unmet-clinical-need-extractor

Extracts concrete unmet clinical needs from guidelines, reviews, real-world studies, and clinical-practice evidence. Use this skill when a user wants to turn broad medical research value into specific clinical pain points such as weak early detection, poor risk stratification, treatment-response heterogeneity, monitoring gaps, diagnostic delay, undertreatment, overtreatment, or implementation failure. Always ground unmet-need claims in retrieved evidence and distinguish true care gaps from generic statements of importance.

Quality

79%

Does it follow best practices?

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tessl review fix ./awesome-med-research-skills/Evidence Insight/unmet-clinical-need-extractor/SKILL.md

Quality

Content

71%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 body is well-structured with a clear sequenced workflow, a concrete mandatory output structure, and exemplary progressive disclosure through verified reference files. Its main weakness is redundancy, with the same prohibitions restated across multiple sections that could be consolidated to reduce token cost.

Suggestions

Consolidate the repeated prohibitions (disease-burden-vs-unmet-need, biomarker-enthusiasm-is-not-proof, no generic 'better outcomes needed') so each appears once, e.g. keep them in Hard Rules and remove the duplicate 'What This Skill Should Not Do' section.

Add one concise worked example showing a filled-in section or two (e.g. a sample Patient-Journey Need Map row) to make the output structure unambiguous for Claude.

Tighten the Core Function 'should / should not' lists since they substantially overlap the Hard Rules and the per-step guidance that follows.

Dimension	Reasoning	Score
Conciseness	Per-section prose is efficient and assumes Claude's competence without explaining basic concepts, but the same guidance (e.g. 'do not treat disease burden as unmet need') is repeated across Core Function, Hard Rules, Step 6, and 'What This Skill Should Not Do', introducing noticeable redundancy that could be consolidated.	3 / 5
Actionability	Provides a concrete 8-step execution sequence, a fully specified A-J output structure with per-section bullet requirements, input examples, and sample triggers; the only gap is the absence of a complete worked output example showing the filled-in structure.	4 / 5
Workflow Clarity	The 8 steps run in explicit order with a self-critical review (Step 8) and an out-of-scope redirect-and-stop validation gate, plus Hard Rules acting as a checklist; it stops short of an explicit validate-fix-retry feedback loop, keeping it just below a 5.	4 / 5
Progressive Disclosure	A dedicated 'Reference Module Integration' section maps each of seven real, one-level-deep reference files to specific output sections (A-I), keeping detailed frameworks out of the body while the overview remains navigable.	5 / 5
	Total	16 / 20 Passed

Description

87%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 concrete, well-scoped, and clearly answers both what the skill does and when to use it, with rich domain-specific trigger terms. It is slightly action-narrow (one primary verb) and could add a few simpler user phrasings, but is otherwise strong.

Dimension	Reasoning	Score
Specificity	Names a concrete action ('Extracts concrete unmet clinical needs from guidelines, reviews, real-world studies, and clinical-practice evidence') plus secondary actions ('ground unmet-need claims', 'distinguish true care gaps'), but is dominated by one primary verb rather than a comprehensive list of distinct actions.	4 / 5
Completeness	Explicitly answers both 'what' ('Extracts concrete unmet clinical needs...') and 'when' ('Use this skill when a user wants to turn broad medical research value into specific clinical pain points such as...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Strong natural keyword coverage including 'unmet clinical needs', 'clinical pain points', 'weak early detection', 'poor risk stratification', 'monitoring gaps', 'diagnostic delay', 'undertreatment', 'overtreatment', and 'implementation failure', though it lacks simpler lay phrasings a user might say like 'where care fails'.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (unmet-clinical-need extraction) with domain-specific triggers unlikely to fire for unrelated skills; minimal conflict risk.	5 / 5
	Total	18 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: aipoch/medical-research-skills
Path: awesome-med-research-skills/Evidence Insight/unmet-clinical-need-extractor/SKILL.md
Commit: 63c61d3

Reviewed: 4 hours ago

Table of Contents

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