building-gold-corpus

building-gold-corpus is a Fine-tuning & training data skill for AI agents, published by maziyarpanahi in the openmed repository on GitHub. Skillspace rates building-gold-corpus C (74/100) and it passed the safety scan; the rating is based on static analysis only.

Scaffold a synthetic gold-standard annotation project for evaluating OpenMed NER and de-identification models — label schema, annotation guidelines, BRAT or Label Studio config, and disjoint train/dev/test splits. Use when the user wants to create eval fixtures, set up annotation, define a label set, write guidelines, configure an annotation tool, or build a held-out gold set for the OpenMed eval harness. Trigger on "gold corpus", "annotation project", "label schema", "annotation guidelines", "BRAT", "Label Studio", "train dev test split", or "build eval fixtures" for OpenMed. Committed gold must be synthetic; licensed (i2b2/n2c2/MIMIC) data is eval-only and never committed.

Grade

C

Quality

74 / 100

Safety

Passed

GitHub stars

4,654

Unproven: rated by static analysis only, not yet compared head-to-head.

Not launch-quality. It is graded C with a clean safety verdict, but our quality filters exclude it: a model that read it found it written only for the repository it lives in. So it is not in our launch-quality set, the skills our MCP server searches and recommends. How launch-quality is decided

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

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