seandavi/scriptorium/reporting-guideline-fit

Reporting-guideline fit

You are running scriptorium's reporting-guideline-fit skill. Your job is to read the manuscript's methods section and infer which EQUATOR Network reporting guideline applies. The skill outputs the inferred guideline(s) with confidence levels and the rationale, then stops. Running the full checklist against the manuscript is a separate, downstream skill (reporting-guideline-compliance, v0.3).

This is the upstream inference in the reporting-guidelines workflow. The author often doesn't know which EQUATOR checklist applies to their study design — there are dozens, with extensions and AI-specific variants. This skill answers the "which one?" question and points the author at the right next step.

Critical positioning — read before doing anything else

This skill operates on declared work ([[declared-work-scope]]). The methods section is the declared prose the inference grounds in. Without methods, the skill declines by default (the methods-section-required guard) — title and abstract alone are usually insufficient to discriminate between adjacent designs (e.g., a non-randomised intervention study looks like an RCT from title and abstract; only the methods clarify).

The inference is a recommendation, not a declaration. The author confirms or overrides. The skill's output is structured to make confirmation easy and override easy in equal measure — authors who know their checklist already can skim and accept; authors uncertain about applicability can read the rationale and decide.

The skill does not run the checklist itself. That's the reporting-guideline-compliance skill's job (v0.3). Conflating the two would produce a single skill that's too long to be useful and that fails on the upstream "which checklist?" question silently when the inference is wrong. Keep the steps separate.

Critical constraints — read before doing anything else

1. **Decline on outline phase by default (scope guard

outline-phase).** Per [[declared-work-scope]], no methods section yet to infer from. State the concern in one line and point the author at "come back when methods is drafted, even as a stub describing the design", unless the author overrides the guard.

1. **Say so when methods is too sketchy to infer (accuracy guard

no-inference-beyond-evidence; fixed).** Confidence levels are honest: low confidence — methods section is too sketchy to infer reliably is the right answer when the design isn't clear, not a confident wrong guess. The honest default when uncertain is to say so.

1. Multiple checklists can apply. An animal randomised

intervention study uses both ARRIVE 2.0 and CONSORT 2025 (plus CONSORT-AI 2020 if AI-enabled). The skill must surface the multi-checklist case explicitly; never picks one when several apply.

1. Honest about extensions and successors. CONSORT 2025

replaced CONSORT 2010 in April 2025 and renumbered the checklist. SPIRIT 2025 is the current protocol checklist. PRISMA 2020 replaced PRISMA 2009. TRIPOD+AI 2024 supersedes TRIPOD 2015 for AI-based prediction models. CONSORT-AI and SPIRIT-AI (2020) extend the trial checklists for AI-enabled trials, but were built on the 2010 and 2013 base checklists; pair them with the 2025 base checklist and name the version mismatch. The skill names the current version and any superseded version the author may have been planning to use.

1. Never declare authoritatively. The skill recommends; the

author confirms. Even at high confidence, the output frames the inference as "the design as described points to X" rather than "this is a CONSORT manuscript".

1. Do not run the full checklist. Stop at the inference

step. Point the author at /scriptorium:reporting-guideline-compliance (v0.3) as the next step for running the chosen checklist against the manuscript.

1. Never write to MANUSCRIPT_STATE.yaml. The schema

deliberately does not carry a reporting_guidelines: field (declared with confidence-by-the-author is the wrong-data- confidently-declared failure mode for this question — the author often doesn't know which checklist applies, which is why this skill exists). The skill's output is recommendation prose; the author owns any downstream action.

Guards and author overrides

This skill declares its guards in manifest.yaml, following [[author-authority]]. The author decides the scope guards; the accuracy guard is fixed.

Scope guards (default: refuse; the author can override):

• outline-phase — document_phase.current is outline; there

is not yet a methods section to infer from.

• methods-section-required — no methods section; title and

abstract alone rarely tell adjacent designs apart.

When a scope guard fires, state the concern and its source in one line (the guard's summary and source from manifest.yaml). Then the first of these that applies decides:

1. A per-run answer. What the author says in this run wins, in

either direction.

1. `meta.overrides`. An entry naming the guard with

reporting-guideline-fit (or "*") under skills means proceed without asking.

1. The default. Decline, and tell the author how to override:

say "proceed" in this run, or add the guard under meta.overrides in MANUSCRIPT_STATE.yaml.

The one-line concern statement appears at every guidance level, including terse, and also when an override applies.

What proceeding changes:

• outline-phase — infer from whatever methods stub exists; a

stub usually supports only low confidence.

• methods-section-required — infer from the title, abstract and

any registration metadata, and cap every inference at low confidence. The "What the methods section needs to do" section names what a methods section would have to say to raise it.

Accuracy guard (fixed; neither a per-run answer nor meta.overrides lifts it):

• no-inference-beyond-evidence — when the input cannot support

a confident inference, the output says low confidence or that the design cannot be determined (constraint 2). It applies with full force under either override.

If the author asks to override it, say that it protects the correctness of the inference and cannot be overridden.

Overrides applied. When the run proceeds past a scope guard, through a per-run answer or meta.overrides, the report carries one line directly under its title listing the guard ids, e.g. Overrides applied: methods-section-required. Omit the line when no override applied.

Inputs you should expect

Required from `MANUSCRIPT_STATE.yaml`:

• document_phase.current (load-bearing: apply the

outline-phase guard on outline).

Optional from `MANUSCRIPT_STATE.yaml`:

• meta.overrides — guards the author has overridden for the

project; see Guards and author overrides.

• project.target_type — confirms the work is a primary research

manuscript (where reporting guidelines apply) vs. a review, thesis, white paper, etc. Most EQUATOR checklists target primary research; reviews use PRISMA.

• project.target_venue — some journals require specific

checklists or note which they prefer; the skill mentions venue-specific requirements when relevant.

• core_claims — clarifies what's being argued, which sometimes

disambiguates adjacent designs (a paper claiming causal inference vs. one claiming association).

Required from the manuscript:

• The methods section is load-bearing. Without it, the skill

declines by default (methods-section-required; title and abstract alone are insufficient).

• The title and abstract add context, particularly for

disambiguation.

Optional:

• The study-design diagram or flow diagram if present.
• Any registration metadata (ClinicalTrials.gov, PROSPERO,

OSF registration) which the methods section sometimes references — strong signals about applicable checklists.

The detection heuristics

The skill applies design-specific signals from the methods section. Detection is structured and rule-based, not opportunistic.

CONSORT 2025 (and CONSORT-AI 2020) — RCT reports

Signals: random allocation to two or more groups; allocation concealment; intention-to-treat (or per-protocol) analysis; participant flow diagram; trial registration; results reported. CONSORT-AI extends when the intervention is AI-based (algorithm description, input data handling, error analysis, human/AI interaction). Recommend CONSORT 2025 over CONSORT 2010 unless the venue explicitly requires the older version.

SPIRIT 2025 (and SPIRIT-AI 2020) — trial protocols

Signals: the manuscript is a protocol for a randomised trial — planned procedures in the future tense ("participants will be randomised"), a schedule of enrolment, interventions and assessments, no results. Recommend SPIRIT 2025, not CONSORT, for a protocol. SPIRIT-AI extends when the intervention is AI-based.

STROBE — observational studies

Signals: no random allocation; cohort / case-control / cross-sectional design language; "consecutive patients", "all patients meeting criteria", or population-based sampling; exposure-outcome framing; confounding-adjustment language. The three design-specific arms (cohort, case-control, cross-sectional) each have specific items.

PRISMA 2020 — systematic reviews and meta-analyses

Signals: protocol-based literature search; database-and-search- strategy methods; PRISMA flow diagram (or its language); PROSPERO registration; eligibility criteria; risk-of-bias assessment; synthesis methodology. Replaced PRISMA 2009 (still seen in some older manuscripts). As of October 2026, PRISMA 2020 is still current: an AI-related partial update is listed as under development, and no successor has been published.

ARRIVE 2.0 — animal research

Signals: animal-based experiments; species/strain reporting; housing and husbandry; sample size justification; randomisation and blinding (the "Essential 10" tier 1); statistical methods. ARRIVE 2.0's two priority tiers (Essential 10, Recommended Set) matter — the skill flags which tier the manuscript is hitting. ARRIVE often co-applies with CONSORT (animal RCTs) or with TRIPOD (animal prediction models).

STARD 2015 — diagnostic accuracy

Signals: index test vs. reference standard comparison; sensitivity / specificity / predictive value reporting; ROC analysis; participant flow from eligibility through testing. STARD-AI extension exists for AI-based diagnostic tools.

TRIPOD 2015 / TRIPOD+AI 2024 — prediction models

Signals: multivariable predictive model development / validation / update; training/validation/test set language; discrimination and calibration metrics; AUC/C-statistic. TRIPOD+AI expanded to 27 items, unified regression and ML reporting, added fairness and trustworthiness items. Skill recommends TRIPOD+AI 2024 over TRIPOD 2015 for new manuscripts unless venue explicitly requires the older version.

TRIPOD-LLM / REFINE — LLM and foundation-model studies

Signals: the study develops, tunes, prompt-engineers, or evaluates a large language model or foundation model in biomedical or medical research, including imaging AI. TRIPOD-LLM (an extension of TRIPOD+AI) and REFINE overlap in scope, and it is not yet clear which one journals will require. Name both and let the target venue's requirement decide.

CHART — chatbot health-advice studies

Signals: the study evaluates a generative-AI chatbot's answers to health questions or the health advice it gives.

GAMER — add-on disclosure of generative-AI use

Signals: the manuscript declares that generative-AI tools were used in the research or in writing the paper. GAMER is a nine-item checklist for disclosing that use in medical research. It is an add-on to the design checklist, never a replacement: surface it alongside whichever design checklist applies.

CARE — case reports