Loading...
Loading...
Found 717 Skills
Prompt engineering guidance for Claude (Anthropic) model. Use when crafting prompts for Claude to leverage XML-style tags, long-context capabilities, extended thinking, and strong instruction following.
Add new LLM model pricing entries to Langfuse's default-model-prices.json. Use when adding model prices, updating model pricing, creating model entries, adding Claude/OpenAI/Anthropic/Google/Gemini/AWS Bedrock/Azure/Vertex AI model pricing, working with matchPattern regex, pricingTiers, or model cost configuration. Covers model price JSON structure, regex patterns for multi-provider matching, tiered pricing with conditions, cache pricing, and validation rules.
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.
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.
Use when managing AI Hub account, API keys, balance, usage, or API endpoints. Use when user says "AI Hub", "add AI credits", "create API key", "check AI usage", "auto-recharge", "AI Hub endpoint", "AI Hub base URL", "how to use AI Hub API", "LLM API", "AI API", "OpenAI compatible", "Anthropic API", "GPT", "Claude", "Gemini", "DeepSeek", or "Grok" in the context of Zeabur.
Write a high-quality prompt for any LLM or AI assistant — Claude, Claude Code, ChatGPT, Gemini, Cursor, Windsurf, Copilot, or any coding / chat agent. Use this skill whenever the user asks to write, improve, refine, shorten, or rewrite a prompt; asks "how should I phrase this for [model]" or "what's a good prompt for [task]"; describes a task they want an AI to do but hasn't yet formulated it as a prompt; or pastes an existing prompt and asks for revision. Based on Boris's (Anthropic, Claude Code creator) prompt methodology — short and accurate prompts, plan-before-code, feedback loops, persistent context in files. The universal principles (short, plan-first, feedback-loop, no-padding) apply to any LLM; the Claude-Code-specific anchors (CLAUDE.md, @file, slash commands) only apply when the target is Claude Code. If the user's intent is unclear (target model, deliverable, scope, or whether the AI has a way to self-verify is missing), ask 1–3 targeted clarifying questions via AskUserQuestion before writing the prompt.
Converts any Claude Code skills repository into an official plugin marketplace. Analyzes existing skills, generates .claude-plugin/marketplace.json conforming to the Anthropic spec, validates with `claude plugin validate`, tests real installation, and creates a PR to the upstream repo. Encodes hard-won anti-patterns from real marketplace development (schema traps, version semantics, description pitfalls). Use when the user mentions: marketplace, plugin support, one-click install, marketplace.json, plugin distribution, auto-update, or wants a skills repo installable via `claude plugin install`. Also trigger when the user has a skills repo and asks about packaging, distribution, or making it installable.
Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker podcasts from research, chatting with documents using context-aware AI, searching across materials with full-text and vector search, or running custom content transformations. Supports 16+ AI providers including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral with complete data privacy through self-hosting.
Guide for implementing HolmesGPT - an AI agent for troubleshooting cloud-native environments. Use when investigating Kubernetes issues, analyzing alerts from Prometheus/AlertManager/PagerDuty, performing root cause analysis, configuring HolmesGPT installations (CLI/Helm/Docker), setting up AI providers (OpenAI/Anthropic/Azure), creating custom toolsets, or integrating with observability platforms (Grafana, Loki, Tempo, DataDog).
Validate and optimize CLAUDE.md files using Anthropic's best practices for focused sessions. Detects contradictions, redundancy, excessive length (200+ lines), emphasis overuse (2%+ density), broken links, and orphaned sections. Scores health 0-50 points. Suggests safe automated fixes and extraction opportunities. Use when editing CLAUDE.md, before commits, when document grows past 200 lines, user says "optimize CLAUDE.md", "check contradictions", "validate documentation", or during quarterly reviews. Works with project and global CLAUDE.md files (.md extension). Based on Anthropic 2025 context engineering best practices.
Feature-complete companion for the actual CLI, an ADR-powered CLAUDE.md/AGENTS.md generator. Runs and troubleshoots actual adr-bot, status, auth, config, runners, and models. Covers all 5 runners (claude-cli, anthropic-api, openai-api, codex-cli, cursor-cli), all model patterns, all 3 output formats (claude-md, agents-md, cursor-rules), and all error types. Use when working with the actual CLI, running actual adr-bot, configuring runners or models, troubleshooting errors, or managing output files.
Build and deploy autonomous AI agents in-process using Open Agent SDK, an open-source alternative to @anthropic-ai/claude-agent-sdk that works anywhere without CLI dependencies.