check-reporting

Files (82)

• SKILL.md
• LICENSES.md · references/
• METRICS.md · references/appraisal_tools/
• METRICS_RELOADED.md · references/appraisal_tools/
• AMSTAR2.md · references/checklists/
• ARRIVE_2.md · references/checklists/
• CARE.md · references/checklists/
• CHEERS_2022.md · references/checklists/
• CLAIM_2024.md · references/checklists/
• CLEAR.md · references/checklists/
• CONSORT.md · references/checklists/
• CONSORT_AI.md · references/checklists/
• COREQ.md · references/checklists/
• COSMIN_RoB.md · references/checklists/
• CROSS.md · references/checklists/
• DECIDE_AI.md · references/checklists/
• GATHER.md · references/checklists/
• GRRAS.md · references/checklists/
• MI_CLEAR_LLM.md · references/checklists/
• MOOSE.md · references/checklists/
• NOS.md · references/checklists/
• PGS_RS.md · references/checklists/
• PRISMA_2020.md · references/checklists/
• PRISMA_2020_Abstracts.md · references/checklists/
• PRISMA_DTA.md · references/checklists/
• PRISMA_P.md · references/checklists/
• PRISMA_ScR.md · references/checklists/
• PROBAST.md · references/checklists/
• PROBAST_AI.md · references/checklists/
• QUADAS2.md · references/checklists/
• QUADAS3.md · references/checklists/
• QUADAS_C.md · references/checklists/
• RECORD.md · references/checklists/
• REMARK.md · references/checklists/
• ROBINS_E.md · references/checklists/
• ROBINS_I.md · references/checklists/
• ROBIS.md · references/checklists/
• ROB_ME.md · references/checklists/
• RoB2.md · references/checklists/
• RoB_NMA.md · references/checklists/
• SPIRIT.md · references/checklists/
• SPIRIT_AI.md · references/checklists/
• SQUIRE_2.md · references/checklists/
• SRQR.md · references/checklists/
• STARD.md · references/checklists/
• STARD_AI.md · references/checklists/
• STROBE.md · references/checklists/
• STROBE_MR.md · references/checklists/
• SWiM.md · references/checklists/
• TARGET.md · references/checklists/
• TRIPOD.md · references/checklists/
• TRIPOD_AI.md · references/checklists/
• TRIPOD_LLM.md · references/checklists/
• critical_item_floor.md · references/
• genai_image_study_object_decision_aid.md · references/
• report_templates.md · references/
• step4c_registration_timing.md · references/
• step4d_prisma_figure_audit.md · references/
• check_checklist_exists.py · scripts/
• check_checklist_version.py · scripts/
• …and 22 more

SKILL.md (first lines)

# 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:

```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 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 model (development/validation) | TRIPOD | TRIPOD+AI |
| Polygenic (risk) score prediction study | PGS-RS (with TRIPOD / TRIPOD+AI) | -- |
| Prognostic tumor-marker / biomarker study (single or multiple markers; e.g., ctDNA / molecular residual disease) | REMARK (pair with STROBE for the observational-design items; TRIPOD / TRIPOD+AI if a prognostic model is developed) | -- |
| Causal / comparative-effectiveness question emulated on observational data (treatment vs treatment, screening vs none, drug A vs B on registry / EHR / claims data) | TARGET (pair with the /design-study target-trial-emulation module for design; RECORD / STROBE for the routinely-collected-data items) | -- |
| Health-estimate / burden-of-disease modeling study (GBD or GBD-satellite, comparative-risk / population-attributable-fraction, cause-of-death or prevalence/incidence estimation, with or without forecasts) | GATHER (pair with `/analyze-stats` burden-decomposition-forecasting guide for the analytic layer) | -- |
| Systematic review / meta-analysis | PRISMA 2020 | PRISMA 2020 for Abstracts (run on the abstract, scored separately) |
| DTA systematic review / meta-analysis | PRISMA-DTA | PRISMA 2020 for Abstracts (run on the abstract, scored separately) |
| Meta-analysis of observational studies | MOOSE | PRISMA 2020 (use both) |
| Risk of bias (DTA studies) | **QUADAS-3** (current recommended version) | QUADAS-2 only when appraising or reproducing a review that used it |
| Risk of bias (RCTs) | RoB 2 | -- |
| Risk of bias (non-randomised intervention studies) | ROBINS-I | -- |
| Risk of bias (non-randomised exposure studies) | ROBINS-E | -- |
| Risk of bias (comparative DTA studies) | QUADAS-C | **QUADAS-3** (use both; apply the E&E's adaptation — see *Using QUADAS-C with QUADAS-3* in `QUADAS3.md`) |
| Risk of bias (prediction models) | PROBAST | PROBAST+AI |
| Risk of bias (systematic reviews) | ROBIS | AMSTAR 2 |
| Risk of bias (missing evidence in MA) | ROB-ME | -- |
| Risk of bias (network meta-analysis) | RoB NMA | -- |
| Risk of bias (measurement properties) | COSMIN RoB | -- |
| Quality assessment (observational) | NOS | -- |
| Case report | CARE | -- |
| Study protocol | SPIRIT 2025 | SPIRIT-AI |
| Animal study | ARRIVE 2.0 | -- |

From Aperivue/medsci-skills, MIT.

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