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clinical-data-cleaner

Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data fo...

64

Quality

76%

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SecuritybySnyk

Passed

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tessl review fix ./scientific-skills/Data Analysis/clinical-data-cleaner/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

53%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 skill body is actionable and well-structured with a genuine reference bundle, but it is padded with generic boilerplate template sections and lacks concrete validate-fix-retry feedback loops for its batch/destructive cleaning operations. Tightening the appended template material and adding explicit SDTM re-validation checkpoints would raise both conciseness and workflow clarity.

Suggestions

Remove or condense the appended generic template sections (Output Requirements, Response Template, Output Contract, Failure Handling, User Checkpoints, Required Inputs) and the verbatim 'When to Use' restatement of the description to cut boilerplate padding.

Add an explicit validate->fix->retry feedback loop in the Workflow that re-runs SDTM IG validation (e.g. validate_domain / cross-check against references/sdtm_ig_guide.md) after each cleaning pass rather than only generic 'confirm with user' checkpoints.

Replace the audit --input text-string example with a real file-path invocation consistent with the CLI Usage examples so all runnable commands are copy-paste ready.

DimensionReasoningScore

Conciseness

While the code and field lists are relevant, the body carries noticeable boilerplate padding — a 'When to Use' section that restates the description verbatim and appended template sections (Output Requirements, Response Template, Output Contract, Failure Handling, User Checkpoints, Required Inputs) that repeat generic scope/fallback ideas rather than clinical-data specifics.

2 / 5

Actionability

Provides mostly executable guidance — real Python API calls, concrete CLI commands with flags, required-field lists, and a clinical-threshold table — with only minor gaps (e.g. the audit --input example uses a text string rather than a file path).

4 / 5

Workflow Clarity

A 5-step workflow with a quick-check and fallback language exists, but for a batch/destructive clinical-data skill the validation checkpoints are generic ('confirm with user') rather than concrete validate->fix->retry loops against SDTM IG, which caps the score per the batch-operations guideline.

3 / 5

Progressive Disclosure

The body acts as an overview pointing to real, one-level-deep reference files (common-patterns, troubleshooting, sdtm_ig_guide, domain_specs, outlier_thresholds) that all exist and are clearly signaled, with bulk detail offloaded; minor inlining of boilerplate keeps it just below the top anchor.

4 / 5

Total

13

/

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.

The description is specific, complete, and well-targeted: it states concrete capabilities and explicit trigger conditions for a distinct clinical-data regulatory niche. It answers both what the skill does and when to use it.

DimensionReasoningScore

Specificity

Lists multiple concrete actions covering the full scope — cleaning, FDA/EMA submission prep, SDTM standardization, missing-value handling, outlier detection, CRF-to-CDISC conversion, and audit trails — matching the comprehensive-coverage anchor.

5 / 5

Completeness

Explicitly answers both 'what' ('Cleans and standardizes clinical trial data for regulatory compliance with audit trails') and 'when' (a full 'Use when...' clause with concrete trigger phrases), matching the top anchor.

5 / 5

Trigger Term Quality

Includes natural user phrases with domain synonyms and standards names (clinical trial data, FDA/EMA submission, SDTM datasets, missing values, outliers in lab results, raw CRF data, CDISC format), giving comprehensive natural-term coverage.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear clinical-regulatory niche with distinct triggers (SDTM, CDISC, CRF, FDA/EMA, lab results) and minimal overlap risk with other skills.

5 / 5

Total

20

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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