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Found 1,280 Skills
Vision, audio, and multimodal LLM integration patterns. Use when processing images, transcribing audio, generating speech, or building multimodal AI pipelines.
LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. Use when implementing tool use, SSE streaming, local model deployment, LoRA/QLoRA fine-tuning, or multi-provider LLM APIs.
Deploys ML and LLM models on TrueFoundry with GPU inference servers (vLLM, TGI, NVIDIA NIM). Uses YAML manifests with `tfy apply`. Use when serving language models, deploying Hugging Face models, or hosting GPU-accelerated inference endpoints.
Route AI coding queries to local LLMs in air-gapped networks. Integrates Serena MCP for semantic code understanding. Use when working offline, with local models (Ollama, LM Studio, Jan, OpenWebUI), or in secure/closed environments. Triggers on local LLM, Ollama, LM Studio, Jan, air-gapped, offline AI, Serena, local inference, closed network, model routing, defense network, secure coding.
Use Claude Code's full tool system with any OpenAI-compatible LLM — GPT-4o, DeepSeek, Gemini, Ollama, and 200+ models via environment variable configuration.
Apply when implementing fulfillment, invoice, or tracking logic for VTEX marketplace seller connectors. Covers the External Seller fulfillment protocol: fulfillment simulation (checkout and indexation), order placement with reservation id, order dispatch (authorize fulfillment), OMS invoice and tracking APIs, and partial invoicing. Use for seller-side services that must answer within the simulation SLA and integrate with VTEX marketplace order management.
Detects common LLM coding agent artifacts by spawning 4 parallel subagents
List available LLM-accessible credentials. Use when you need API keys, passwords, or other secrets that have been made available to you.
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
Produce an LLM Build Pack (prompt+tool contract, data/eval plan, architecture+safety, launch checklist). Use for building with LLMs, GPT/Claude apps, prompt engineering, RAG, and tool-using agents.
Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.
Use when "LLM inference", "serving LLM", "vLLM", "llama.cpp", "GGUF", "text generation", "model serving", "inference optimization", "KV cache", "continuous batching", "speculative decoding", "local LLM", "CPU inference"