check-reporting
The check-reporting skill audits medical manuscripts against 32 evidence-based reporting guidelines spanning study types from randomized trials (CONSORT) and systematic reviews (PRISMA) to specialized domains like AI diagnostics (STARD-AI, CLAIM) and quality improvement (SQUIRE 2.0). It generates item-by-item compliance assessments marking each guideline element as PRESENT, MISSING, or PARTIAL, producing publication-ready reports suitable for journal submission.
git clone --depth 1 https://github.com/Aperivue/medsci-skills /tmp/check-reporting && cp -r /tmp/check-reporting/skills/check-reporting ~/.claude/skills/check-reportingSKILL.md
# 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 (no open licence — © ACP; 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 (CC BY 4.0, Husereau et al. BMJ 2022)
- `RECORD.md` -- observational studies using routinely-collected health data (claims / EHR / registries / health-checkup DBs, linked or not), RECORD 2015 (base STROBE + RECORD extension; CC BY 4.0, Benchimol et al. PLoS Med 2015; RECORD-PE for drug studies)
- `CROSS.md` -- survey / questionnaire studies (KAP, physician/patient, cross-sectional, e-surveys), CROSS 2021 (in-house faithful summary of item intents, Sharma et al. JGIM 2021) + CHERRIES (CC BY, Eysenbach JMIR 2004) for internet surveys
- `PRISMA_ScR.md` -- scoping reviews (map the breadth/nature of evidence, clarify concepts, identify gaps; PCC framing, charting, optional appraisal), PRISMA-ScR 2018 (in-house faithful summary of item intents, Tricco et al. Ann Intern Med 2018; DOI 10.7326/M18-0850)
- `SRQR.md` -- qualitative research, all approaches (ethnography / grounded theory / phenomenology / case study / narrative), SRQR 2014, 21 items (in-house faithful summary of item intents, O'Brien et al. Acad Med 2014; DOI 10.1097/ACM.0000000000000388)
- `COREQ.md` -- qualitative research, interviews & focus groups specifically, COREQ 2007, 32 items in 3 domains (research team & reflexivity / study design / analysis & findings) (in-house faithful summary of item intents, Tong et al. Int J Qual Health Care 2007; DOI 10.1093/intqhc/mzm042)
- `REMARK.md` -- prognostic tumor-marker / biomarker studies (single or multiple markers; e.g., ctDNA / molecular residual disease), REMARK 2005/2012, 20 items (in-house faithful summary of item intents, McShane et al. Br J Cancer 2005 + Altman et al. PLoS Med 2012)
- `TARGET.md` -- observational studies emulating a target trial (causal / comparative-effectiveness questions on routinely-collected / registry / EHR data), TARGET 2025, 21 items (in-house faithful summary of item intents, Cashin/Hansford/Hernán et al. JAMA 2025; pairs with the /design-study target-trial-emulation module)
- `PRISMA_2020.md` -- systematic reviews (CC BY)
- `PRISMA_2020_Abstracts.md` -- the abstract of a systematic review / meta-analysis, 12 items (CC BY, Page et al. BMJ 2021). A separate instrument from the 27-item checklist, not a subset: item 2 of the main checklist defers to it. Score it with its own denominator.
- `ARRIVE_2.md` -- animal studies (CC0)
- `PRISMA_DTA.md` -- DTA systematic reviews (no open licence — © AMA; McInnes et al. JAMA 2018)
- `QUADAS3.md` -- diagnostic accuracy risk of bias, **current recommended version** (no open licence -- (c) ACP; Whiting et al. Ann Intern Med 2026)
- `QUADAS2.md` -- diagnostic accuracy risk of bias (no open licence — © ACP; Whiting et al. Ann Intern Med 2011)
- `RoB2.md` -- RCT risk of bias (CC BY, Sterne et al. BMJ 2019)
- `ROBINS_I.md` -- non-randomised studies risk of bias (CC BY-**NC** 3.0 — non-commercial; Sterne et al. BMJ 2016)
- `PROBAST.md` -- prediction model risk of bias (no open licence — © ACP; Wolff et al. Ann Intern Med 2019)
- `NOS.md` -- observational study quality (public domain, Ottawa Hospital)
- `CONSORT.md` -- randomised controlled trials, CONSORT 2025 (CC BY 4.0, Hopewell et al. BMJ 2025)
- `CONSORT_AI.md` -- AI clinical-trial reports, CONSORT-AI 2020 (CC BY 4.0, Liu et al. Nat Med 2020)
- `CARE.md` -- case reports, CARE 2013 (no confirmed open licence — Elsevier TDM only; Gagnier et al. J Clin Epidemiol 2014)
- `SPIRIT.md` -- clinical trial protocols, SPIRIT 2025 (CC BY 4.0, Chan et al. BMJ 2025)
- `SPIRIT_AI.md` -- AI clinical-trial protocols, SPIRIT-AI 2020 (CC BY 4.0, Cruz Rivera et al. Nat Med 2020)
- `CLAIM_2024.md` -- AI/ML in clinical imaging, CLAIM 2024 Update (RSNA open access, Tejani et al. Radiol Artif Intell 2024)
- `DECIDE_AI.md` -- early-stage clinical evaluation of AI decision-support systems, DECIDE-AI 2022 (educational summary, CC BY-NC, Vasey et al. Nat Med 2022)
- `MI_CLEAR_LLM.md` -- LLM accuracy studies in healthcare (CC BY-NC 4.0, Park et al. KJR 2024; 2025 update)
- `SQUIRE_2.md` -- quality improvement in healthcare/education (no open licence — Crossref returns none; Ogrinc et al. BMJ Qual Saf 2016)
- `CLEAR.md` -- radiomics studies (CC BY 4.0, Kocak et al. Insights Imaging 2023)
- `MOOSE.md` -- meta-analysis of observational studies (Stroup et al. JAMA 2000)
- `GRRAS.md` -- reliability and agreement studies (Kottner et al. J Clin Epidemiol 2011)
- `QUADAS_C.md` -- comparative DTA risk of bias, extension to QUADAS-2 (no open licence — © ACP; YMedical AI paper optimization for AI search engines (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace) and RAG-based literature tools. Applies when drafting or reviewing titles, abstracts, structured summary boxes (Key Points / Research in Context / Plain-Language Summary), manuscripts for high-impact medical AI journals (Lancet Digital Health, Radiology, Radiology-AI, npj Digital Medicine, Nature Medicine), preprints (medRxiv/arXiv), GitHub README + CITATION.cff + Zenodo archives, and Hugging Face model/dataset cards. Integrates TRIPOD+AI, CLAIM 2024, STARD-AI, TRIPOD-LLM, DECIDE-AI reporting requirements with generative engine optimization (GEO) principles. Produces a visible pass/fail checklist.
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Statistical analysis for medical research papers. Generates reproducible Python/R code with publication-ready tables and figures. Supports diagnostic accuracy, inter-rater agreement, meta-analysis, survival analysis, survey data, group comparisons, regression, propensity score, and repeated measures.
PubMed author profile analysis. Author name → PubMed fetch → study-type classification → visualization → strategy report → optional trajectory-archetype classification.
Generate N analysis scripts from a single methodology template × multiple exposure/outcome combinations. The "80-person team" pattern — same validated method, swap variables only. Produces batch R/Python code + summary matrix.
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Interactive data profiling and cleaning assistant for medical research. Three-stage workflow (profile, flag, code-generate) with user approval gates at each step. Handles missing values, outliers, duplicates, and type mismatches in CSV/Excel clinical data. Does NOT auto-clean — all decisions require researcher confirmation.