nuggetizer

Use when analyzing nuggetizer metrics outputs — comparing runs, computing agreement between models, reporting token usage and latency from trace fields, and building side-by-side evaluation tables. Use after running metrics to interpret and compare results.

Set up a nuggetizer development environment — checks Python 3.11+, installs via uv or pip, and verifies with doctor. Use when someone is onboarding, setting up a fresh clone, or troubleshooting their environment.

Use when validating nuggetizer batch outputs — checks JSONL integrity, duplicate qids, missing nugget fields, assignment label validity, and metrics consistency. Wraps `nuggetizer validate` plus custom assertions. Use after running create, assign, or metrics to verify output correctness.

Use when working with nuggetizer CLI commands (create, assign, metrics), prompt templates, JSONL formats, or introspection (doctor, describe, schema, validate). Covers all entry points, flags, and the YAML prompt system.