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etl-to-omop-cdm

Map OpenMed-extracted, terminology-coded conditions, drugs, and measurements into OMOP CDM v5.4 clinical tables (condition_occurrence, drug_exposure, measurement) for OHDSI/ATLAS analytics. Use when the user wants to load NLP-derived facts into an OMOP database, build an OHDSI ETL from clinical notes, populate condition_occurrence or drug_exposure from text, or standardize note-derived findings to OMOP standard concepts. Covers the source-to-standard concept mapping pattern, required vs optional CDM fields, type concepts for NLP-derived rows, and the user-supplied OHDSI vocabulary (CONCEPT/CONCEPT_RELATIONSHIP). Consumes coded OpenMed analyze_text output (after SNOMED/RxNorm/LOINC linking) and produces OMOP-conformant rows.

70

Quality

85%

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SKILL.md
Quality
Evals
Security

Quality

Content

78%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, actionable skill body with good progressive disclosure and a clear sequenced workflow including validation. The main improvement opportunity is making the batch-load validate→fix→retry feedback loop explicit rather than implicit.

Suggestions

Make the validation feedback loop explicit: after running Achilles/DataQualityDashboard, add a 'fix and re-run' step so the validate→fix→retry cycle is unambiguous for this batch database-load operation.

Tighten the Quick start block — replace the `char_span: (fact_start, fact_end)` placeholder with a concrete value and trim the verbose inline comment block.

Slightly compress the 'source → standard pattern' section, which restates the two-concept-id idea Claude already knows, to recover token budget.

DimensionReasoningScore

Conciseness

Lean and well-sectioned with no padding of basics Claude already knows; a couple of spots (the restated two-concept-id pattern, the verbose Quick start comment block) could be trimmed.

4 / 5

Actionability

Concrete Quick start code with a worked E11.9 → 201826 example, a domain→table mapping table, and explicit field-level workflow steps; minor gaps (a placeholder char_span and the coding step delegated to other skills) keep it from fully copy-paste ready end-to-end.

4 / 5

Workflow Clarity

Six clearly sequenced steps with a validation checkpoint (Achilles/DataQualityDashboard) and error-routing edge cases, but the validate→fix→retry feedback loop is only implicit rather than spelled out for this batch database-load operation.

4 / 5

Progressive Disclosure

A concise overview body that signals one-level-deep references to references/omop_cdm_v5_4_fields.md (verified to exist and hold the referenced field detail), with external standards grouped in a references section.

5 / 5

Total

17

/

20

Passed

Description

92%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, specific description that clearly states what the skill does and when to use it, with concrete trigger phrases and a distinct niche. Slightly heavy on domain jargon, costing one point on trigger-term breadth.

DimensionReasoningScore

Specificity

Names the domain and multiple concrete actions — mapping conditions/drugs/measurements into named OMOP tables (condition_occurrence, drug_exposure, measurement), the source-to-standard pattern, type concepts, and required vs optional fields — giving comprehensive coverage.

5 / 5

Completeness

Explicitly answers both 'what' (map OpenMed-extracted coded facts into OMOP CDM v5.4 tables) and 'when' via a concrete 'Use when...' clause with multiple trigger phrases.

5 / 5

Trigger Term Quality

Strong natural triggers ('load NLP-derived facts into an OMOP database', 'build an OHDSI ETL from clinical notes', 'populate condition_occurrence or drug_exposure from text') with named artifacts, though coverage leans technical and a few lay synonyms are absent.

4 / 5

Distinctiveness Conflict Risk

A clear niche (OpenMed → OMOP CDM v5.4 NLP-derived ETL into specific tables) with distinct triggers and minimal overlap with generic coding/linking skills.

5 / 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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
maziyarpanahi/openmed
Reviewed

Table of Contents

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