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Found 304 Skills
imagine is a multi-provider command-line tool for generating and editing images via Google Gemini, Google Vertex AI, and OpenAI (gpt-image-2).
Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
Char (formerly Hyprnote) platform help — open-source, bot-free, local-first AI meeting notepad with system audio capture, markdown output, plugin SDK, and optional cloud STT/LLM (GPL-3.0). Use when setting up Char on macOS for the first time, speaker identification not working in group meetings, configuring local-only transcription with Cactus or Ollama for full offline use, choosing between Char's cloud STT providers (Deepgram, AssemblyAI, Soniox, OpenAI, etc.), app not launching or bouncing on dock without opening, telemetry concerns with PostHog or Sentry in a local-first app, building a Char plugin or using the automation hooks system, comparing Char to Granola or Meetily or Fathom for privacy, or configuring the CLI for template management. Do NOT use for picking between note-takers generally (use /sales-note-taker) or reviewing a single call for coaching (use /sales-call-review).
Score and compare images using vision LLMs as judges. YAML-defined criteria presets for 11 use cases (text-to-image, photorealism, document OCR, charts, UI, portrait, product, scientific, invoice, alt-text, artistic style). Supports OpenAI, Anthropic, Gemini, Mistral, and OpenRouter as judge providers. Keys auto-decrypted via SOPS + age.
Build RAG pipelines with Exa.ai for real-time web retrieval. Use when building retrieval-augmented generation, integrating Exa with LangChain, LlamaIndex, Vercel AI SDK, or implementing AI agents with web search capabilities. Triggers on: RAG pipeline, retrieval augmented generation, Exa LangChain, Exa LlamaIndex, ExaSearchRetriever, ExaSearchResults, Exa MCP, Exa tool calling, Claude tool use, AI agent web search, grounded generation, citation generation, fact checking, hallucination detection, OpenAI compatibility, chat completions.
Use PAL MCP to orchestrate multiple AI models (Gemini, OpenAI, Grok, Ollama) for code reviews, debugging, planning, and CLI bridging
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
Guides Claude from idea to working prototype using frameworks from OpenAI, Figma, and Airbnb. Use when starting new product features, planning MVP scope, making build-vs-buy decisions, or guiding users from concept to shippable prototype. Applies AI-first thinking (Kevin Weil), simplicity forcing functions (Dylan Field), and complete experience design (Brian Chesky).
Access and interact with Large Language Models from the command line using Simon Willison's llm CLI tool. Supports OpenAI, Anthropic, Gemini, Llama, and dozens of other models via plugins. Features include chat sessions, embeddings, structured data extraction with schemas, prompt templates, conversation logging, and tool use. This skill is triggered when the user says things like "run a prompt with llm", "use the llm command", "call an LLM from the command line", "set up llm API keys", "install llm plugins", "create embeddings", or "extract structured data from text".
Build Retrieval-Augmented Generation (RAG) Q&A systems with Claude or OpenAI. Use for creating AI assistants that answer questions from document collections, technical libraries, or knowledge bases.
Extract structured information from unstructured text using LLMs with source grounding. Use when extracting entities from documents, medical notes, clinical reports, or any text requiring precise, traceable extraction. Supports Gemini, OpenAI, and local models (Ollama). Includes visualization and long document processing.
Run application agents through SpendGuard with strict hard budget caps. Use when setting up `spendguard-sidecar`, creating agent IDs, setting or topping budgets, sending OpenAI/Grok/Gemini/Anthropic calls through SpendGuard endpoints, and troubleshooting budget enforcement errors like insufficient budget, in-flight lock conflicts, missing `x-cynsta-agent-id`, or remote pricing signature failures.