By rohitg00
Log ML experiment parameters, metrics, and artifacts with environment details, then compare runs side-by-side using comparison tables, parameter sensitivity analysis, visualizations, and best-configuration identification.
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npx claudepluginhub rohitg00/awesome-claude-code-toolkit --plugin experiment-trackerPersistent 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
Set up ML experiment tracking
Evaluate and compare ML model performance metrics
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
ML/perf investigation skills: topic, plan, judge, run, sweep
General training-decision agent + SDK usage skills for evsys-sdk. Reads a project's goal and experiment history, decides what experiment to run next, and launches it via the project's own train/benchmark skills.