check-reporting

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Check manuscript compliance with medical research reporting guidelines. Supports 47 guidelines including STROBE, STROBE-MR, RECORD, REMARK (prognostic tumor-marker studies), TARGET (target trial emulation), GATHER (burden-of-disease / health-estimate modeling), CONSORT, CONSORT-AI, STARD, STARD-AI, TRIPOD, TRIPOD+AI, TRIPOD-LLM, PGS-RS, ARRIVE, PRISMA, PRISMA-DTA, PRISMA-P, PRISMA-ScR (scoping reviews), CARE, SPIRIT, SPIRIT-AI, CLAIM, DECIDE-AI, MI-CLEAR-LLM, SQUIRE 2.0, CLEAR, MOOSE, GRRAS, SWiM, AMSTAR 2, CHEERS 2022, CROSS (survey studies), SRQR and COREQ (qualitative research), and risk of bias tools (QUADAS-2, QUADAS-C, RoB 2, ROBINS-I, ROBINS-E, ROBIS, ROB-ME, PROBAST, PROBAST+AI, NOS, COSMIN, RoB NMA). Generates item-by-item assessment with PRESENT/MISSING/PARTIAL status.

AI & Automation 223 stars 55 forks Updated yesterday MIT

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Quality Score: 95/100

Stars 20%
78
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# Check-Reporting Skill You are helping a medical researcher verify that their manuscript complies with the appropriate medical research reporting guideline. You perform a systematic, item-by-item audit and produce a compliance report suitable for journal submission. ## Communication Rules - Communicate with the user in their preferred language. - Checklist items and report output are in English (matching guideline originals). - Medical terminology is always in English. ## Reference Files - **Checklists (bundled, open license)**: `${CLAUDE_SKILL_DIR}/references/checklists/` - `STROBE.md` -- observational studies (CC BY) - `STROBE_MR.md` -- Mendelian randomization studies, STROBE-MR 2021 (base STROBE + MR extension; CC BY, Davey Smith et al. BMJ 2021) - `STARD.md` -- diagnostic accuracy studies (CC BY 4.0) - `STARD_AI.md` -- AI diagnostic accuracy studies (CC BY, Sounderajah et al. Nat Med 2025) - `TRIPOD.md` -- prediction models, classic 2015 version (CC BY, Moons et al. Ann Intern Med 2015) - `TRIPOD_AI.md` -- prediction models with AI/ML (CC BY 4.0, Collins et al. BMJ 2024) - `TRIPOD_LLM.md` -- studies using large language models, TRIPOD-LLM 2025 (educational summary, Gallifant et al. Nat Med 2025) - `PGS_RS.md` -- polygenic (risk) score prediction studies, PGS-RS / PRS-RS 2021 (educational summary, Wand et al. Nature 2021) - `CHEERS_2022.md` -- health economic evaluations (cost-effectiveness / cost-utility / cost-benefit / budget-impact), CHEERS 2022...

Details

Author
Aperivue
Repository
Aperivue/medsci-skills
Created
3 months ago
Last Updated
yesterday
Language
Python
License
MIT

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