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Found 5,792 Skills
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.
Connect AI agents to your live Chrome session via CDP for real-time tab interaction, screenshots, and JS evaluation without re-login
Migrate a Java application from the classic Elastic APM Java agent to the EDOT Java agent. Use when switching from elastic-apm-agent.jar to elastic-otel-javaagent.jar.
Monitor LLMs and agentic apps: performance, token/cost, response quality, and workflow orchestration. Use when the user asks about LLM monitoring, GenAI observability, or AI cost/quality.
Audit and sync AI agent configuration files (CLAUDE.md, CODEX.md, AGENTS.md, .cursorrules, hooks, settings) across workspaces. Use when agent configs drift, rules duplicate, files go stale, or after workspace restructuring.
Use when building any system where email content triggers actions — AI agent inboxes, automated support handlers, email-to-task pipelines, or any workflow processing untrusted inbound email. Always use this skill when the user wants to receive emails and act on them programmatically, even if they don't mention "agent" — the skill contains critical security patterns (sender allowlists, content filtering, sandboxed processing) that prevent untrusted email from controlling your system.
Curate a Chinese reading digest from a fixed bundle of RSS and Atom feeds, with a strong preference for AI agent thinking, frontier AI commentary, deep interviews, and non-boring high-signal essays. Use when Codex needs to pull the latest week's posts by default, or a specific day's posts when explicitly requested, summarize them, score each article on a 10-point scale, and output only the posts scoring above 7 in a concise Chinese daily-brief style.
Enables agents to register, manage, and execute scheduled tasks using OS native scheduler (crontab for Linux/WSL, launchd for macOS). No git, no dangerous flags, no session dependency. Tasks run headless, output to log files, user reads when ready. Use this skill when: - User wants to schedule recurring tasks with natural language - User mentions "every day at", "cada hora", "schedule", "programar", "automatizar" - User needs tasks to run without open session (headless) - User wants OS-level scheduling (crontab/launchd) - User mentions "cada minuto durante la próxima hora" or temporal intervals ACTIVATE when user mentions: "schedule", "programar", "cron", "cada día", "every hour", "automate", "tarea programada", "ejecutar automáticamente", "recordatorio", "cada minuto durante", "durante la próxima", "intervalo", "task scheduler", "opencode headless", "kiro scheduled", "background task", "tarea en segundo plano" DO NOT USE for: git operations, dangerous permissions, MCP sampling dependency.
Guides the agent through authoring and validating agent skills. Use when creating new skill directories, tightening skill metadata, extracting supporting references, or preparing skillgrade evals. Do not use for general app documentation, generic README editing, or non-agentic library code.
Use this skill when working with A2UI (Agent-to-User Interface) - Google's open protocol for agent-driven declarative UIs. Triggers on tasks involving A2UI message generation, component catalogs, data binding, surface management, renderer development, custom components, or integrating A2UI with A2A Protocol, AG UI, or agent frameworks like Google ADK. Covers building agents that generate A2UI JSON, setting up client renderers (Lit, React, Angular, Flutter), creating custom catalogs, and handling client-to-server actions.
Professional prompt engineering, context engineering, and AI agent orchestration for coding agents (Claude Code, Codex, Cursor, Gemini CLI). Use when designing CLAUDE.md/AGENTS.md files, writing skills, planning multi-agent pipelines, optimizing token usage, managing session handoffs, or structuring any prompt for maximum agent performance. Do NOT use for general coding tasks or code review.
[Trigger] When PPT workflow needs SVG slide quality review via Gemini. [Output] Structured review assessment with scores, pass/fail, and fix suggestions. [Skip] For content authoring or SVG generation tasks (those are handled by Claude). [Ask] No user input needed; invoked by review-core agent. [Resource Usage] Use references/, scripts/ (`scripts/invoke-gemini-ppt.ts`).