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Found 1,378 Skills
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
This skill should be used when the user asks to "use marimo", "create a marimo notebook", "debug a marimo notebook", "inspect cells", "understand reactive execution", "fix marimo errors", "convert from jupyter to marimo", or works with marimo reactive Python notebooks.
REST API for cross-chain and same-chain token swaps, bridging, and DeFi operations. USE THIS SKILL WHEN USER WANTS TO: - Swap tokens between different blockchains (e.g., "swap USDC on Ethereum to ETH on Arbitrum") - Bridge tokens to another chain (e.g., "move my ETH from mainnet to Optimism") - Swap tokens on the same chain with best rates (e.g., "swap ETH to USDC on Polygon") - Find the best route or quote for a token swap across chains - Execute DeFi operations across chains (zap, bridge+swap+deposit, yield farming entry) - Build multi-chain payment flows (accept any token, settle in specific token) - Check supported chains, tokens, or bridges for cross-chain transfers - Track status of a cross-chain transaction - Build backend services (Python, Go, Rust, etc.) that need cross-chain swaps - Integrate cross-chain functionality via HTTP/REST (not JavaScript SDK)
Analyze CSV files, generate summary statistics, and create visualizations using Python and pandas. Use when the user uploads, attaches, or references a CSV file, asks to summarize or analyze tabular data, requests insights from CSV data, or wants to understand data structure and quality.
Remove code comments via natural language guidance, suitable for simple scenarios in languages such as Python, JavaScript, TypeScript (.ts/.tsx), Java, C/C++, Go, HTML, etc.
Design and generate property-based tests (PBT) for changed files in the current git branch. Extracts specifications, designs properties (invariants, round-trip, idempotence, metamorphic, monotonicity, reference model), builds generator strategies, implements tests, and self-scores against a rubric (24/30+ required). Supports fast-check (TS/JS), hypothesis (Python), and proptest (Rust). Use when: (1) "write property tests for my changes", (2) "add PBT", (3) "property-based test", (4) after implementing pure functions, validators, parsers, or formatters to verify invariants.
Manage and troubleshoot PATH configuration in zsh. Use when adding tools to PATH (bun, nvm, Python venv, cargo, go), diagnosing "command not found" errors, validating PATH entries, or organizing shell configuration in .zshrc and .zshrc.local files.
Use when starting a new session without feature-list.json, setting up project structure, or breaking down requirements into atomic features. Load in INIT state. Detects project type (Python/Node/Django/FastAPI), creates feature-list.json with priorities, initializes .claude/progress/ tracking.
Securely execute untrusted Python, Node.js, Bun, Deno, and Bash code in sandboxed Docker containers.
Use this skill when you need to test or evaluate LangGraph/LangChain agents: writing unit or integration tests, generating test scaffolds, mocking LLM/tool behavior, running trajectory evaluation (match or LLM-as-judge), running LangSmith dataset evaluations, and comparing two agent versions with A/B-style offline analysis. Use it for Python and JavaScript/TypeScript workflows, evaluator design, experiment setup, regression gates, and debugging flaky/incorrect evaluation results.
Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the `deepagents` package. Use when users need to create agents with built-in planning/filesystem/subagents, configure middleware/backends/checkpointing/HITL, migrate from `create_react_agent` or `create_agent`, scaffold projects with repo scripts, validate agent config files, and confirm compatibility with current LangChain/LangGraph/LangSmith docs.
Primary tool for all code navigation and reading in supported languages (Rust, Python, TypeScript, JavaScript, Go). Use instead of Read, Grep, and Glob for finding symbols, reading function implementations, tracing callers, discovering tests, and understanding execution paths. Provides tree-sitter-backed indexing that returns exact source code — full function bodies, call sites with line numbers, test locations — without loading entire files into context. Use for: finding functions by name or pattern, reading specific implementations, answering 'what calls X', 'where does this error come from', 'how does X work', tracing from entrypoint to outcome, and any codebase exploration. Use Read only for config files, markdown, and unsupported languages.