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Found 1,672 Skills
Detect and neutralize prompt injection attacks in OpenClaw skill content, user inputs, and external data sources. Prevents instruction hijacking and context manipulation.
Anti-detect browser automation CLI for AI agents. Use when the user needs to interact with websites with bot detection, CAPTCHAs, or anti-bot blocks, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task that requires bypassing fingerprint checks.
Modern Python 3.12+ patterns your AI agent should use. Type hints, async/await, Pydantic v2, uv, match statements, and project structure.
Strengthen a raw user prompt into an execution-ready instruction set for Amp, Claude Code, or another AI agent. Use when the user wants to improve an existing prompt, build a reusable prompting framework, wrap the current request with better structure, add clearer tool rules, or create a hook that upgrades prompts before execution.
Interact with GitLab via the glab CLI. Primary use case is MR review — fetches the diff, runs parallel code review + security review via specialist agents, then posts the result as a Thai comment on the MR. Also supports listing MRs, viewing MR status, checking CI/CD pipelines, approving MRs, and other glab operations. Trigger whenever the user provides a GitLab MR URL or says anything like "review MR", "ช่วย review MR นี้", "ดู MR ให้หน่อย", "review https://gitlab.../merge_requests/42", "check pipeline", "list open MRs", or any GitLab-related task.
Audit Claude Code configuration health across all layers (CLAUDE.md, rules, skills, hooks, MCP). Run periodically or when collaboration feels off.
Use when starting work on any project to produce or update living documentation (TechStack.md, ProjectStructure.md) that bootstraps context for any AI agent session. Run before any feature work, or periodically to keep docs current.
Use when an AI agent should run protocols or workflow tests against kairos-dev (KAIROS MCP in this repo's dev environment). Covers AI–MCP integration and workflow-test flows; MCP-only, reports/ output.
Monitors context window health throughout a session and rides peak context quality for maximum output fidelity. Activates automatically after plan-interview and intent-framed-agent. Stays active through execution and hands off cleanly to simplify-and-harden and self-improvement when the wave completes naturally or exits via handoff. Use this skill whenever a multi-step agent task is underway and session continuity or context drift is a concern. Especially important for long-running tasks, complex refactors, or any work where degraded context would silently corrupt the output. Trigger even if the user doesn't say "context surfing" — if an agent task is running across multiple steps with intent and a plan already established, this skill is live.
Autonomous AI Project Agent & Cron Task Runner. Orchestrates repetitive AI-driven engineering tasks with state persistence (Memory) and advanced workflow controls.
This skill should be used when the user asks to "build an AI agent with Claude", "use the Claude Agent SDK", "integrate claude-agent-sdk into a project", "set up an autonomous agent with tools", or needs guidance on the Anthropic Claude Agent SDK best practices for Python and TypeScript.
Expert guidance for building production-grade AI agents and workflows using Pydantic AI (the `pydantic_ai` Python library). Use this skill whenever the user is: writing, debugging, or reviewing any Pydantic AI code; asking how to build AI agents in Python with Pydantic; asking about Agent, RunContext, tools, dependencies, structured outputs, streaming, multi-agent patterns, MCP integration, or testing with Pydantic AI; or migrating from LangChain/LlamaIndex to Pydantic AI. Trigger even for vague requests like "help me build an AI agent in Python" or "how do I add tools to my LLM app" — Pydantic AI is very likely what they need.