# Reporting Guideline Fit > 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. - Skill: `seandavi/reporting-guideline-fit` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add seandavi/reporting-guideline-fit` - Raw SKILL.md: https://api.skillmd.com/api/skills/seandavi/reporting-guideline-fit/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: seandavi (https://skillmd.com/u/seandavi) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/seandavi/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`, 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-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-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-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-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-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 ## Inferred guideline(s) ## Why this guideline (per inference) ## Other guidelines considered ## Multi-checklist applicability ## What the methods section needs to do ## Recommended next step ## What this inference did NOT do ``` ## 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-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-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-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-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-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.