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Clean Implementations For Large Language Models

implementing-llms-litgpt

Orchestra-Research/AI-Research-SKILLs

LitGPT provides clean, readable, and production-ready implementations for over 20 state-of-the-art LLMs (including Llama, Gemma, Phi, Mistral). It supports comprehensive workflows, including full fine-tuning, efficient LoRA/QLoRA adaptation, and pretraining from scratch. Ideal for developers needing academic understanding or robust, deployable model architectures.

Automate Insighto AI Tasks

insighto-ai-automation

ComposioHQ/awesome-claude-skills

Automate Insighto AI operations by discovering tool schemas via Rube MCP, validating Insighto AI connections, and executing workflows through Composio’s toolkit to keep integrations resilient.

Kling AI Pricing and Cost Optimization

klingai-pricing-basics

jeremylongshore/claude-code-plugins-plus-skills

This skill provides a comprehensive guide to the credit-based pricing model of Kling AI. It details costs for video generation (Standard/Professional modes), image creation (Kolors), and API resource packs. Users can utilize this guide to accurately budget, estimate total project costs, and implement cost-saving strategies, ensuring the efficient and economical execution of their AI content generation workflow.

Kontent AI Automation

kontent-ai-automation

ComposioHQ/awesome-claude-skills

Automates Kontent AI workflows via the Rube MCP-powered Composio toolkit, guiding tool discovery, connection checks, and schema-compliant multi-executions for reliable content automation.

Netlicensing Automation

labs64-netlicensing-automation

ComposioHQ/awesome-claude-skills

Automate Labs64 Netlicensing operations through Composio’s Rube MCP by discovering current tool schemas, managing toolkit connections, and executing workflows safely with session-aware, schema-compliant tool calls.

Lambda GPU Cloud Guide

lambda-labs-gpu-cloud

Orchestra-Research/AI-Research-SKILLs

Playbook for running ML training and inference on Lambda Labs’ on-demand GPU cloud, covering when to choose it, console/SSH/API workflows, available GPUs, pricing, and persistent storage with Lambda Stack pre-installed.

Langbase Automation Guide

langbase-automation

ComposioHQ/awesome-claude-skills

Automates Langbase operations through the Rube MCP-powered Composio toolkit, guiding connectors, tool discovery, schema validation, and multi-step execution flows while emphasizing session reuse and pagination awareness.

Building LangChain Tool-Calling Agents

langchain-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

Master the creation of autonomous AI agents using LangChain. This guide covers defining tools with schemas (Zod), utilizing `createToolCallingAgent` and `AgentExecutor` for multi-step decision-making, managing conversation memory, and implementing streaming output. Ideal for building complex, tool-augmented workflows.

Langfuse LLM Tracing and Observability

langfuse-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

Implement comprehensive end-to-end tracing for Large Language Model (LLM) calls, chains, and complex agents using Langfuse. This workflow demonstrates techniques like OpenAI drop-in wrappers, manual span creation for RAG pipelines, tracking streamed responses, and integrating with various LLM providers (OpenAI, Anthropic). Use this to monitor latency, track token usage, and debug complex AI features in production.

Langfuse LLM Evaluation And Benchmarking

langfuse-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive workflow for implementing robust LLM evaluation using Langfuse. This skill guides users through key processes including scoring traces (numeric, categorical, boolean), collecting user feedback, managing versioned prompts, creating structured datasets, running automated experiments, and implementing LLM-as-a-Judge patterns for advanced quality assurance and system benchmarking.

Learning Rate Scheduler

learning-rate-scheduler

jeremylongshore/claude-code-plugins-plus-skills

Provides automated guidance for configuring learning rate schedulers during ML training, covering best practices, setup patterns, and framework-ready code snippets for PyTorch, TensorFlow, and Scikit-learn while validating against common standards.

Troubleshooting Lindy AI Agent Errors

lindy-common-errors

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to diagnose and resolve common errors in Lindy AI agent workflows. Covers troubleshooting issues related to trigger failures (webhooks, email, schedule), action execution errors (Slack, Gmail, HTTP requests, code), and advanced agent step logic problems, ensuring reliable automation.

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