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Agent Data Handling and Compliance Guide

lindy-data-handling

jeremylongshore/claude-code-plugins-plus-skills

This skill provides comprehensive best practices for managing data within AI agent workflows. It covers data classification (PII, PHI), prompt-level controls, secure memory usage, and isolation techniques. It ensures that agents adhere to global regulations like GDPR, CCPA, and HIPAA when handling sensitive or regulated data.

Advanced Automation Migration to Lindy AI

lindy-migration-deep-dive

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide detailing advanced strategies for migrating complex, rule-based automations from platforms like Zapier, Make, n8n, or custom codebases to Lindy AI. It teaches users how to redesign workflows, leveraging AI agents to replace rigid conditional logic with adaptable reasoning, ensuring a smooth and intelligent transition across multiple phases.

AI Agent Performance Tuning Guide

lindy-performance-tuning

jeremylongshore/claude-code-plugins-plus-skills

This guide provides comprehensive strategies for optimizing the execution speed, reliability, and cost efficiency of AI agents. Learn how to minimize latency, right-size models (e.g., Flash vs. Opus), consolidate multiple LLM steps into single prompts, and optimize workflow triggers. Ideal for use when agents are slow, consuming excessive credits, or producing inconsistent results.

LLM Integration Patterns

llm-integration

rohitg00/awesome-claude-code-toolkit

Practical guide to integrating Claude models: covers API client usage, streaming responses, tool calls, RAG pipelines, document chunking, cost-aware model selection, and anti-pattern warnings for efficient LLM workflows.

Multi-Agent MLOps Pipeline Orchestration

machine-learning-ops-ml-pipeline

sickn33/antigravity-awesome-skills

This skill guides the design and implementation of a complete, production-grade Machine Learning pipeline. It employs a multi-agent orchestration workflow, systematically covering all stages of the ML lifecycle: data ingestion and quality assurance, feature engineering, model training, optimization, and robust deployment (MLOps). It ensures built-in reproducibility, monitoring, and scalability.

Mailcoach Workflow Automation

mailcoach-automation

ComposioHQ/awesome-claude-skills

Automates Mailcoach operations through Composio’s Rube MCP toolkit by first discovering current tool schemas, managing the mailcoach connection, and executing schema-compliant workflows safely with session and memory handling.

Mailsoftly Automation Guide

mailsoftly-automation

ComposioHQ/awesome-claude-skills

Guidelines for automating Mailsoftly tasks through Composio’s Mailsoftly toolkit via Rube MCP, covering discovery, connection checks, execution steps, and schema-aware tool usage for reliable workflows.

Mistral AI Automation

mistral-ai-automation

ComposioHQ/awesome-claude-skills

Automates Mistral AI workflows through the Composio MCP integration, letting teams upload files for fine-tuning, batch jobs, OCR, manage RAG document libraries, monitor tuning jobs, and retrieve or catalog data for cross-app pipelines.

Mistral: Embeddings, RAG, and Function Calling

mistral-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive workflow for advanced AI applications using Mistral AI. This guide covers generating text and code embeddings (1024 dimensions), implementing semantic search via cosine similarity, and building robust RAG pipelines. It also demonstrates advanced tool augmentation using function calling, allowing LLMs to interact with external APIs and databases.

Mixed Precision Trainer

mixed-precision-trainer

jeremylongshore/claude-code-plugins-plus-skills

Automates mixed precision ML training workflows by surfacing best practices, generating runnable code/configurations, and validating setups across data prep, model tuning, and experiment tracking.

Production ML Engineering and MLOps

sickn33/antigravity-awesome-skills

A comprehensive skill set for building, deploying, and managing production-grade Machine Learning systems. Expertise spans modern frameworks (PyTorch, TensorFlow), model serving using Kubernetes, advanced feature engineering (Feast, Tecton), and robust MLOps practices (monitoring, A/B testing, CI/CD). Focuses on delivering scalable, reliable, and business-value-driven AI solutions.

ML Paper Writing

ml-paper-writing

Orchestra-Research/AI-Research-SKILLs

Guides researchers through drafting publication-ready machine learning papers for NeurIPS, ICML, ICLR, ACL, AAAI, and COLM by combining expert writing philosophy with LaTeX templates, citation verification APIs, and collaborative workflows.

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