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building-gold-corpus

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.

77

Quality

97%

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SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, lean body with a concrete fixture shape, an executable validation snippet, and a disciplined workflow with an explicit validation checkpoint. The only notable gap is that agreement computation and annotation-tool config translation are described but not shown with runnable examples.

Suggestions

Add a short copy-paste snippet for computing span-level inter-annotator agreement (e.g., a few lines producing span F1 or Cohen's κ), since step 5 currently names the metric without showing how to compute it.

Include a minimal BRAT `annotation.conf` entity-types example and/or a Label Studio labeling-config XML fragment so step 4's tool configuration is directly executable rather than descriptive.

Show one example of converting a BRAT `.ann` standoff (or Label Studio JSON) record into the fixture `gold_spans` format, since the 'map either back to the fixture shape above' instruction currently leaves the translation logic to the reader.

DimensionReasoningScore

Conciseness

The body is lean and assumes competence: a fixture JSON example, a directory tree, seven tight workflow steps, and an edge-case list with no padding or explanation of concepts Claude already knows. The repeated 'committed = synthetic' rule is deliberate hard-rule emphasis rather than verbosity, so it fits the score-5 anchor over the score-4 'could be trimmed' anchor.

5 / 5

Actionability

The fixture JSON and the `load_fixtures` validation snippet are executable and copy-paste ready, and the directory layout is concrete. However, key steps — computing 'span-level inter-annotator agreement (F1 or Cohen's κ)' and mapping BRAT's `annotation.conf` or Label Studio's labeling-config XML back to the fixture shape — remain at the instruction level without examples, matching the score-4 anchor 'minor gaps'.

4 / 5

Workflow Clarity

The seven-step workflow has a clear sequence and an explicit validation checkpoint ('Validate and commit. Run `load_fixtures`; confirm spans align and ids are unique') plus a pre-trust validation snippet in Quick Start and re-validate guidance in the gotchas for shifted offsets. This feedback-loop structure matches the score-5 anchor, and the batch-commit operation does have validation so the cap does not apply.

5 / 5

Progressive Disclosure

The single SKILL.md stays at overview altitude with well-organized sections, and the 'Standards & references' section links to external resources one level deep, clearly signaled. No content that belongs in separate bundle files is inlined (no references/, scripts/, or assets/ directories exist).

5 / 5

Total

19

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description in third person that states concrete deliverables, an explicit 'Use when' clause, and a rich set of natural trigger terms, all scoped tightly to the OpenMed gold-corpus niche. It also encodes the synthetic-vs-licensed data boundary, which further distinguishes it from adjacent skills.

DimensionReasoningScore

Specificity

The description enumerates multiple concrete deliverables — 'label schema, annotation guidelines, BRAT or Label Studio config, and disjoint train/dev/test splits' — giving comprehensive coverage of what scaffolding produces. It clearly exceeds the score-4 anchor (minor gaps) since every major output of the skill is named.

5 / 5

Completeness

It explicitly answers both questions: 'what' in the opening capability sentence and 'when' via 'Use when the user wants to create eval fixtures, set up annotation, ...' plus a concrete trigger list. This mirrors the rubric's score-5 example pattern exactly; a 4 would require the 'when' to be less explicit.

5 / 5

Trigger Term Quality

The explicit 'Trigger on' clause lists natural terms users would say ('gold corpus', 'annotation project', 'label schema', 'BRAT', 'Label Studio', 'train dev test split'), and the 'Use when' clause adds synonyms ('label set', 'held-out gold set', 'eval fixtures'). Coverage includes tool names and common variations, matching the comprehensive score-5 anchor.

5 / 5

Distinctiveness Conflict Risk

The description is pinned to a clear niche — synthetic gold-standard annotation for the 'OpenMed eval harness' — with distinct named triggers (BRAT, Label Studio, gold corpus). Overlap with adjacent skills (NER benchmarking, leakage gating) is avoided by framing this skill as scaffolding fixtures, not running evaluations.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

maziyarpanahi/openmed

Path

skills/building-gold-corpus/SKILL.md

Reviewed

Table of Contents

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