CtrlK
BlogDocsLog inGet started
Tessl Logo

clinical-decision-support

Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.

52

Quality

58%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./.claude/skills/clinical-decision-support/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

55%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is comprehensive and clinically detailed with a sensible bundle structure (real references, templates, and most scripts), but it suffers from repetition of the mandatory-first-page requirement, a fabricated scripts list, and a missing concrete build→validate→retry workflow. It reads as a thorough specification rather than lean, executable guidance.

Suggestions

Remove the four-fold repetition of the mandatory page-1 executive-summary rule; state it once in 'Document Structure' and reference it from 'Output Format' and 'Best Practices'.

Fix the 'Scripts' section: delete the four nonexistent files (create_waterfall_plot.py, create_forest_plot.py, calculate_statistics.py, grade_evidence.py) and add an example invocation for each real script.

Add a concrete end-to-end workflow with a validation checkpoint, e.g. fill template → run scripts → `python scripts/validate_cds_document.py` → fix and re-validate → compile to PDF.

DimensionReasoningScore

Conciseness

The ~510-line body is mostly domain-useful but padded: the page-1 executive-summary structure and LaTeX tcolorbox example are repeated across the 'Document Structure', 'Output Format', and 'Best Practices' sections (the mandatory-first-page requirement is stated four times), which is verbosity even though accurate.

2 / 3

Actionability

It gives concrete LaTeX structure and one real executable command (`python scripts/generate_schematic.py`), but the 'Scripts' section names 6 nonexistent files (create_waterfall_plot.py, create_forest_plot.py, calculate_statistics.py, grade_evidence.py) and never shows how to invoke the real analysis scripts, so guidance is partly fabricated and incomplete rather than copy-paste ready.

2 / 3

Workflow Clarity

A multi-step document-generation flow is implied (executive summary → TOC → detailed sections → figures) and validation is mentioned via `validate_cds_document.py`, but there is no sequenced build/validate/retry workflow with checkpoints — the validation script is only listed, never wired into a validate→fix→revalidate loop, capping clarity at 2.

2 / 3

Progressive Disclosure

Content is appropriately split into real `references/`, `assets/`, and `scripts/` bundles signaled by 'See the references/ directory', but navigation is generic rather than per-file, the templates section lists files correctly while the scripts section lists nonexistent ones, and large inline blocks (full LaTeX example, repeated best-practices) that belong in references sit in the body.

2 / 3

Total

8

/

12

Passed

Description

62%

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 rich with concrete, domain-specific capabilities but is dense with jargon and lacks an explicit 'Use when...' trigger clause, weakening its completeness and natural-trigger quality. It is clearly framed for pharmaceutical/research use but risks overlapping with adjacent clinical skills without sharper triggers.

Suggestions

Add an explicit trigger clause such as 'Use when generating cohort analyses, evidence-based treatment guidelines, or GRADE-graded recommendation reports for clinical research — not for individual bedside treatment plans.'

Soften the technical-jargon density (e.g. 'biomarker-stratified', 'hazard ratios', 'waterfall plots') or pair each with the plain-language term a user would actually say.

State the distinguishing trigger more directly (e.g. 'for population-level cohort and guideline documents') to reduce overlap with the treatment-plans skill.

DimensionReasoningScore

Specificity

The description lists many concrete actions — 'patient cohort analyses (biomarker-stratified with outcomes)', 'treatment recommendation reports (evidence-based guidelines with decision algorithms)', 'GRADE evidence grading', 'statistical analysis (hazard ratios, survival curves, waterfall plots)' — matching the top anchor for multiple specific concrete actions.

3 / 3

Completeness

It thoroughly answers 'what' the skill does but has no explicit 'Use when...' trigger clause; the 'when' is only implied by the listed capabilities, which caps completeness at 2 per the rubric guideline.

2 / 3

Trigger Term Quality

It includes some terms a user might say ('treatment recommendation reports', 'cohort analyses', 'GRADE') but leans on technical jargon ('biomarker-stratified', 'hazard ratios', 'waterfall plots', 'regulatory compliance') and omits common phrasings like 'clinical guidelines', 'clinical decision support', or 'evidence-based guidelines' as natural triggers, so it is missing common variations.

2 / 3

Distinctiveness Conflict Risk

The pharmaceutical/research framing and the explicit split from bedside 'treatment-plans' helps, but 'clinical decision support documents' and 'treatment recommendations' are broad enough to overlap with a treatment-plans or clinical-reports skill without an explicit trigger, leaving it somewhat specific but still potentially overlapping.

2 / 3

Total

9

/

12

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (510 lines); consider splitting into references/ and linking

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

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

Warning

Total

13

/

16

Passed

Repository
K-Dense-AI/claude-scientific-writer
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.