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parsing-lab-values

Parse laboratory values and reference ranges from clinical text and flag results as low, normal, high, or critical with OpenMed. Use when the user needs to interpret lab results, compute abnormal flags, parse reference ranges like "135-145" or "<5", honor an originating-lab flag (H/L/critical), or turn extracted lab entities into structured high/low/critical signals. Covers openmed.clinical.parse_reference_range, derive_abnormal_flag, ReferenceRange, and AbnormalFlag, with UCUM/LOINC framing. Unit-agnostic — it does not convert units. Pairs after extracting-clinical-entities (lab entities from analyze_text).

75

Quality

93%

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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 tight, actionable reference for a focused API: executable examples, a sequenced workflow, and a strong gotchas section. Only minor conciseness trims and the absence of an explicit validation loop keep it from full marks.

Suggestions

Trim the introductory framing paragraph and the one-line glosses next to LOINC/UCUM/HL7 links, which restate concepts Claude already knows.

Add an explicit validate→fix→retry note (e.g., re-parsing a range that returned empty bounds after normalizing units) to give the workflow a clear feedback loop.

DimensionReasoningScore

Conciseness

Mostly lean and code-first, but the opening framing ('Lab results in clinical text arrive as a value, a unit, and a reference range') and the Standards glosses ('LOINC — universal codes for laboratory observations') lightly explain concepts Claude already knows and could be trimmed.

4 / 5

Actionability

Copy-paste-ready Python in Quick start covers closed ranges, one-sided bounds, explicit-flag precedence, and unparseable inputs with shown return values, and the workflow gives concrete callable signatures for every common case.

5 / 5

Workflow Clarity

A clear 5-step sequence with an explicit 'handle unknown as needs review' checkpoint and a dense edge-case checklist; not a destructive/batch task so no cap applies, but there is no validate→fix→retry feedback loop to reach a 5.

4 / 5

Progressive Disclosure

No bundle files exist and none are warranted; the body is a single well-organized file with clearly signaled sections and external standards linked one level deep, matching the simple-skill exception for well-organized self-contained content.

5 / 5

Total

18

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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.

An exemplary description: third-person voice, explicit what-and-when, named API surface, and a clear hand-off note that disambiguates it from related clinical skills. No padding or over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions—parse lab values, parse reference ranges, flag as low/normal/high/critical, honor an originating-lab flag—and names the specific OpenMed functions, giving comprehensive coverage rather than vague domain labeling.

5 / 5

Completeness

Explicitly answers both 'what' (parse/flag lab values with OpenMed) and 'when' via a concrete 'Use when the user needs to interpret lab results, compute abnormal flags, parse reference ranges…' clause with multiple trigger phrases.

5 / 5

Trigger Term Quality

Natural phrases a user would say ('interpret lab results', 'compute abnormal flags', 'parse reference ranges like "135-145" or "<5"') appear alongside synonyms and concrete flag tokens (H/L/critical), giving comprehensive keyword coverage.

5 / 5

Distinctiveness Conflict Risk

Carves a clear niche (OpenMed lab-value flagging) with distinct triggers and an explicit pairing note ('Pairs after extracting-clinical-entities'), minimizing overlap with sibling skills.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
maziyarpanahi/openmed
Reviewed

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