Content
75%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-crafted, expert-level skill body: tight edge-case guidance, a realistic abridged CIRCE example, and a sequenced workflow with a validation step. The main gaps are executability of the NLP-feature materialization step and of the validation tooling, plus mild repetition of frontmatter context.
Suggestions
Replace the comment placeholders in the NLP augmentation example with executable code or a concrete SQL insert showing how an extracted entity (e.g. 'Current smoker' -> SNOMED concept) becomes an OBSERVATION row that a CIRCE ConceptSet can reference.
Make the validation step concrete: name the actual command or path (e.g. ATLAS/WebAPI endpoint or circe-be invocation) for generating SQL and running PheValuator-style checks, instead of 'generate SQL, run on a database'.
Trim the opening computable-phenotype definition and the open-source/licensing paragraph (already stated in the frontmatter description), and consider moving a fuller CIRCE JSON schema or validation detail into a references/ file.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and assumes domain competence (the CIRCE JSON is 'abridged', edge cases are stated as terse imperatives), but a few tokens could be trimmed: the opening definition of 'computable phenotype', the eMERGE/PheKB historical comparison ("This mirrors how eMERGE and PheKB phenotypes mix..."), and the open-source/licensing paragraph repeat frontmatter statements. Anchor 4 ('minor instances of over-explanation that could be trimmed') fits better than 5, where every token would earn its place. | 4 / 5 |
Actionability | Concrete, realistic CIRCE JSON with real concept IDs and an executable `openmed.analyze_text` call anchor the guidance, but the NLP-to-OMOP materialization steps are comment placeholders rather than code ("# 2) Write a derived OBSERVATION ... e.g. Observation: ..."), and SQL generation/validation is described only at command-less, high level ("generate SQL, run on a (synthetic/de-identified) database"). That is 'mostly executable with minor gaps' (anchor 4), not fully copy-paste ready (anchor 5) nor pseudocode-dominant (anchor 3). | 4 / 5 |
Workflow Clarity | The six-step workflow has a clear sequence and an explicit validate step with an iterate loop ("Validate against OMOP CDM: generate SQL, run on a (synthetic/de-identified) database, review cohort counts; iterate with PheValuator-style checks"), and this is analytics (non-destructive) so the batch/destruction cap does not apply. However, the validation checkpoints lack executable commands (no WebAPI endpoint, script, or tool invocation), matching anchor 4 ('most checkpoints present; minor validation gaps') rather than anchor 5's fully explicit validate->fix->retry loop. | 4 / 5 |
Progressive Disclosure | No bundle files exist (no references/, scripts/, or assets/), and the ~137-line body is well-sectioned with an appropriately abridged inline JSON example and a one-level 'Standards & references' list of external URLs. Structure is good (anchor 4), but everything lives inline in SKILL.md — bulkier material (a fuller CIRCE schema reference, validation/PheValuator detail) could be split into a references/ file, so it falls short of anchor 5's 'content appropriately split' with well-signaled file references. | 4 / 5 |
Total | 16 / 20 Passed |