git:20260520.3370c37 to git:20260520.b9d0a32

15 added, 15 removed. Audit A to A.

---

name: reporting-guideline-fit

- description: Read the manuscript's methods section (and abstract/title for context) and infer which EQUATOR Network reporting guideline applies — CONSORT for RCTs, STROBE for observational, PRISMA for systematic reviews, ARRIVE for animal research, TRIPOD/TRIPOD+AI for prediction models, STARD for diagnostic accuracy, CARE for case reports, COREQ for qualitative, with relevant AI-extensions (CONSORT-AI, SPIRIT-AI, TRIPOD+AI) when applicable. Outputs the inferred guideline(s) with confidence levels (high/moderate/low), the rationale per inference, and an explicit "other guidelines considered" section naming checklists evaluated and rejected. The author confirms or overrides; the skill never declares authoritatively. Upstream inference for the v0.3 reporting-compliance skill — this skill stops at "which checklist applies?"; running the full checklist against the manuscript is a separate skill. Refuses on outline phase; refuses cleanly when the methods section is too sketchy to infer reliably.

+ description: Read the manuscript's methods section (and abstract/title for context) and infer which EQUATOR Network reporting guideline applies — CONSORT for RCTs, STROBE for observational, PRISMA for systematic reviews, ARRIVE for animal research, TRIPOD/TRIPOD+AI for prediction models, STARD for diagnostic accuracy, CARE for case reports, COREQ for qualitative, with relevant AI-extensions (CONSORT-AI, SPIRIT-AI, TRIPOD+AI) when applicable. Outputs the inferred guideline(s) with confidence levels (high/moderate/low), the rationale per inference, and an explicit "other guidelines considered" section naming checklists evaluated and rejected. The author confirms or overrides; the skill never declares authoritatively. Upstream inference for the v0.3 reporting-guideline-compliance skill — this skill stops at "which checklist applies?"; running the full checklist against the manuscript is a separate skill. Refuses on outline phase; refuses cleanly when the methods section is too sketchy to infer reliably.

grounding:

- knowledge/conventions/guidance-level.md

- knowledge/conventions/declared-work-scope.md

- knowledge/scientific-writing/reporting-guidelines.md

---

# 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-compliance`,

+ manuscript is a separate, downstream skill (`reporting-guideline-compliance`,

planned for 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 refuses

— 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-compliance` skill's job (planned for v0.3). Conflating

+ `reporting-guideline-compliance` skill's job (planned for 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. **Refuse on `outline` phase.** Per [[declared-work-scope]],

no methods section yet to infer from. Refuse cleanly and

point the author at "come back when methods is drafted, even

as a stub describing the design".

2. **Refuse when methods is too sketchy to infer.** 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.

3. **Multiple checklists can apply.** An animal randomised

intervention study uses both ARRIVE 2.0 and CONSORT (or

CONSORT-AI if AI-enabled). The skill must surface the

multi-checklist case explicitly; never picks one when several

apply.

4. **Honest about extensions and successors.** PRISMA 2020

replaced PRISMA 2009. TRIPOD+AI 2024 supersedes TRIPOD 2015

for AI-based prediction models. CONSORT-AI extends CONSORT

for AI-enabled trials. The skill names the current version

and any superseded version the author may have been planning

to use.

5. **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".

6. **Do not run the full checklist.** Stop at the inference

- step. Point the author at `/scriptorium:reporting-compliance`

+ step. Point the author at `/scriptorium:reporting-guideline-compliance`

(v0.3) as the next step for running the chosen checklist

against the manuscript.

7. **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.

## Inputs you should expect

**Required from `MANUSCRIPT_STATE.yaml`:**

- `document_phase.current` (load-bearing: refuse on `outline`).

**Optional from `MANUSCRIPT_STATE.yaml`:**

- `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

refuses (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 2010 (and CONSORT-AI 2020) — RCTs

Signals: random allocation to two or more groups; allocation

concealment; intention-to-treat (or per-protocol) analysis;

participant flow diagram; trial registration. CONSORT-AI extends

when the intervention is AI-based (algorithm description, input

data handling, error analysis, human/AI interaction).

### 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).

### 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.

### CARE — case reports

Signals: single-patient or small-case-series narrative;

timeline / diagnostic-process description; outcomes following

intervention; informed-consent statement specific to case

reporting.

### COREQ — qualitative research

Signals: qualitative methodology (interviews, focus groups,

observational); thematic / framework / grounded-theory

analysis; researcher reflexivity; data-saturation language.

COREQ has 32 items across research-team-and-reflexivity, study

design, and analysis-and-findings.

### CHEERS 2022 — health economic evaluations

Signals: cost-effectiveness / cost-utility / cost-benefit

analysis; QALY / DALY measurement; sensitivity analysis on

economic parameters.

### Reviews and editorials — usually no EQUATOR checklist

Signals: narrative review of literature without systematic

methodology; editorial commentary; opinion / perspective piece.

The skill names this case explicitly — "no EQUATOR checklist

applies; this is a [narrative review / editorial]" — rather

than picking a wrong checklist.

### Mixed and unclear cases

When the design genuinely spans categories (an RCT with

embedded qualitative interviews; a systematic review with a

small meta-analysis sub-component), the skill names all

applicable checklists and notes the multi-checklist

applicability is intentional, not a confusion.

## Conversational style

Read `meta.guidance_level` from `MANUSCRIPT_STATE.yaml` (default

`standard` if absent). Adapt framing per [[guidance-level]]:

- `terse` — open with one line ("running reporting-guideline-fit

inference"); emit the markdown report; no closing summary.

- `standard` — open with one sentence naming the methods-section

signal strength (clear / moderately clear / sketchy) and the

count of applicable checklists; close with the recommended

- next step (`/scriptorium:reporting-compliance` against the

+ next step (`/scriptorium:reporting-guideline-compliance` against the

chosen checklist, planned v0.3).

- `full` — open with what reporting guidelines do (standardise

reporting so reviewers and readers can evaluate methodology

consistently — the EQUATOR Network maintains the registry of

~500 active guidelines), why the inference matters (authors

often don't know which checklist applies to their study

design, especially across the AI-extension landscape), and

the major design-to-checklist mapping. If first invocation

this session, also offer

`/scriptorium:explain reporting-guideline-fit`.

Run the signal-based check-in once if appropriate. The

structured output is unchanged across levels — only framing

changes.

## Operational protocol

1. **Read the inputs.** `MANUSCRIPT_STATE.yaml` (refuse on

outline; note `project.target_type` and `project.target_venue`

for context), the manuscript methods section (load-bearing —

refuse if absent), title and abstract for disambiguation,

any registration metadata if visible in the methods.

2. **Apply each detection heuristic** systematically (not

opportunistic). For each guideline, check the methods

against the detection signals; tag the match as high /

moderate / low / no-match.

3. **Identify multi-checklist applicability.** Animal RCTs

need ARRIVE + CONSORT. AI-based diagnostic tools need

STARD-AI (and STARD baseline). Systematic reviews with a

meta-analysis sub-component need PRISMA. Surface all

applicable checklists, not just the most prominent.

4. **Confirm with high-vs-low confidence assignment.** For each

recommended guideline:

- **High confidence**: methods clearly describes a design

that matches the checklist's scope. RCT with randomisation

and allocation concealment → CONSORT high confidence.

- **Moderate confidence**: design matches but some signals

are ambiguous. A cohort study with non-standard

observational structure → STROBE moderate confidence.

- **Low confidence**: methods is sketchy or design genuinely

unclear. Skill recommends the checklist but flags that the

methods needs to be fleshed out before the

- reporting-compliance step is useful.

+ reporting-guideline-compliance step is useful.

5. **Surface rejected candidates explicitly.** The "Other

guidelines considered" section names checklists the skill

evaluated but rejected — particularly important when the

manuscript is on the edge between two designs (a

non-randomised intervention study would consider both

CONSORT and STROBE; the skill names which and why one was

chosen).

6. **Recommend the next step.** Point the author at the v0.3

- `reporting-compliance` skill (or, while that's not yet

+ `reporting-guideline-compliance` skill (or, while that's not yet

built, at the EQUATOR-published checklist URL and a

manual checklist walkthrough). Never run the checklist

itself.

7. **Emit the structured markdown report.**

## Output format

Emit a markdown document with exactly these section headings,

in order:

```markdown

# Reporting-guideline fit

## Summary

<one paragraph: methods-section signal strength (clear /

moderately clear / sketchy); count and names of inferred

applicable guidelines; recommended next step.>

## Inferred guideline(s)

<per recommended guideline: name (with version — CONSORT 2010,

PRISMA 2020, ARRIVE 2.0, TRIPOD+AI 2024, etc.); confidence

(high / moderate / low); one paragraph naming the methods-

section signals that matched, with quotes where useful;

URL or DOI for the checklist (from EQUATOR registry); whether

an AI-extension applies and which.>

## Why this guideline (per inference)

<expanded rationale per guideline. The methods quotes from

the inferred-guideline section get expanded here with

additional context — "the methods describes random allocation

to two arms (intent-to-treat analysis named on p. 7), which

maps to CONSORT 2010 items 8-9 and 16". Useful when the

author wants to verify the inference against their own

understanding of the design.>

## Other guidelines considered

<checklists the skill evaluated and rejected, per item:

guideline name; the methods signals that initially suggested

it; the signal that ruled it out. Particularly important for

edge cases (non-randomised intervention → considered CONSORT

but rejected because allocation was not randomised; chose

STROBE instead).>

## Multi-checklist applicability

<only when more than one checklist applies. Names the

combination explicitly (animal RCT → ARRIVE 2.0 + CONSORT

2010; AI-based diagnostic → STARD 2015 + STARD-AI extension).

Notes that multi-checklist applicability is intentional, not

a confusion of the inference.>

## What the methods section needs to do

<only at moderate or low confidence. Names what's currently

sketchy or ambiguous; suggests what the author could clarify

to raise confidence. "The methods describes 'two-arm trial'

but doesn't explicitly describe randomisation method —

clarifying whether allocation was randomised (CONSORT) or

deliberately allocated (STROBE) would resolve the

ambiguity.">

## Recommended next step

<concrete: "Once you confirm the inference, run

- /scriptorium:reporting-compliance with [chosen guideline] to

+ /scriptorium:reporting-guideline-compliance with [chosen guideline] to

audit the manuscript against the checklist items. Until

- reporting-compliance lands in v0.3, the EQUATOR Network

+ reporting-guideline-compliance lands in v0.3, the EQUATOR Network

publishes the [checklist] at [URL]; a manual walkthrough is

feasible for any of the v0.1 supported checklists." Concrete

URLs where stable.>

## What this inference did NOT do

- <explicit boundaries: not the full reporting-compliance audit

+ <explicit boundaries: not the full reporting-guideline-compliance audit

(this skill stops at the inference); not a declaration of

which guideline must be used (recommendation only — the

author confirms); not a study-design critique (the skill

infers from the methods as written, not whether the design

was the right choice); not editor-side enforcement.>

```

## What "good output" looks like

- **Single inferred guideline at high confidence**: methods is

clear, design matches the checklist scope cleanly, output is

concise (Summary + Inferred + Recommended next step

substantive; other sections empty or brief).

- **Multi-checklist applicability surfaced explicitly**: when

ARRIVE + CONSORT both apply, the Multi-checklist section names

the combination and the author isn't surprised when the

- reporting-compliance step runs against both.

+ reporting-guideline-compliance step runs against both.

- **Low confidence is named cleanly**: "Methods section is too

sketchy to discriminate between CONSORT (randomised) and

STROBE (observational); flesh out the allocation method

- before running the reporting-compliance step" is the honest

+ before running the reporting-guideline-compliance step" is the honest

output when the design isn't clear.

- **Rejected candidates have rationale**: not just "considered

STROBE", but "considered STROBE because the prose used

'cohort' language; rejected because the methods also describes

randomisation to two arms, which moves the design to CONSORT".

- **Version honesty**: PRISMA 2020 over PRISMA 2009; TRIPOD+AI

2024 over TRIPOD 2015 for AI-based prediction models. The

skill names supersession explicitly.

- **AI-extensions surfaced when applicable**: CONSORT-AI for

AI-enabled trials; SPIRIT-AI for the protocol; TRIPOD+AI for

AI-based prediction models; STARD-AI for AI-based diagnostic

tools.

## What you must not do

- - Run the full reporting-compliance audit (separate v0.3

+ - Run the full reporting-guideline-compliance audit (separate v0.3

skill).

- Declare a checklist as authoritatively applicable. The skill

recommends; the author confirms.

- Operate on outline-phase manuscripts (refuse cleanly).

- Force a confident answer when the methods is too sketchy

to discriminate. Low confidence is honest; confident wrong

is not.

- Write to MANUSCRIPT_STATE.yaml. The schema doesn't carry a

`reporting_guidelines:` field (deliberately — authors often

don't know which checklist applies, which is why this skill

exists; declaring with confidence in state is the

wrong-data-confidently-declared failure mode).

- Conflate the inference with the full audit. Stop at "which

checklist?" — never start running the checklist itself.

- Recommend a checklist version that's been superseded

(PRISMA 2009 when PRISMA 2020 is current; TRIPOD 2015 when

TRIPOD+AI 2024 is current for AI-based prediction models).

- Run other skills as side effects.

## Grounding

This skill is grounded in published research:

- [[reporting-guidelines]] — the primary anchor. The EQUATOR

Network registry, the design-specific checklists (CONSORT

2010, STROBE, PRISMA 2020, ARRIVE 2.0, STARD 2015,

TRIPOD+AI 2024, CARE, COREQ, CHEERS 2022), the AI-extension

landscape (CONSORT-AI / SPIRIT-AI / STARD-AI / TRIPOD+AI),

and the EQUATOR-published rationale for why standardised

reporting matters. The skill's detection heuristics map

directly to the design-discriminating signals each checklist

was built to capture.

- [[declared-work-scope]] — the convention. The inference

operates on the declared methods section; refuses cleanly

on outline or when methods is too sketchy; does not run the

full checklist (separate, downstream).

- [[guidance-level]] — the framing-level convention.

This skill is the **upstream inference** in the

- reporting-guidelines workflow; the v0.3 `reporting-compliance`

+ reporting-guidelines workflow; the v0.3 `reporting-guideline-compliance`

skill (planned) is the downstream checklist runner. The two are

deliberately separate — conflating them produces a skill that's

too long, and that fails silently on the inference step when

it's wrong.

## See also

- - `/scriptorium:reporting-compliance` (planned v0.3) — the

+ - `/scriptorium:reporting-guideline-compliance` (planned v0.3) — the

downstream skill that runs the inferred checklist against

the manuscript. Natural follow-on.

- `/scriptorium:reviewer-simulation` — pairs naturally before

submission. Reviewers at high-tier journals check

reporting-guideline compliance; running both before

submission catches both content and reporting issues.

- `/scriptorium:desk-rejection-risk` — pairs naturally when

`target_venue` is set. Some venues desk-reject manuscripts

that don't meet their required reporting guideline.

- `/scriptorium:explain reporting-guideline-fit` — full design

tour.