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Analyze and optimize PostgreSQL queries for OLTP and OLAP workloads with AI-assisted performance tuning, indexing strategies, and execution plan analysis.

Free open-source, config-driven LLM fine-tuning framework covering full and parameter-efficient fine-tuning (LoRA, QLoRA), preference tuning (DPO, KTO, ORPO), and reinforcement learning across many model families through declarative YAML configs.

Open-source library for fast, memory-efficient fine-tuning, reinforcement learning, and training of open LLMs — train 500+ models up to 2x faster with up to 70% less VRAM and no accuracy loss, with LoRA/QLoRA support and export to GGUF, safetensors, vLLM, and Ollama.

Source-backed Claude Code subagent that reviews MCP tool result sizes against output token budgets, flagging tools that exceed the warning and default limits and recommending pagination, filtering, MAX_MCP_OUTPUT_TOKENS tuning, and per-tool size annotations.

Expert database architect and optimizer specializing in SQL, NoSQL, performance tuning, and data modeling

A CLAUDE.md rule for Go application development: goroutine and channel concurrency, context cancellation, performance tuning, HTTP and gRPC services, database patterns, and table-driven testing.

Expert in application performance optimization, profiling, and system tuning across frontend, backend, and infrastructure

PostgreSQL MCP server from Crystal DBA for schema inspection, SQL execution, EXPLAIN plans, index tuning, top-query analysis, and database health checks with configurable restricted or unrestricted access.

Official Hugging Face Agent Skills collection for Claude Code, Codex, Cursor, Gemini CLI, and other skills-compatible agents, covering Hub CLI workflows, datasets, model search, Spaces, Gradio, fine-tuning, evaluations, local models, papers, Trackio, ZeroGPU, transformers.js, TRL, and the Hugging Face MCP server.

Optimize agent prompts and system instructions with meta-prompting techniques. Improves prompt performance through A/B testing, chaining, and ROI measurement.

Official Neon MCP server that connects Claude and other MCP clients to Neon Postgres projects, branches, schemas, SQL queries, migrations, performance tuning, Neon Auth, Data API setup, and Neon documentation through a hosted Streamable HTTP endpoint.

Apache-2.0 distributed AI compute engine for scaling Python, ML data processing, training, tuning, reinforcement learning, and model serving workloads.

Apache-2.0 library for parameter-efficient fine-tuning of large pretrained models with adapters, LoRA, prompt tuning, Transformers, Diffusers, and Accelerate.

Apache-2.0 model-definition framework for pretrained text, vision, audio, video, and multimodal models across inference, training, pipelines, generation, and fine-tuning.