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Optimizing Juicebox API Performance

juicebox-performance-tuning

jeremylongshore/claude-code-plugins-plus-skills

This guide provides advanced strategies for optimizing interactions with the Juicebox AI API. It covers crucial best practices such as implementing caching mechanisms (with defined TTLs), executing batch operations for efficiency, managing rate limits, and optimizing data chunking for large datasets. Following these steps can significantly reduce end-to-end analysis time and ensure responsive, scalable data 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 AI Compliance and Data Protection

mistral-data-handling

jeremylongshore/claude-code-plugins-plus-skills

This skill provides comprehensive patterns for handling sensitive data when integrating with Mistral AI. It covers PII redaction (emails, phone numbers, etc.), implementing data retention policies, and enforcing compliance with global privacy regulations like GDPR and CCPA. Key features include safe API wrappers, dataset sanitization for fine-tuning, and conversation history management with TTL and user erasure rights.

PEFT Fine-Tuning Guide

peft-fine-tuning

Orchestra-Research/AI-Research-SKILLs

Covers HuggingFace PEFT workflows for LoRA/QLoRA-based LLM fine-tuning, enabling sub-1% parameter updates on limited GPUs plus adapter serving tips.

PostgreSQL Database Optimization Workflow

postgresql-optimization

sickn33/antigravity-awesome-skills

A specialized, comprehensive workflow designed for optimizing PostgreSQL databases. It guides users through performance assessment, advanced query tuning (using EXPLAIN ANALYZE), defining optimal indexing strategies, fine-tuning configuration parameters (like shared_buffers), and setting up robust maintenance and monitoring practices for production environments.

LLM Quantization Toolkit

quantizing-models-bitsandbytes

Orchestra-Research/AI-Research-SKILLs

Quantizes HuggingFace LLMs to 8-bit or 4-bit with bitsandbytes, cutting memory by up to 75% while keeping accuracy, and supports QLoRA fine-tuning plus 8-bit optimizers for faster, memory-efficient training.

Sentry Cost Optimization and Tuning

sentry-cost-tuning

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide on optimizing Sentry observability costs. Learn techniques such as SDK-level error sampling, fine-grained performance tracing control, and event filtering using the `beforeSend` hook. These methods significantly reduce the volume of error and transaction data sent, allowing users to manage quotas and prevent overage charges without losing visibility into mission-critical production errors.

Simple Preference Optimization for LLM Alignment

Orchestra-Research/AI-Research-SKILLs

SimPO (Simple Preference Optimization) is a state-of-the-art, reference-free method designed for aligning Large Language Models (LLMs) using human preference data. It is an efficient alternative to DPO and PPO, notably outperforming DPO without requiring a separate reference model. It is ideal for practitioners who need faster, simpler, and more resource-efficient fine-tuning for preference alignment.

Optimize TwinMind Meeting Transcription Performance

twinmind-performance-tuning

jeremylongshore/claude-code-plugins-plus-skills

This skill optimizes the performance of TwinMind's meeting AI operations. It fine-tunes transcription accuracy and speed using the advanced Ear-3 model, managing audio quality, and implementing caching strategies. Ideal for developers or system administrators needing to integrate, troubleshoot, or optimize high-volume meeting transcription workflows.

Rapid Unsloth Fine-Tuning

Orchestra-Research/AI-Research-SKILLs

Provides expert guidance on Unsloth to accelerate fine-tuning via LoRA/QLoRA, reduce memory usage, and streamline API usage, debugging, and best practices.

Transformer Models for AI Tasks

K-Dense-AI/claude-scientific-skills

A comprehensive skill for leveraging massive pre-trained transformer models across diverse AI domains, including Natural Language Processing (NLP), Computer Vision (CV), and Audio. Use this skill for advanced tasks such as text generation, classification, question answering, summarization, object detection, and model fine-tuning on proprietary datasets. It provides streamlined pipelines and granular control for deployment.

Training LLMs on Hugging Face Cloud Infrastructure

hugging-face-model-trainer

sickn33/antigravity-awesome-skills

A comprehensive skill for fine-tuning and training large language and vision models using advanced techniques like TRL (SFT, DPO, GRPO) or Unsloth. This process runs entirely on managed Hugging Face cloud GPUs, eliminating the need for local GPU setup. It supports modern training workflows, model conversion (GGUF for local deployment), and ensures seamless artifact storage to the Hugging Face Hub.

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