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Your localLLM repo provides a full local LLM toolchain on Apple Silicon: setup scripts, a rich CLI chat launcher, benchmarks, and diagnostics. The operational path is: install Ollama, ensure the service is running, initialize the venv, pull models, then launch chat or benchmarks.

Access AI/LLM model brand icons and logos from the lobe-icons library. The library contains 100+ icons for models (Claude, GPT, Gemini), providers (OpenAI, Anthropic, Google), and applications (ComfyUI, LobeChat).

You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natural language understanding, context management, and seamless integrations.

Multi-agent planning council that orchestrates independent implementation plans, anonymizes them, then merges into one final plan. \n \n Supports configurable planner agents (Codex, Claude, Gemini, OpenCode, or custom CLI commands) running in parallel, with optional judge override \n Conducts structured intake questioning before plan generation to clarify ambiguities, constraints, and success criteria \n Produces validated Markdown outputs with automatic retry logic (up to 2 attempts) and failur

Hybrid regex-and-LLM framework for parsing structured text, optimizing cost by handling 95–98% with regex and reserving LLM calls for edge cases. \n \n Combines regex extraction with confidence scoring to flag low-confidence items, then validates only those items with an LLM, reducing LLM calls by ~95% versus all-LLM approaches \n Includes production-ready Python patterns for regex parsing, confidence scoring, and hybrid pipeline orchestration with real metrics from a 410-item quiz parsing examp

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Answer user questions about monitoring LLMs and agentic components using data ingested into Elastic only. Focus on LLM performance, cost and token utilization, response quality, and call chaining or agentic workflow orchestration. Use ES|QL, Elasticsearch APIs, and (where needed) Kibana APIs. Do not rely on Kibana UI; the skill works without it. A given deployment typically uses one or more ingestion paths (APM/OTLP traces and/or integration metrics/logs)— discover what is available before query

Production-ready patterns for building LLM applications, inspired by Dify and industry best practices.

You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

Access 100+ language models including Claude, Gemini, Kimi, and GLM via OpenRouter. \n \n Supports Claude Opus 4.5, Sonnet 4.5, and Haiku 4.5 for varying performance and cost tradeoffs, plus Gemini 3 Pro, Kimi K2 Thinking, GLM-4.6, and Intellect 3 \n Auto-select mode picks the most cost-effective model automatically for your use case \n Designed for code generation, reasoning, content creation, data analysis, and building AI agent workflows \n Requires inference.sh CLI ( infsh ) and API authenti