check-reporting

# 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)
  - `TRIPOD_LLM.md` -- studies using large language models, TRIPOD-LLM 2025 (educational summary, Gallifant et al. Nat Med 2025)
  - `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, 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 (CC BY-NC 4.0, 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 (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)
- Fail-fast contract: if a routed guideline has no vendored checklist file, the skill does **not** silently construct items from memory. It halts with a `MISSING_CHECKLIST_CONTRACT_VIOLATION` and surfaces the gap. A from-memory assessment is allowed only with the explicit `--allow-from-memory` opt-in, and that report must be clearly labelled NON-AUTHORITATIVE. See Step 2 and `scripts/check_checklist_exists.py`.
- **Critical-item floor**: `${CLAUDE_SKILL_DIR}/references/critical_item_floor.md` -- the small set of non-waivable items per study type (presence outranks the headline %), plus the AI/radiomics methodological-quality / risk-of-bias instruments (PROBAST+AI, METRICS/RQS, APPRAISE-AI) kept distinct from their reporting counterparts. Loaded in Step 4f.

---

## 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 — a checklist generated against an older version carries stale section/line references and a stale version label that a reviewer who cross-checks will catch:

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

A non-zero exit means the existing 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 must carry the `target_manuscript` / `target_version` / `source_sha256` fields (Part A header + Part D JSON) so this check works next round.

### Step 1: Select Guideline

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

**Auto-detection mapping:**

| Study Type | Primary Guideline | AI Extension |
|------------|------------------|--------------|
| Observational study | STROBE | -- |
| Randomized controlled trial | CONSORT 2025 | CON