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Found 6,779 Skills
Run metric-driven iterative optimization loops. Define a measurable goal, build measurement scaffolding, then run parallel experiments that try many approaches, measure each against hard gates and/or LLM-as-judge quality scores, keep improvements, and converge toward the best solution. Use when optimizing clustering quality, search relevance, build performance, prompt quality, or any measurable outcome that benefits from systematic experimentation. Inspired by Karpathy's autoresearch, generalized for multi-file code changes and non-ML domains.
Use when building features with **Claude Code** in any codebase and the work should go through a disciplined build → review → test → fix loop. Triggers on "run the build loop", "build the next task", "continue the plan", "build this feature properly", or any request to implement work from a plan file or a direct feature prompt. Builds from the plan (or the prompt if no plan exists), runs Claude Code's `/review` (plus `/security-review` for sensitive surfaces) and fixes every issue found, tests and verifies the feature end to end, fixes anything testing surfaces, and reports back once complete. Repeats until all plan tasks are checked off.
Systematically evaluate completed short stories or novel chapters to identify strengths, weaknesses, and improvement opportunities. Use after drafting to assess whether the piece achieves its narrative goals.
AI-powered crypto trading agent via natural language. Use when the user wants to trade crypto (buy/sell/swap tokens), check portfolio balances, view token prices, transfer crypto, manage NFTs, use leverage, bet on Polymarket, deploy tokens, set up automated trading strategies, submit raw transactions, execute calldata, or send transaction JSON. Supports Base, Ethereum, Polygon, Solana, and Unichain. Comprehensive capabilities include trading, portfolio management, market research, NFT operations, prediction markets, leverage trading, DeFi operations, automation, and arbitrary transaction submission.
Multi-language code quality standards and review for TypeScript, Python, Go, and Rust. Enforces type safety, security, performance, and maintainability. Use when writing, reviewing, or refactoring code. Includes review process, checklist, and Python PEP 8 deep-dive.
This skill should be used whenever users ask food-related questions, meal suggestions, nutrition advice, recipe recommendations, or dietary planning. On first use, the skill collects comprehensive user preferences (allergies, dietary restrictions, goals, likes/dislikes) and stores them in a persistent database. All subsequent food-related responses are personalized based on these stored preferences.
Set up and maintain basic bookkeeping for a solopreneur business. Use when tracking income and expenses, preparing for taxes, managing invoices and receipts, understanding cash flow, or generating financial reports. Covers accounting software selection, chart of accounts, expense categorization, reconciliation, and financial statements. Not professional accounting advice — consult a CPA for complex situations. Trigger on "bookkeeping", "accounting", "track expenses", "financial records", "QuickBooks", "invoicing", "receipts", "profit and loss".
Use to stress-test predictions by assuming they failed and working backward to identify why. Invoke when confidence is high (>80% or <20%), need to identify tail risks and unknown unknowns, or want to widen overconfident intervals. Use when user mentions premortem, backcasting, what could go wrong, stress test, or black swans.
Find which of a GitHub repository's dependencies are sponsorable via GitHub Sponsors. Uses deps.dev API for dependency resolution across npm, PyPI, Cargo, Go, RubyGems, Maven, and NuGet. Checks npm funding metadata, FUNDING.yml files, and web search. Verifies every link. Shows direct and transitive dependencies with OSSF Scorecard health data. Invoke with /sponsor followed by a GitHub owner/repo (e.g. "/sponsor expressjs/express").
Phase 1 of the feature workflow — Draft a design document for the new feature, which serves as the sole input for subsequent implementation and acceptance. First gather evidence (read architecture docs, review relevant code, grep to prevent term conflicts, check archives), then write a complete first draft in one go (including YAML frontmatter + three-layer structure + test design), submit it to the user for overall review, and iterate until approval. After approval, extract {slug}-checklist.yaml from {slug}-design.md for use in the next two phases. Trigger scenarios: "Start designing the solution", "Write design doc", "Prepare to implement XX", with the prerequisite that you already know what to do, who it's for, and how to define success.
Airbnb-DLS-aligned design system engineering for Expo / React Native apps targeting both web and native iOS, built on Unistyles v3, Reanimated, Skia, and FlashList. Use whenever building, reviewing, or refactoring shared UI — design tokens, theming, variant-driven component APIs, typography, spacing, cross-platform web/iOS parity, native-feel performance, or complex surfaces like calendars and drawing canvases (examples use a clinic app). Covers token architecture, theming, component API contracts (variants over style props), web/iOS parity (Unistyles `_web` hover/focus/cursor, Platform splits, one shared theme), the Unistyles styling engine, and governance. Trigger even when the user does not say "design system" but is creating or changing reusable React Native components, tokens, theme code, or making a component behave natively on both web and iOS. Teaches how to BUILD the design system; pair with expo-react-native-coder for features and expo-ios-hig for iOS native-feel decisions.
Analyze code for logic bugs, error handling issues, and edge cases. Detects off-by-one errors, null handling, race conditions, and incorrect error paths. Use when reviewing core business logic or complex algorithms.