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Found 2,167 Skills
Build, deploy, and secure Model Context Protocol (MCP) servers on Netlify. Use whenever the task involves creating an MCP server, exposing an app or API to AI agents as MCP tools, letting Claude / Cursor / Claude Code call a custom remote server, or adding MCP tools to an existing Netlify site. Covers the MCP SDK + Streamable HTTP transport on a Netlify Function, authentication (single shared secret vs per-user API keys with Netlify Identity), read/write safety, file uploads, and connecting clients. Use even when the user just says "MCP", "tool server for an agent", or "let an AI use my API".
Use when parallel agents share a codebase: adding new behavior, editing or resolving conflicts in shared files (dispatchers, registries, lockfiles), finding and splitting churn hotspots, or writing AGENTS.md, CLAUDE.md, or README.
Apple-authored SwiftUI and platform guidance extracted from Xcode. Helps AI agents write idiomatic, Apple-native SwiftUI with reduced hallucinations.
Setup Sentry AI Agent Monitoring in any project. Use when asked to monitor LLM calls, track AI agents, or instrument OpenAI/Anthropic/Vercel AI/LangChain/Google GenAI. Detects installed AI SDKs and configures appropriate integrations.
Build agents for legal document analysis, contract review, and compliance checking. Handles document parsing, risk identification, and legal research. Use when creating contract analysis tools, legal research assistants, compliance checkers, or document review systems.
This skill should be used when orchestrating multi-agent swarms using Claude Code's TeammateTool and Task system. It applies when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns.
Audits AGENTS.md and CLAUDE.md files using execution-first standards. Checks commands, gotchas, and signal-to-noise ratio. Use when asked to audit, review, score, refactor, or improve agent instruction files, fix stale commands, or reduce bloat.
Build autonomous game-playing agents using AI and reinforcement learning. Covers game environments, agent decision-making, strategy development, and performance optimization. Use when creating game-playing bots, testing game AI, strategic decision-making systems, or game theory applications.
Transform session learnings into permanent capabilities (skills, rules, agents). Use when asked to "improve setup", "learn from sessions", "compound learnings", or "what patterns should become skills".
Audits agent skill instructions and system prompts for vulnerabilities to prompt hijacking and indirect injection. Use when designing new agent skills or before deploying agents to public environments where users provide untrusted input.
Analyze a codebase to extract its conventions, patterns, and style. Spawns specialized analyzer agents that each focus on one aspect (structure, naming, patterns, testing, frontend). Generates a comprehensive style guide that other skills can reference. Use when starting work on an unfamiliar codebase, or to create explicit documentation of implicit conventions.
Create and maintain AGENTS.md / CLAUDE.md snippet indexes that route tasks to the correct dotnet-skills skills and agents (including compressed Vercel-style indexes).