guards, utility types, and more.
Use when assessing TYPO3 extension quality, conformance checking, standards compliance, modernization to v12/v13/v14 (v14.3 LTS is the default/gold standard), TER readiness, or best practices review. Also triggers on: extension audit, quality score, full assessment, fix all findings, conformance audit, Fluid 5 strict ViewHelpers, ext_tables.php removal, Extbase attributes (Authorize/RateLimit), HashService removal, Bootstrap 5 migration, CSP compliance, ViewHelper security, XLIFF hygiene, PHP 8.4/8.5 compat.
Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics.
Provides comprehensive guidance for uView Pro Vue 3 component library including components, tools, layouts, and templates. Use when the user asks about uView Pro, needs to build Vue 3 applications with uView Pro, or implement mobile-first UI components.
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.
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.