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Found 10,160 Skills
Reusable template for authoring new Agent Skills with clear triggers, workflow, and I/O contracts.
Ethereum development knowledge for AI agents — from idea to deployed dApp. Fetch real-time docs on gas costs, Solidity patterns, Scaffold-ETH 2, Layer 2s, DeFi composability, security, testing, and production deployment. Use when: (1) building any Ethereum or EVM dApp, (2) writing or reviewing Solidity contracts, (3) deploying to mainnet or L2s, (4) the user asks about gas, tokens, wallets, or smart contracts, (5) any web3/blockchain/onchain development task. NOT for: trading, price checking, or portfolio management — use a trading skill for those.
The complete AI web agency toolkit. One skill to run a full client website project — from intake to design to build to deploy. Orchestrates sub-skills and sub-agents for fast, high-quality delivery.
Create, synthesize, and iteratively improve agent skills following the Agent Skills specification. Use when asked to "create a skill", "write a skill", "synthesize sources into a skill", "improve a skill from positive/negative examples", "update a skill", or "maintain skill docs and registration". Handles source capture, depth gates, authoring, registration, and validation.
Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.
Autonomous workflow execution pipeline with CSV wave engine. Session discovery → plan validation → IMPL-*.json → CSV conversion → wave execution via spawn_agents_on_csv → results sync. Task JSONs remain the rich data source; CSV is brief + execution state.
Modern TypeScript patterns your AI agent should use. Strict mode, discriminated unions, satisfies operator, const assertions, and type-safe patterns for TypeScript 5.x.
25 advanced POWERFUL-tier engineering skills covering agent design, RAG architecture, MCP servers, CI/CD pipelines, database design, observability, security auditing, release management, and platform operations. Works with Claude Code, Codex CLI, and OpenClaw.
This skill is used when the user requests 'review my prompt', 'analyze my conversation history', 'diagnose my understanding level', or when it is invoked via /prompt-review. It reads past AI Agent conversation histories (Claude Code, GitHub Copilot Chat, Cline, Roo Code, Windsurf, Antigravity), estimates the user's technical understanding level, prompting patterns and AI dependency, then generates a corresponding report.
Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.
Perform exhaustive code reviews using multi-agent analysis, ultra-thinking, and worktrees
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).