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benchmarking-clinical-ner

Score an OpenMed clinical or biomedical NER model against a user-supplied gold corpus with entity-level precision, recall, and F1, then break errors down per label. Use when the user wants a seqeval-style scorecard, strict vs partial (relaxed) span matching, a per-label confusion matrix, false-negative / false-positive examples, or to debug why a model misses entities. Trigger on "evaluate NER", "entity-level F1", "seqeval", "precision recall F1", "confusion matrix", "error analysis", "strict vs partial match", or "score against gold" in an OpenMed context. The gold corpus is user-supplied; OpenMed bundles no i2b2/n2c2/MIMIC data.

76

Quality

96%

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Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

93%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.

The body is lean, dense with executable guidance, and well-structured for a single-file skill, with valuable edge-case rules (no-PHI error examples, DUA-gated corpora) and clear integration points with sibling skills. The only meaningful gap is the absence of an explicit validation checkpoint in the workflow.

DimensionReasoningScore

Conciseness

Every section earns its tokens: a compact match-mode table, two tight code blocks, a six-step workflow, hand-off pointers, and gotchas. The span-vs-token rationale is project-specific judgment, not explanation of concepts Claude already knows, so there is nothing padded to trim.

5 / 5

Actionability

Both code blocks are fully executable with real function names, arguments, and expected output keys (run_suite, error_report, compute_exact_span_f1), and the fixture format is documented inline, making the quick start copy-paste ready and covering the common cases.

5 / 5

Workflow Clarity

The six-step workflow is clearly sequenced and includes a diagnostic checkpoint (interpreting the strict-vs-relaxed F1 gap) and a triage rule, but it lacks an explicit validate-before-scoring step such as checking the fixture format, and there is no feedback loop for error recovery.

4 / 5

Progressive Disclosure

No bundle files exist, and none are needed at this size: the single body is well-organized into scannable sections, external standards links are one level deep, and hand-offs to sibling skills are clearly signaled with no nested references or inlined material that belongs in a separate file.

5 / 5

Total

19

/

20

Passed

Description

96%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 that clearly states capabilities, scope boundaries (user-supplied gold corpus, no bundled restricted data), and explicit trigger phrases. Its only weakness is a couple of generic trigger terms that slightly overlap with neighboring evaluation skills.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — entity-level precision, recall, and F1, per-label error breakdown, strict vs partial span matching, per-label confusion matrix, and FN/FP examples — matching the comprehensive-coverage anchor with no coverage gaps.

5 / 5

Completeness

It explicitly answers both what ("Score an OpenMed clinical or biomedical NER model... with entity-level precision, recall, and F1, then break errors down per label") and when ("Use when the user wants..." plus "Trigger on...") with concrete trigger phrases, mirroring the anchor-5 example.

5 / 5

Trigger Term Quality

Triggers cover the natural phrases users would say ("evaluate NER", "entity-level F1", "precision recall F1", "confusion matrix", "error analysis", "score against gold") plus tool-specific synonyms like "seqeval" and "strict vs partial match", giving comprehensive coverage.

5 / 5

Distinctiveness Conflict Risk

It carves a clear niche (OpenMed NER span-level scoring) with mostly distinct triggers, but the generic terms "error analysis" and "confusion matrix" create minor overlap risk with closely related sibling evaluation skills even though they are qualified with "in an OpenMed context".

4 / 5

Total

19

/

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/benchmarking-clinical-ner/SKILL.md

Reviewed

Table of Contents

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