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Scar literature review

Skill KangjieShen/openscar-starter/skills/research/scar-literature-review

AI-native scar and keloid phenotyping framework with registry schema, synthetic mock data, validation scripts, and clinician-reviewable workflow skills

Install
npx -y skills add KangjieShen/openscar-starter --skill scar-literature-review

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What its author says it does

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Produce evidence-aware literature briefs for scar and keloid imaging, recurrence, treatment response, and fibrosis biology.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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Scar Literature Review

Description

Use this skill to structure literature review briefs for scar and keloid research. It supports evidence mapping, citation planning, and OpenScar manuscript preparation. It is not a formal systematic review by itself and does not provide medical advice.

When to Use

  • A user asks for evidence on scar imaging, recurrence prediction, treatment response, or fibrosis biology.
  • A team wants to expand docs/literature-seed.md.
  • A manuscript section needs a structured evidence table.
  • A research question needs PICO-style framing.

Do Not Use

  • Do not cite sources that have not been directly verified.
  • Do not overstate evidence strength.
  • Do not turn literature summaries into patient-specific treatment recommendations.
  • Do not ignore follow-up duration, recurrence definition, or external validation status.

Required Inputs

  • Research question.
  • Target bucket: imaging, recurrence prediction, single-cell/spatial biology, treatment response, or registry design.
  • Population and scar type.
  • Outcome of interest.
  • Citation requirements and date range, if any.

Workflow

  1. Define the research question and scope.
  2. Choose search buckets from docs/literature-seed.md.
  3. Draft search strings for PubMed, Google Scholar, clinical trial registries, or guidelines.
  4. Extract study metadata:
    • study type
    • cohort size
    • scar type
    • modality
    • treatment or exposure
    • outcome definition
    • follow-up duration
    • external validation
  5. Grade evidence conservatively by design, sample size, bias, reproducibility, and clinical relevance.
  6. Summarize findings, gaps, and implications for OpenScar schema or skills.
  7. List citations to verify before public release.

Output Format

  • Research question
  • Search strategy
  • Inclusion and exclusion notes
  • Evidence table
  • Summary of findings
  • Safety and adverse-event findings
  • Evidence gaps
  • OpenScar implications
  • Citation verification checklist

Safety and Compliance

This skill supports documentation, research preparation, and clinician review. It does not diagnose, prescribe, determine recurrence, or replace licensed clinical judgment. Escalate urgent patient-specific symptoms outside the literature workflow. Clearly separate literature summary from medical advice.

Examples

Create an evidence brief on machine learning models for keloid recurrence prediction, including cohort size, recurrence definitions, and validation status.

Integration

Works well with recurrence-risk-factor-extraction, scar-treatment-response-review, scar-registry-entry-builder, and aesthetic-literature-review.

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