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/check-reporting

Use when auditing a manuscript item by item against a reporting guideline or risk-of-bias tool. Covers 49 reporting guidelines and risk-of-bias tools (STROBE, CONSORT, STARD, TRIPOD+AI, PRISMA, QUADAS and more), marking each item PRESENT, PARTIAL or MISSING. Not a reviewer

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medsci-skills
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Install
$ npx -y skills add Aperivue/medsci-skills --skill check-reporting --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/check-reporting

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when auditing a manuscript item by item against a reporting guideline or risk-of-bias tool. Covers 49 reporting guidelines and risk-of-bias tools (STROBE, CONSORT, STARD, TRIPOD+AI, PRISMA, QUADAS and more), marking each item PRESENT, PARTIAL or MISSING. Not a reviewer

SKILL.md

check-reporting.SKILL.md
name: check-reporting
description: Use when auditing a manuscript item by item against a reporting guideline or risk-of-bias tool. Covers 49 reporting guidelines and risk-of-bias tools (STROBE, CONSORT, STARD, TRIPOD+AI, PRISMA, QUADAS and more), marking each item PRESENT, PARTIAL or MISSING. Not a reviewer critique (/self-review).
metadata:
  triggers: "checklist, QUADAS-3, abstract checklist, structured abstract, reporting guideline, STROBE, STROBE-MR, Mendelian randomization, CONSORT, CONSORT-AI, STARD, STARD-AI, TRIPOD, TRIPOD-LLM, PGS-RS, PRS-RS, polygenic risk score, polygenic score, PRISMA, PRISMA-DTA, PRISMA-P, PRISMA-ScR, scoping review, scoping, evidence map, ARRIVE, CARE, CLAIM, DECIDE-AI, MI-CLEAR-LLM, SPIRIT, SPIRIT-AI, QUADAS, QUADAS-C, RoB, ROBINS, ROBINS-E, ROBIS, ROB-ME, PROBAST, NOS, COSMIN, AMSTAR, SWiM, CHEERS, economic evaluation, cost-effectiveness, cost-utility, QALY, ICER, RECORD, RECORD-PE, routinely-collected data, registry, claims, electronic health records, EHR, real-world data, CROSS, CHERRIES, survey, questionnaire, KAP, e-survey, response rate, SRQR, COREQ, qualitative research, interviews, focus groups, thematic analysis, grounded theory, reflexivity, REMARK, tumor marker, prognostic marker, prognostic biomarker, molecular residual disease, TARGET, target trial emulation, target trial, causal inference, estimand, immortal time bias, GATHER, burden of disease, global burden, GBD, health estimates, attributable burden, comparative risk assessment, population attributable fraction, disability-adjusted life years, DALY, forecasting, decomposition, risk of bias, compliance check, LLM accuracy, large language model, clinical deployment"

Check-Reporting Skill

Reference Files

Checklists are vendored under `${CLAUDE_SKILL_DIR}/references/checklists/`, one file per instrument. Each file's header gives its version, source citation and licence, and says when its item text is an own-words summary rather than the published wording.

  • `STROBE.md` -- STROBE
  • `STROBE_MR.md` -- STROBE-MR 2021
  • `RECORD.md` -- RECORD 2015 (RECORD-PE for drug studies)
  • `REMARK.md` -- REMARK
  • `TARGET.md` -- TARGET 2025
  • `GATHER.md` -- GATHER 2016
  • `CHEERS_2022.md` -- CHEERS 2022
  • `CROSS.md` -- CROSS 2021 + CHERRIES (internet surveys)
  • `SRQR.md` -- SRQR 2014 (all qualitative approaches)
  • `COREQ.md` -- COREQ 2007 (interviews and focus groups)
  • `STARD.md` -- STARD 2015
  • `STARD_AI.md` -- STARD-AI 2025
  • `TRIPOD.md` -- TRIPOD 2015
  • `TRIPOD_AI.md` -- TRIPOD+AI 2024
  • `TRIPOD_LLM.md` -- TRIPOD-LLM 2025
  • `PGS_RS.md` -- PGS-RS / PRS-RS 2021
  • `CONSORT.md` -- CONSORT 2025
  • `CONSORT_AI.md` -- CONSORT-AI 2020
  • `SPIRIT.md` -- SPIRIT 2025
  • `SPIRIT_AI.md` -- SPIRIT-AI 2020
  • `CLAIM_2024.md` -- CLAIM 2024
  • `DECIDE_AI.md` -- DECIDE-AI 2022
  • `MI_CLEAR_LLM.md` -- MI-CLEAR-LLM
  • `CLEAR.md` -- CLEAR (radiomics)
  • `ARRIVE_2.md` -- ARRIVE 2.0
  • `CARE.md` -- CARE 2013
  • `SQUIRE_2.md` -- SQUIRE 2.0
  • `GRRAS.md` -- GRRAS
  • `PRISMA_2020.md` -- PRISMA 2020
  • `PRISMA_2020_Abstracts.md` -- PRISMA 2020 for Abstracts, 12 items. A separate instrument, not a subset of the 27-item checklist (main item 2 defers to it): score it with its own denominator.
  • `PRISMA_DTA.md` -- PRISMA-DTA
  • `PRISMA_P.md` -- PRISMA-P
  • `PRISMA_ScR.md` -- PRISMA-ScR
  • `MOOSE.md` -- MOOSE
  • `SWiM.md` -- SWiM
  • `AMSTAR2.md` -- AMSTAR 2
  • `QUADAS3.md` -- QUADAS-3
  • `QUADAS2.md` -- QUADAS-2
  • `QUADAS_C.md` -- QUADAS-C
  • `RoB2.md` -- RoB 2
  • `ROBINS_I.md` -- ROBINS-I
  • `ROBINS_E.md` -- ROBINS-E
  • `ROBIS.md` -- ROBIS
  • `ROB_ME.md` -- ROB-ME
  • `RoB_NMA.md` -- RoB NMA
  • `PROBAST.md` -- PROBAST
  • `PROBAST_AI.md` -- PROBAST+AI
  • `NOS.md` -- NOS
  • `COSMIN_RoB.md` -- COSMIN RoB

---

Workflow

Step 0: Existing-checklist staleness pre-check

If a checklist already exists for this project (`qc/reporting_checklist.json` or a prior `.md` report), verify it targets the **current** manuscript before reusing it — one generated against an older version carries stale section/line references and a stale version label:

python3 "${CLAUDE_SKILL_DIR}/scripts/check_checklist_version.py" \
  --checklist qc/reporting_checklist.json --manuscript manuscript_v8.md

A non-zero exit means the checklist is stale (older `target_version`, changed `source_sha256`, different `target_manuscript`) or pre-dates the version contract: regenerate it against the current manuscript (Steps 1–5) rather than reusing it. Every report you generate carries the `target_manuscript` / `target_version` / `source_sha256` fields (Part A header + Part D JSON) so this check works next round.

Step 1: Select Guideline

Use the guideline the user names; otherwise auto-detect it from the study type. If the study type is ambiguous, ask the user to confirm before selecting a guideline.

**Auto-detection mapping:**

| Study Type | Primary Guideline | AI Extension | |------------|------------------|--------------| | Observational study | STROBE | -- | | Mendelian randomization study | STROBE-MR (base STROBE + MR extension) | -- | | Health economic evaluation (cost-effectiveness / cost-utility / cost-benefit / budget-impact) | CHEERS 2022 | -- | | Observational study using routinely-collected data (claims / EHR / registry / health-checkup DB) | RECORD (base STROBE + RECORD extension; RECORD-PE for drug studies) | -- | | Survey / questionnaire study (KAP, physician/patient, cross-sectional, e-survey) | CROSS (+ CHERRIES for internet surveys) | -- | | Scoping review (maps breadth/nature of evidence, clarifies concepts, identifies gaps — not a focused effectiveness/accuracy question) | PRISMA-ScR (base PRISMA + scoping-review extension) | -- | | Qualitative study (interviews, focus groups, ethnography, grounded theory, phenomenology, document analysis) | SRQR (all qualitative approaches); COREQ (interviews/focus groups specifically) | -- | | Randomized controlled trial | CONSORT 2025 | CONSORT-AI | | Diagnostic accuracy study | STARD 2015 | STARD-AI | | Prediction

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Created & maintained by Yoojin Nam, MD Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea MedSci Skills is a set of Agent Skills for clinical research: literature

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