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Found 743 Skills
Get external agent review and feedback. Routes Anthropic models through Claude Agent SDK (uses local subscription) and other models through OpenRouter API. Use for code review, architecture feedback, or any external consultation.
AI Radar Skill — Zero-API, Zero-Key, Zero-Server Chinese AI News Query. Data comes from public static JSON files hosted on GitHub Pages by AI News Radar (automatically updated daily via GitHub Actions). You can retrieve data with curl, no authentication, no UA requirements, no rate limits, and you can fork the entire data pipeline to create your own version. This Skill should be triggered when users ask any Chinese AI news-related questions such as "What's happening in the AI circle today?", "AI news in the past 24 hours", "AI daily briefing", "Any recent large model releases?", "AI product updates", "What's new in Agent tools?", "What have OpenAI/Anthropic/Google released recently?", "Hot topics in the AI circle", "Check AI Radar", "Which AI news sources are worth following?", etc. Even if users only say "AI circle", "AI news", or "What's new today", as long as the context is in the AI / large model / Agent / developer tools domain, this Skill should be triggered. **Do NOT undertrigger** — if users ask for AI news and you don't invoke this Skill, you're treating outdated training data as today's news, which is harmful to users. Do NOT use this Skill for maintaining the AI News Radar repository itself (adding news sources, modifying crawling logic, deploying Pages — use Bole Skill / ai-news-radar for that); do NOT use it for non-AI general news queries; do NOT use it for private information sources that require login status.
Build AI-powered chat applications with TanStack AI and React. Use when working with @tanstack/ai, @tanstack/ai-react, @tanstack/ai-client, or any TanStack AI packages. Covers useChat hook, streaming, tools (server/client/hybrid), tool approval, structured outputs, multimodal content, adapters (OpenAI, Anthropic, Gemini, Ollama, Grok), agentic cycles, devtools, and type safety patterns. Triggers on AI chat UI, function calling, LLM integration, or streaming response tasks using TanStack AI.
Setup Spanora AI observability in any project (JavaScript/TypeScript or Python). Use when user asks to "add spanora", "setup spanora", "integrate spanora", "add AI observability", "monitor LLM calls with spanora", "track AI costs", or mentions spanora in the context of adding observability to their project. Detects the language and installed AI SDKs (Vercel AI, Anthropic, OpenAI, LangChain) and configures the optimal integration pattern.
BYOK — register a custom LLM endpoint (Anthropic, OpenAI, Qwen, DeepSeek, etc.) with your own API key
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 AI-powered Ruby applications with RubyLLM. Full lifecycle - chat, tools, streaming, Rails integration, embeddings, and production deployment. Covers all providers (OpenAI, Anthropic, Gemini, etc.) with one unified API.
INVOKE THIS SKILL when creating, reading, updating, or deleting Arize AI integrations. Covers listing integrations, creating integrations for any supported LLM provider (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM, custom), updating credentials or metadata, and deleting integrations using the ax CLI.
Build backend AI with Vercel AI SDK v6 stable. Covers Output API (replaces generateObject/streamObject), speech synthesis, transcription, embeddings, MCP tools with security guidance. Includes v4→v5 migration and 15 error solutions with workarounds. Use when: implementing AI SDK v5/v6, migrating versions, troubleshooting AI_APICallError, Workers startup issues, Output API errors, Gemini caching issues, Anthropic tool errors, MCP tools, or stream resumption failures.
Operational prompt engineering for production LLM apps: structured outputs (JSON/schema), deterministic extractors, RAG grounding/citations, tool/agent workflows, prompt safety (injection/exfiltration), and prompt evaluation/regression testing. Use when designing, debugging, or standardizing prompts for Codex CLI, Claude Code, and OpenAI/Anthropic/Gemini APIs.
Review AI API key leakage patterns and redaction strategies. Use for identifying exposed keys for OpenAI, Anthropic, Gemini, and 10+ other providers. Use proactively when code integrates AI providers or when environment variables/keys are present. Examples: - user: "Check for leaked OpenAI keys" → scan for `sk-` patterns and client-side exposure - user: "Is my Gemini integration secure?" → audit vertex AI config and key redaction - user: "Review AI provider logging" → ensure secrets are redacted from logs - user: "Scan for Anthropic secrets" → check for `ant-` keys in code and configs - user: "Audit Vertex AI integration" → verify proper IAM roles and service account usage
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AI Configs implementation in five stages: extract prompts, wrap in the AI SDK, add tools, add tracking, add evals/judges. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini) to a managed AI Config, or stage a full hardcoded-to-LaunchDarkly migration.