llm▌

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.

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

$21

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

Intelligent model routing, budget tracking, and retry logic to optimize LLM API costs without sacrificing quality. \n \n Routes requests to cheaper models (Haiku) for simple tasks and expensive models (Sonnet, Opus) only when complexity thresholds are met, reducing spend by 3–19x on routine work \n Tracks cumulative API costs with immutable dataclasses, enforces budget limits, and fails early to prevent overspend \n Implements narrow retry logic that retries only on transient errors (network, ra

Systematic evaluation of LLM applications using automated metrics, human feedback, and statistical testing. \n \n Covers three evaluation approaches: automated metrics (BLEU, ROUGE, BERTScore, accuracy, precision/recall), human evaluation across dimensions like accuracy and coherence, and LLM-as-Judge for pointwise, pairwise, and reference-based scoring \n Includes implementations for text generation, classification, and retrieval (RAG) evaluation with ready-to-use metric functions and custom me