Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.
Evidence-first automation inventory and overlap audit workflow for ECC. Use when the user wants to know which jobs, hooks, connectors, MCP servers, or wrappers are live, broken, redundant, or missing before fixing anything.
Label GitHub issues and PRs found during QA testing. Use when organizing QA findings with proper labels.
Gamitin kapag humihiling ang gumagamit na gumawa, mag-scaffold, o mag-edit ng mga Jupyter notebook (`.ipynb`) para sa mga eksperimento, eksplorasyon, o tutorial; mas piliin ang mga naka-bundle na template at patakbuhin ang helper script na `new_notebook.py` upang makabuo ng malinis na panimulang notebook.
Use when recording TiDB integration tests under tests/integrationtest and verifying regenerated result files stay minimal and correct.
Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing.
Track ML experiments with Trackio using Python logging, alerts, and CLI metric retrieval.