发现 tools llm ai 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
Manual testing workflow for Claude HUD to verify core functionality. Use when asked to "test the app", "verify the app works", "run manual tests", "test after changes", or after implementing significant feature...
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 a...
Evaluate a codebase for agent-friendliness based on autonomous agent best practices. Use when asked to "evaluate for agents", "check agent readiness", "audit for autonomous execution", "assess agent-friendlines...
Guide for creating effective skills with UV + justfile structure. This skill should be used when users want to create a new skill (or update an existing skill) that extends the agent's capabilities with speci...
Authoritative templates and scaffolding for creating slash commands. This skill should be used when creating new commands, reviewing existing commands for template compliance, or understanding command structure...
Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx file...
Research scope too big? This skill decomposes large questions into independent parts, spawns parallel researchers, and synthesizes findings. Essential when comprehensive research would overflow a single conte...
Authoritative templates and scaffolding for creating agent system prompts (primary agents and subagents). This skill should be used when creating new agents, reviewing existing agent prompts for template compli...
Building chat interfaces in Streamlit. Use when creating conversational UIs, chatbots, or AI assistants. Covers st.chat_message, st.chat_input, message history, and streaming responses.
Building multi-page Streamlit apps. Use when creating apps with multiple pages, setting up navigation, or managing state across pages.
Documents Streamlit CLI commands for running apps, managing configuration, and diagnostics. Use when starting Streamlit apps, configuring runtime options, or troubleshooting CLI issues.
Build production-grade Streamlit apps. Used when creating, editing, debugging, or deploying Streamlit applications. Routes to specialized sub-skills for performance, layouts, design, data display, and more.
Guide for creating or updating Agent Skills using the standardized SKILL.md specification, including naming rules, optional frontmatter fields, validation, packaging, and best practices for structuring scripts,...
Import DevExpert testimonials from Google Sheets (gog) or pasted TSV lists, format text with line breaks, crop profile images to 400x400, copy them to src/assets/testimonials, update src/data/testimonials.json,...
Use for language design questions including syntax decisions, semantics, grammar authoring, and comparing DomainLang with other DSLs. Activate when designing new language features, evaluating syntax alternative...
This skill should be used when the user wants to "create a skill", "add a skill", "write a new skill", "create a skill for opencode", "create a skill for claude", "improve skill description", "organize skill co...
Configure or debug LLM blog post generation using Vercel AI SDK and Google Gemini. Use when updating blog generation prompts, fixing AI integration issues, or modifying content generation logic.
Generate type-safe AI content using Gemini structured output with Zod validation and Code Execution Tool. Use when building AI generation functions that need guaranteed output format.
Create effective AI skills. This skill should be used when user wants to create a new skill (or update an existing skill) that extends an AI with specialized capabilities, knowledge, workflows, or tool integrat...
LiteLLM-RS MCP Gateway Architecture. Covers Model Context Protocol, JSON-RPC 2.0 implementation, multi-transport support (HTTP, SSE, WebSocket, Stdio), and permission system.
LiteLLM-RS A2A Protocol Architecture. Covers Agent-to-Agent communication, JSON-RPC 2.0 messaging, multi-provider orchestration, agent registry, and task state management.
LiteLLM-RS Provider Architecture Guide. Covers 66+ provider integration, trait object design, unified error handling, connection pooling, and LLMProvider trait implementation patterns.
LiteLLM-RS Routing Architecture. Covers 7 routing strategies, lock-free design with DashMap, health-aware selection, fallback chains, and load balancing.
LiteLLM-RS Observability Architecture. Covers Prometheus metrics, OpenTelemetry tracing, structured logging, health checks, and alerting integration.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。