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quapas-quality-assessment-for-prognosis-studies

Evaluates bias in medical literature (prognosis studies) using QUAPAS criteria. Use when the user wants to assess the quality or risk of bias of a medical paper text.

57

Quality

66%

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SecuritybySnyk

Passed

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Fix and improve this skill with Tessl

tessl review fix ./scientific-skills/Data Analysis/quapas-quality-assessment-for-prognosis-studies/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

60%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 core Workflow is a clear, actionable QUAPAS procedure with a strict JSON schema and well-signaled bundle references. The body is weakened by large tracts of generic boilerplate that restate the description and by a truncated Helper Scripts section.

Suggestions

Remove the templated 'When to Use', 'Key Features', 'Dependencies', and 'Implementation Details' boilerplate that restates the description; keep only skill-specific guidance.

Finish or delete the incomplete 'Helper Scripts' section, which ends mid-sentence at 'before assessment:' with no following content.

Add an explicit validation step to the workflow (e.g., 'Confirm the final JSON conforms to the schema before returning') to push workflow_clarity toward 5.

DimensionReasoningScore

Conciseness

Multiple boilerplate sections ('When to Use', 'Key Features', 'Dependencies', 'Implementation Details') repeat the description verbatim or state generic templated guidance, adding padding Claude does not need; not severe enough for 1 but noticeably verbose.

2 / 5

Actionability

The workflow gives concrete ROB rules, five named domains, a strict JSON output schema, and references a real executable script (scripts/extract_pdf.py); minor gaps (no inline example of running the assessment) keep it below 5.

4 / 5

Workflow Clarity

A clear 6-step sequence with explicit decision rules (all Yes -> Low, any No -> High) and a bounded JSON deliverable; lacks an explicit 'verify output against schema' checkpoint, so it does not reach 5.

4 / 5

Progressive Disclosure

Detail is appropriately pushed to one-level-deep, clearly signaled references (references/quapas_prompts.md, scripts/extract_pdf.py, both real files); the JSON schema is duplicated inline in both SKILL.md and the reference, a minor organization gap.

4 / 5

Total

14

/

20

Passed

Description

73%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 clearly identifies a specialized niche (QUAPAS prognosis-study bias assessment) and pairs a concrete 'what' with an explicit 'Use when' trigger. It is held back from the top tier by a single trigger phrase and limited action coverage.

Suggestions

List a couple more concrete actions (e.g., 'extracts study metadata', 'produces a per-domain risk-of-bias JSON') to lift specificity toward 5.

Broaden trigger terms with common synonyms users actually say, such as 'systematic review', 'meta-analysis', or 'critical appraisal of prognosis studies'.

DimensionReasoningScore

Specificity

Names the domain ('prognosis studies', 'QUAPAS criteria') and one concrete action ('Evaluates bias'), matching the 1-2 concrete actions anchor; not comprehensive enough for 4.

3 / 5

Completeness

Explicitly states both what it does and a 'Use when the user wants to assess...' trigger; the trigger is a single phrase rather than the multiple concrete triggers of the 5 anchor.

4 / 5

Trigger Term Quality

Includes natural phrases a user would say ('assess the quality or risk of bias', 'medical paper text'); a few common synonyms like 'systematic review' or 'meta-analysis' are missing, keeping it below 5.

4 / 5

Distinctiveness Conflict Risk

'QUAPAS criteria' and 'prognosis studies' carve a clear, specialized niche with distinct triggers and minimal overlap with other skills.

5 / 5

Total

16

/

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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