Loading...
Loading...
Found 1,564 Skills
Technical Document Knowledge Base (LLM Wiki) for Alibaba Cloud Tongyi Qianfan Platform. Activated when users inquire about Qianfan-related issues such as model lists, API parameters, error codes, application development (Agent/RAG/Knowledge Base/Memory/Plugins), model comparison and pricing, SDK/OpenAI compatible interfaces, multimodal capabilities (speech/image/video), Token billing, etc. It includes structured model market data in models (including contextWindow/QPM/pricing/sample code), wiki synthesis layer (topic pages/concept pages/comparison pages), and raw original document layer; for model specification issues, check models/index.md first, and for document-related issues, check wiki/index.md first.
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
AI-first application patterns, LLM testing, prompt management
Vision, audio, and multimodal LLM integration patterns. Use when processing images, transcribing audio, generating speech, or building multimodal AI pipelines.
Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies. Track spend by team and model, set budgets, and implement cost-aware routing.
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
Finding and accessing AI/LLM model brand icons from lobe-icons library. Use when users need icon URLs, want to download brand logos for AI models/providers/applications (Claude, GPT, Gemini, etc.), or request icons in SVG/PNG/WEBP formats.
Large Language Model development, training, fine-tuning, and deployment best practices.
LLM gateway and routing configuration using OpenRouter and LiteLLM. Invoke when: - Setting up multi-model access (OpenRouter, LiteLLM) - Configuring model fallbacks and reliability - Implementing cost-based or latency-based routing - A/B testing different models - Self-hosting an LLM proxy Keywords: openrouter, litellm, llm gateway, model routing, fallback, A/B testing
Security patterns for LLM integrations including prompt injection defense and hallucination prevention. Use when implementing context separation, validating LLM outputs, or protecting against prompt injection attacks.
Crafting effective prompts for LLMs. Use when designing prompts, improving output quality, structuring complex instructions, or debugging poor model responses.
Motto: The LLM is the dice. It narrates the outcome.