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)
  • 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)
  • PRISMA_2020.md -- systematic reviews (CC BY)
  • ARRIVE_2.md -- animal studies (CC0)
  • PRISMA_DTA.md -- DTA systematic reviews (CC BY, McInnes et al. JAMA 2018)
  • QUADAS2.md -- diagnostic accuracy risk of bias (CC BY, 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, Sterne et al. BMJ 2016)
  • PROBAST.md -- prediction model risk of bias (CC BY, Wolff et al. Ann Intern Med 2019)
  • NOS.md -- observational study quality (public domain, Ottawa Hospital)
  • CONSORT.md -- randomised controlled trials
  • CARE.md -- case reports
  • SPIRIT.md -- study protocols
  • CLAIM_2024.md -- AI/ML in clinical imaging
  • 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 (CC BY, 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 (CC BY 4.0, Yang et al. 2021)
  • ROBINS_E.md -- non-randomised exposure studies risk of bias (CC BY-NC-ND 4.0, Higgins et al. Environ Int 2024)
  • ROBIS.md -- risk of bias in systematic reviews (Whiting et al. J Clin Epidemiol 2016)
  • ROB_ME.md -- risk of bias due to missing evidence in meta-analysis (CC BY-NC-ND 4.0, Page et al. BMJ 2023)
  • PROBAST_AI.md -- prediction model risk of bias, updated for AI/ML (Moons et al. BMJ 2025)
  • COSMIN_RoB.md -- reliability/measurement error risk of bias (Mokkink et al. BMC Med Res Methodol 2020)
  • RoB_NMA.md -- risk of bias in network meta-analysis (Lunny et al. 2024)
  • AMSTAR2.md -- quality of systematic reviews (Shea et al. BMJ 2017)
  • PRISMA_P.md -- systematic review protocols (Shamseer et al. BMJ 2015)
  • SWiM.md -- synthesis without meta-analysis reporting (Campbell et al. BMJ 2020)
• If a local checklist file is not found for a requested guideline, the skill constructs checklist items from its knowledge of the guideline.

Workflow

Step 1: Select Guideline

Determine the appropriate reporting guideline. Auto-detect from the manuscript type or accept user specification.

Auto-detection mapping:

Rules:

• If the study involves AI/ML, always apply the AI extension in addition to the base guideline.
  • Exception — TRIPOD: TRIPOD+AI 2024 (Collins et al., BMJ 2024) is a complete rewrite, not an addendum to TRIPOD 2015 (Moons et al., Ann Intern Med 2015). For non-AI prediction models, use TRIPOD 2015 only. For AI/ML prediction models, use TRIPOD+AI 2024 only. Do NOT apply both simultaneously.
• STARD-AI (Sounderajah et al., Nat Med 2025) extends STARD 2015 with 14 new and 4 modified items (40 total). For AI diagnostic accuracy studies, use STARD-AI (which incorporates all STARD 2015 items). Do NOT apply both STARD 2015 and STARD-AI simultaneously — STARD-AI supersedes STARD 2015 for AI studies.
• MI-CLEAR-LLM is a supplementary checklist (6 items), not a standalone reporting guideline. Always pair it with the study's primary guideline (e.g., STARD-AI for AI diagnostic accuracy, CLAIM for imaging AI). Apply MI-CLEAR-LLM whenever the study evaluates LLM accuracy as an outcome — do NOT apply it merely because the manuscript was written with LLM assistance.
• If multiple guidelines apply (e.g., a diagnostic accuracy study that is also an AI study), check against all relevant guidelines and merge into one report.
• If the user requests a specific guideline, use that one regardless of auto-detection.

Step 2: Load Checklist

1. Read the checklist file from ${CLAUDE_SKILL_DIR}/references/checklists/.
2. If the checklist file does not exist for the requested guideline, use your knowledge of the guideline to construct the checklist items and inform the user that a local checklist file was not found.

Step 3: Scan Manuscript

Read all sections of the manuscript thoroughly:

1. Title and abstract
2. Introduction
3. Methods (all subsections)
4. Results (all subsections)
5. Discussion
6. Tables, figures, and their captions
7. Supplemental materials (if available)
8. References (for registration numbers, protocol references)

Gather context from the full document before starting the item-by-item assessment.

Step 4: Assess Each Item

For every checklist item, determine:

For each item, record:

• Status: PRESENT / PARTIAL / MISSING / N/A
• Location: Section name and paragraph or approximate position (e.g., "Methods, paragraph 3")
• Notes: What was found (if PRESENT/PARTIAL) or what should be added (if MISSING)

Step 4b: Section Boundary Check

In addition to checklist i