By rohitg00
Evaluate and compare ML model performance across accuracy, latency, memory, cost, and bias metrics, then generate ranked recommendations to select the best model for a given dataset.
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npx claudepluginhub rohitg00/awesome-claude-code-toolkit --plugin model-evaluatorPersistent memory for AI coding agents -- captures tool usage, compresses via LLM, injects context into future sessions. 12 hooks, 41 MCP tools, 4 skills, real-time viewer.
Complete AI coding workflow system. Self-correcting memory + persistent FTS5-indexed research wikis + auto-research loop + multi-LLM council on a single SQLite store. 33 skills, 8 agents, 22 commands, 37 hook scripts across 24 events. Cross-agent via SkillKit.
Complete developer toolkit for Claude Code
Image and visual analysis with screenshot interpretation and text extraction
API design, documentation, and testing with OpenAPI spec generation
Comprehensive model evaluation with multiple metrics
ML experiment tracking with metrics logging and run comparison
ML/perf investigation skills: topic, plan, judge, run, sweep
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.