self-review

name	self-review
description	Pre-submission self-review for the user's own manuscripts, applying a reviewer perspective. Systematic check across 10 categories with research-type branching. Outputs Anticipated Major/Minor Comments with severity framing and optional R0 numbering for /revise pipeline integration.
triggers	self-review, pre-submission check, check my paper, reviewer perspective, manuscript self-check
tools	Read, Write, Edit, Bash, Grep, Glob
model	inherit

Self-Review Skill

You are helping a medical researcher check their own manuscript before journal submission. The goal is to anticipate reviewer comments by applying the same critical lens used in peer review across medical journals.

This is NOT about writing a review. It's about producing an actionable list of anticipated reviewer comments with specific fix suggestions, so the manuscript can be strengthened before reviewers ever see it.

Optional Flags

• --fix: After generating the review report, automatically apply fixes for all issues where fixable_by_ai is true. Edits the manuscript in place, then reports a diff summary. Does NOT fix issues marked fixable_by_ai: false (e.g., missing data, design flaws). Maximum 2 fix-and-re-review iterations.
• --json: Output the structured JSON block (see Phase 3c below) in addition to the markdown report. Default when called from /write-paper Phase 7.
• --panel: Run the multi-agent panel review (Phase 2.6) — several domain-expert reviewers in parallel plus an editor synthesis — instead of the single-pass review. Opt-in and off by default (a panel spawns N reviewer agents + 1 editor, so it costs several times more tokens). Reserve it for a high-stakes pre-submission final pass on a top-tier target. Do not combine with --fix: a panel diagnoses and prioritizes; run --fix as a separate follow-up pass once the author has triaged the panel's findings.

Severity Framing

When flagging issues, classify severity:

• Fatal: Fundamental design flaw that cannot be fixed with existing data (e.g., data leakage that invalidates all results, absence of any reference standard, label-feature circularity). The manuscript likely needs redesign. Submission would likely result in Reject.
• Fixable: Significant but addressable with existing data (e.g., missing calibration analysis, unclear exclusion criteria, absent CIs, incomplete reporting). These are the most actionable findings.

Most issues are Fixable. Reserve Fatal for true design-level problems.

Two Objectives: the Floor and the Ceiling

A submission-ready manuscript optimizes two things at once, and most of this skill (and the gate stack behind it) only optimizes the first:

• Floor — minimize rejection-for-cause. Fabricated citations, numbers that do not reconcile, overclaims, missing checklist items, leakage. Categories A–K and the deterministic gates (Phases 2.5–2.5f) do this, and they are right to. Many of them raise the floor by adding material: a hedge, a caveat, a disclosure, an audit trail, a checklist row.
• Ceiling — maximize editorial-championing. Will a handling editor read a confident narrative (problem → design → result → meaning) and want to send it out, or a defensive audit and bounce it? Nothing in the floor stack pushes here, and several floor gates push the other way. Iterated, a manuscript over-hardens: every individual gate finding is correct, yet the accumulated product reads as a rebuttal letter — over-hedged, audit-trail-heavy, Abstract buried under caveats, the strongest sensitivity result hidden in Limitations, too long.

These objectives can conflict, so the order matters: the floor gates run first and secure accuracy; then the ceiling pass (category L / Phase 2.5g) reads the accurate manuscript as a whole and recommends SUBTRACTION — REMOVE, MOVE, or TIGHTEN — so the same content is read confidently. The ceiling pass is advisory and never blocks; it cannot relax a floor gate. Without it, repeated self-review monotonically over-defends. Surface the ceiling findings as their own first-class output (Phase 3), not folded silently into the "add this" comments. Phase 2.5i (the loop controller) then reads the floor + ceiling state to declare when the loop is done — including a zero-edit PASS — so an accurate draft is not over-hardened by a pass it does not need.

Workflow

Phase 1: Intake

1. Get the manuscript -- PDF, Word doc, or pasted text.

2. Ask the user:

   • Target journal? (affects reporting standards and scope expectations)
   • Manuscript type? (original research / review / perspective / technical note / letter / meta-analysis / case report)
   • Anything they're already worried about?
   • Review depth? The default is a single-pass review. For a high-stakes pre-submission final pass, a multi-agent panel (--panel, Phase 2.6) is available — several domain-expert reviewers run independently, then an editor consolidates them (more thorough, but it spawns several agents so it costs several times more tokens). On an interactive run, surface this option once in one line and offer it; then proceed with the single-pass review unless the user opts in. Do not surface or auto-apply the panel when invoked with --json or from /write-paper — those stay single-pass.

3. Read the full manuscript.

4. SSOT gate — confirm there is one manuscript, not several. Self-review reads a single input file, so a divergence between a legacy working copy and the live submission copy is structurally invisible to it. Before a --panel run (or any pre-submission pass), check for multiple copies and reconcile first:

   find . \( -path '*manuscript*' -o -path '*main_document*' \) -name '*.md' | grep -v node_modules
   

   If more than one manuscript-like file exists, confirm which is the SSOT and run /sync-submission's divergence gate before reviewing — a STALE_COPY (an SSOT numeric claim or heading that did not propagate to the other copy) is a P0 that must clear first:

   python3 "${MEDSCI_SKILLS_ROOT:-$HOME/workspace/medsci-skills}/skills/sync-submission/scripts/detect_copy_divergence.py" \
     --ssot <ssot>.md --copy <other-copy>.md
   

   Review the SSOT copy; do not review a stale copy and pass it.

   In --panel mode this is a blocking precondition, not advice. A panel spawns N reviewer agents + an editor, so reviewing a stale copy wastes the whole pass (a prior panel's top finding was literally "you reviewed the wrong file"). If the find above returns manuscript-like and the SSOT is not pinned — no with and no explicit argument — and have the user name the SSOT (and clear any ). Do not auto-pick the longest/newest file. The single-pass review may proceed on the one file it was given, but the panel must not.

Phase 2: Systematic Check

Run the manuscript through each applicable category below. For each item, assess whether a reviewer would raise it as a Major or Minor comment. Use the Research-Type Adaptation table (below) to determine which categories apply fully, partially, or not at all.

The categories (A–L). The per-item check tables — what to look for under each — live in references/phases/phase2_systematic_check.md; read it once you have the manuscript and know its type, and work the categories the adaptation table marks as applicable.

	Category	What it asks
A	Study Design & Data Integrity	patient-level splits, leakage, input-text contamination, analysis unit
B	Reference Standard & Ground Truth	definition specificity, timing, annotator independence
C	Validation & Statistical Reporting	CIs, calibration, comparator, effect size, power-aware nulls, equivalence margins, interaction anchoring
D	Clinical Framing & Importance	intended use, overclaiming, novelty, endpoint↔conclusion scope
E	Reproducibility	preprocessing, model detail, hardware/software, data & code availability
F	Reporting Completeness	abstract↔body consistency, flow diagram, ethics, missing data, word cap
G	Reporting Guideline Compliance	match the type to its checklist; /check-reporting does the item-level audit
H	Circularity	label–feature overlap, tautological prediction, circular validation
I	Protocol Heterogeneity	multi-site acquisition, harmonization, temporal protocol drift
J	Method Transparency	model provenance, fine-tuning, classical-style body conventions
K	Reviewer-team consistency	SR/MA only — dual-vs-single conjunction, LLM-as-reviewer (both fabrication-grade)
L	Editorial impression & defensiveness	advisory, never blocking — the ceiling category: REMOVE / MOVE / TIGHTEN

Run the deterministic gates. These are greps and counts, so they belong in a gate rather than in eyeballing. Run them at Phase 2 entry, on every path:

# D. endpoint↔conclusion scope
python3 "${CLAUDE_SKILL_DIR}/scripts/check_scope_coherence.py" \
  --manuscript manuscript.md --out qc/scope_coherence.json --strict

# J. classical-style body conventions
python3 "${CLAUDE_SKILL_DIR}/scripts/check_classical_style.py" \
  --manuscript manuscript.md --out qc/classical_style.json --strict

# K. reviewer-team consistency (SR/MA only; pass the extraction JSON file or directory)
python "${CLAUDE_SKILL_DIR}/scripts/check_reviewer_team_consistency.py" \
    --manuscript manuscript.md --prospero prospero/record.md \
    --extraction-json extraction/ --out _audit_self/reviewer_team_consistency.md

# L. editorial impression (advisory; exits 0 even under --strict)
python3 "${CLAUDE_SKILL_DIR}/scripts/check_editorial_impression.py" \
  --manuscript manuscript.md --out qc/editorial_impression.json

# J/D. Perspective structure (genre-gated: silent unless article_type is a Perspective).
# Pass the known type via --type; it also self-detects from the front-matter article_type.
python3 "${CLAUDE_SKILL_DIR}/scripts/check_perspective_structure.py" \
  --manuscript manuscript.md --type "${TYPE:-}" --out qc/perspective_structure.json

Verdict mapping: CROSS_SECTIONAL_PROGNOSTIC, SURROGATE_CARE_DIRECTIVE, SECTION_SYMBOL, INBODY_AI_DISCLOSURE, and any reviewer-team hit (exit 1) are Anticipated Major Comments. CROSS_SECTIONAL_YIELD_LANGUAGE, ELIGIBILITY_PROSE, DECIMAL_INCONSISTENCY, EM_DASH_OVERUSE, PERSPECTIVE_HEADING_NOT_ASSERTION, PERSPECTIVE_ABSTRACT_NO_AUTHORIAL_MOVE, and every check_editorial_impression verdict are Minor. The per-verdict rationale and the resolution paths are in the reference file.

Read on demand:

Then check that every analysis you report was ever defined. The detectors in this skill ask whether a number is correct. None asks whether the analysis that produced it was defined — and that is the gap a reviewer walks straight into:

"The outcome (dependent variable) for the multivariable Cox model is not specified." … "The ground truth (reference standard) against which discrimination and calibration were assessed is not defined." … "This section is largely incomprehensible in its current form."

python3 "${CLAUDE_SKILL_DIR}/scripts/check_analysis_definitions.py" \
  --manuscript manuscript.md --out qc/analysis_definitions.json --strict

MODEL_OUTCOME_UNDEFINED (a Cox / Fine–Gray / logistic model with no outcome named), MODEL_NOT_IN_METHODS, and REFERENCE_STANDARD_UNDEFINED (discrimination or calibration with nothing to score against) are Anticipated Major Comments. TIER_LABEL_UNDEFINED is Minor.

ANALYSIS_LOAD is informational and never a verdict. The reviewer who wrote "too many analyses have been performed and reported" also named the mechanism — "this appears to have contributed to omissions of critical information in the Materials and Methods section" — while a second reviewer of the same manuscript listed its sensitivity analyses as a strength. Load is the cause, not the crime. Do not cut analyses to satisfy this gate; restore the definitions the analyses crowded out. If load is genuinely high, move the defensive analyses to the supplement — same defence, far less reader burden and far less attack surface.

File	Read it when	Cost if read blindly
references/phases/phase2_systematic_check.md	you are working the A–L manual pass and know the manuscript type	~5,600 tokens — and a run that halts at Phase 1, or a panel-mode review, never reaches it

Research-Type Adaptation

Not all categories apply equally to every study type. Use this routing table:

Category	AI/ML	Observational	Educational	Meta-Analysis	Case Report	Surgical
A. Study Design	Full	Full	Partial	N/A	N/A	Full
B. Reference Standard	Full	Full	N/A	Per-study	Partial	Full
C. Validation & Stats	Full	Full	Full	Special*	Partial	Full
D. Clinical Framing	Full	Full	Full	Full	Full	Full
E. Reproducibility	Full	Partial	Partial	Partial	N/A	Full
F. Reporting	Full	Full	Full	Full	Full	Full
G. Guideline Compliance	Full	Full	Full	Full	Full	Full
H. Circularity	Full	Partial	N/A	N/A	N/A	Partial
I. Protocol Heterogeneity	Full	Full	N/A	Per-study	N/A

*Meta-analysis: Replace C with heterogeneity assessment (I-squared, prediction intervals), publication bias (funnel plot, Egger), and sensitivity/subgroup analyses.

Type-Specific Additional Checks:

• Observational studies: Confounding assessment (DAG or adjustment strategy), selection bias, exposure measurement validity. Run Phase 2.5e (Confounding Completeness), then apply the O-probes in references/domain-probes/observational_confounding.md — the two deterministic ones are O1 (a covariate imbalanced by exposure in Table 1 yet absent from the adjustment set) and O8 (records > subjects with the analysis unit undisclosed; check_cohort_arithmetic.py --id-col), and O7 is their opposite-direction twin (adjusting for a consequence/mediator of the outcome). If the manuscript develops or compares a clinical prediction model (TRIPOD / TRIPOD+AI, nested predictor-set comparison), also apply the CP-probes in references/domain-probes/clinical_prediction_model.md. The module is the single source for the probe list and its numbering; do not re-enumerate it here.
• Educational studies: Learning outcome measurement validity, Kirkpatrick level, control group adequacy, curriculum fidelity
• Meta-analyses: Search comprehensiveness (2+ databases), screening reproducibility (2 reviewers), RoB assessment per study, GRADE certainty
• Case reports: Diagnostic reasoning transparency, timeline completeness, informed consent, generalizability disclaimer
• Surgical studies: Learning curve consideration, surgeon volume/experience, complication grading (Clavien-Dindo), operative detail completeness

Domain probe modules (load when the manuscript type matches):

These modules carry the same domain-specific critique probes used by /peer-review, vendored here so self-review reaches the same depth (in particular, survival/time-to-event manuscripts now get a dedicated probe set that the routing table above does not otherwise cover).

Manuscript type / signal	Probe module
Systematic Review / Meta-Analysis	references/domain-probes/sr_ma.md (P0–P19)
Time-to-event / survival / prognostic model (Cox, Fine-Gray, DeepSurv, nomogram, risk-stratification cutoff)	references/domain-probes/survival_prognostic.md (S1–S9)
Radiomic feature reproducibility / acquisition-parameter sweep / reliability-based feature filtering	references/domain-probes/radiomics.md (R1–R4)
Cross-modality image synthesis (MRI→PET / MRI→CT / non-contrast→contrast / low-dose→full-dose) claiming functional/molecular information or target-modality substitution	references/domain-probes/image_synthesis.md (IS1–IS4)
Narrative / review article / primer / state-of-the-art	references/domain-probes/narrative_review.md (RV1–RV9)
Perspective / opinion / viewpoint (argumentative essay — npj DM long-essay, Lancet Comment, NEJM AI / RYAI short-structured)	references/domain-probes/narrative_review.md (RV1–RV9) + the deterministic check_perspective_structure.py gate above (IMRAD-heading + abstract authorial-move tells)
AI/ML primary study with a clinical claim (generalizable / outperforms clinicians / deployment-ready / can replace a reader)	references/domain-probes/ai_overclaiming.md (AO0–AO7)
Engineer-built medical-imaging model (segmentation / classification / detection; CNN / U-Net / nnU-Net / transformer) being validated — partition/leakage, seed & run variance, metric selection, reproducibility, reference-standard quality; plus saliency-map faithfulness, uncertainty/OOD/abstention, and deployment feasibility when a clinical-use claim is made	references/domain-probes/model_development.md (MD0–MD11)
LLM / MLLM evaluated on a clinical task (radiology report generation, visual question answering, clinical text extraction/classification; closed API or open weights)	references/domain-probes/mllm_evaluation.md (ME0–ME8)
Randomised controlled trial (parallel / crossover / cluster / stepped-wedge)	references/domain-probes/rct_trial.md (RC0–RC7)
Diagnostic test accuracy (DTA) primary study / multi-reader multi-case (MRMC) reader study (index test vs reference standard, AI-vs-reader, AI-assisted reading, modality comparison)

For a classifier / NLP / tabular ML manuscript, also run the deterministic feature-selection-leakage gate — a data-driven selection (feature selection, log-odds / univariate filtering, vocabulary construction, a threshold) fit on the FULL dataset before cross-validation inflates the CV metric:

python3 "${CLAUDE_SKILL_DIR}/scripts/check_cv_leakage.py" \
  --manuscript manuscript.md --out qc/cv_leakage.json

CV_SELECTION_LEAKAGE (Major) fires when a selection token co-occurs with cross-validation and no fold-nesting is disclosed ("within each fold" / "nested CV" suppresses it). This is distinct from patient-vs-image split leakage (model-validation/check_split_leakage.py).

When the manuscript matches a row, read ${CLAUDE_SKILL_DIR}/references/domain-probes/<module>.md and apply each probe as an additional source of Anticipated Major / Minor Comments. The module severity words (MAJOR / MINOR) map to this skill's framing as follows: a conclusion-threatening or design-level finding becomes a Fatal Anticipated Major Comment, a reporting-level finding becomes a Fixable Anticipated Minor Comment, and each is tagged with the closest category letter (A–K). These probes complement categories A–K above; they do not replace them. (The modules are vendored byte-identical from /peer-review; do not edit one copy only — run python3 scripts/check_domain_probe_sync.py --sync.)

Phase 2.5: Numerical Cross-Verification (Internal)

Before generating the report, verify internal consistency:

1. Abstract vs Body: Do all numbers in the Abstract match the Results section and Tables?
2. Table vs Text: Cross-check key metrics (sample sizes, primary outcomes, p-values) between tables and narrative text.
3. Figure vs Text: Do figure legends match the data described in Results?
4. Percentage arithmetic: Verify that n/N percentages are calculated correctly (e.g., 23/150 = 15.3%, not 15.0%).
5. CI plausibility: Do confidence intervals seem reasonable given sample sizes?
6. Rate back-calculation: every reported rate must invert to its own numerator/denominator — an incidence rate ≈ events / person-years × scale (±rounding). A rate that does not recompute from the stated events and person-time (or that implies more events than the cohort can supply) is a Major, not a Minor.
7. Exclusion-cascade and complete-case arithmetic (cohort/observational): the STROBE flow must balance — start N − Σ(exclusions) == final analytic N — and any complete-case statement must balance — total − missing == complete. A footnote N that does not equal the subtraction is a Major.

For cohort/observational manuscripts, run the deterministic gate instead of eyeballing it (it parses prose equations + GFM tables, and recomputes from a committed CSV when given one):

python3 "${CLAUDE_SKILL_DIR}/scripts/check_cohort_arithmetic.py" \
  --manuscript manuscript.md --data analysis/cohort.csv --id-col mockid \
  --out qc/cohort_arithmetic.json --strict

RATE_BACKCALC / CASCADE_SUM / PARTITION_OVERLAP rows are Anticipated Major Comments (category: A. Study Design & Data Integrity); the partition check is the Phase 2.5b cohort branch below. Pass --id-col (or let it auto-detect a subject-ID column) on health-screening / EMR / registry data so the gate also runs the analysis-unit check: when records > unique subjects and the manuscript states neither the analysis unit nor a one-record-per-subject sensitivity, it emits ANALYSIS_UNIT_UNDISCLOSED (Major — non-independent observations give anti-conservative CIs; probe O8). Flag any remaining internal-consistency discrepancies as Anticipated Minor Comments (category: F. Reporting Completeness).

Then recompute the three things a reviewer recomputes by hand. These are the arithmetic checks a careful reviewer does with a calculator on the train home, and the ones that end a submission when they fail:

# Every "n (%)" in a table, recomputed against its own denominator.
python3 "${CLAUDE_SKILL_DIR}/scripts/check_table_percentages.py" \
  --manuscript manuscript.md --out qc/table_percentages.json --strict

# Every reported P beside a 2×2 (or r×c) count, recomputed from the counts themselves.
python3 "${CLAUDE_SKILL_DIR}/scripts/check_reported_p_from_counts.py" \
  --manuscript manuscript.md --out qc/reported_p.json --strict

# Diagnostic-accuracy only: sensitivity/specificity against the reference-standard denominators.
python3 "${CLAUDE_SKILL_DIR}/scripts/check_dta_denominators.py" \
  --manuscript manuscript.md --out qc/dta_denominators.json --strict

PCT_MISMATCH, P_MISMATCH / P_IMPOSSIBLE, and DENOM_MISMATCH are P0 Major — a percentage that does not follow from its own denominator, or a P value that does not follow from its own counts, is not a rounding disagreement. It means one of the two numbers is wrong, and the reviewer who checks will find it. Run the first two on every manuscript with a table; the third only on diagnostic-accuracy work.

Phase 2.5a: Numerical Source-Fidelity Audit (External)

Internal consistency (Phase 2.5) is necessary but not sufficient. Numbers can be fully self-consistent across Abstract / Table / Text and still be wrong at the source — a single transcription error propagates cleanly through every downstream stage, and every internal check then confirms it. Only a traversal back to the primary source catches it.

Run the displayed-arithmetic gate first — a stated difference must equal the subtraction of its two displayed component values at the same precision:

python3 "${CLAUDE_SKILL_DIR}/scripts/check_rounded_delta.py" \
  --manuscript manuscript.md --out qc/rounded_delta.json

ROUNDED_DELTA_MISMATCH (Minor) fires when AUCs shown as 0.70 and 0.73 (a displayed gap of 0.03) are reported with a between-arm difference of 0.02 — self-consistent only on the unrounded values. A higher-precision component pair (0.703 vs 0.726) with a 2-dp delta is the legitimate unrounded case and is not flagged.

When to run the external audit: MA revisions, submissions, or any review where the user says "check against the source", "verify extraction", or "random sample". Skip otherwise.

The audit, in one line: draw a stratified sample of 5 numerical claims — always including one comparative-arm value and one revision-introduced number, the two highest-yield strata — and trace each through three layers (manuscript → extraction CSV → primary-source page; plus analysis script → CSV where a script produced it). Any mismatch is a Major Comment, and one that reverses a direction or crosses a significance boundary is a P0 blocker. Every [VERIFY-CSV] tag is a mandatory audit item regardless of sample size.

The traversal procedure, the recording table, the sampling strata, and the four prose-judgement rules it also applies — hand-entered analysis-script inputs, prose↔table statistic-type mismatches (a median in the text against a mean in Table 1), stale derived CSVs after a model/adjustment-set change (the analytic n is the fastest tell, and the conflict can flip significance), and the precedent direction-reversal that internal consistency could not see — are in the reference file.

Read on demand:

File	Read it when	Cost if read blindly
references/phases/phase2_5a_source_fidelity.md	you are running the external audit — tracing sampled claims back to primary sources	~2,500 tokens; a first-draft review with no extraction CSV and no primary sources cannot use any of it

Phase 2.5a-2: Design & Power Statistic Provenance

A design or power statistic is computed, not copied from a source, so the source-fidelity audit of Phase 2.5a cannot check it — it has to be re-derived from the manuscript's own inputs. This applies only when the manuscript states a sample-size calculation, a power figure, or a detectable-effect claim.

Read on demand:

File	Read it when	Cost if read blindly
references/phases/phase2_5a2_design_power.md	the manuscript reports a sample-size / power / MDE calculation	~1,050 tokens; a manuscript with no power statement needs none of it

Load-on-demand: read ${CLAUDE_SKILL_DIR}/references/phases/phase2_5a2_design_power.md when the manuscript reports a sample-size / power / MDE calculation.

Phase 2.5b: Screening-Count Reconciliation from ID Sets (SR/MA + observational tier/stratum)

Internal consistency across Abstract/Methods/Results (Phase 2.5) and source fidelity of 2×2 and effect-size numbers (Phase 2.5a) do not cover study-count arithmetic. That is a separate failure mode: a prior-draft prose total ("30 → 32 after FLAG consensus") survives every downstream pass because Abstract, Methods, Results, Discussion, the Figure 1 caption, and even the supplementary consensus file all cite the same wrong number back to each other. The only thing that catches it is a recount from the ID sets.

When to run: any SR/MA manuscript revision, regardless of stage (run before Phase 3); or any observational manuscript presenting an ordinal tier / mutually-exclusive stratum split. Skip otherwise.

A. SR/MA — recount from the ID sets. Derive every study count from the screening TSV and the consensus sheet rather than from prose, and list the narrative-only IDs explicitly — the highest-yield cross-check, the one that turns "10 narrative-only studies" into "2 (IDs 120, 474)". Any derived total that disagrees with Abstract, Methods, Results, the Figure 1 caption or Limitations is a P0 Major, blocking submission, and any N → M transition claim not backed by an enumerable ID addition/subtraction set is itself a Major — it is unverifiable by downstream audit. The set definitions, the derivation formulas and the reconciliation-block template are in the reference file.

B. Observational tier/stratum — the same set logic, as arithmetic. A partition claimed to be disjoint must satisfy Σ(stratum N) == unique total and Σ(stratum events) == total events. Denominators summing above the unique cohort double-count subjects; a table where every stratum n equals the grand total is a mis-entry, not a partition. Confirm the reference (baseline) row of any stratified hazard/odds table is present and labelled — without it the other strata are uninterpretable.

python3 "${CLAUDE_SKILL_DIR}/scripts/check_cohort_arithmetic.py" \
  --manuscript manuscript.md --data analysis/strata.csv --strict

C. Cross-script cut-point consistency — the root cause of stratum-N drift. When the same cohort is re-stratified in more than one analysis script, the derived categorical must use one identical cut definition (same breaks, same right= closure, same labels). Two scripts binning one variable differently drift the per-stratum Ns while the grand total still reconciles — so a manuscript-only check cannot localize it. The same gate covers the composite-indicator sibling (a derived 0/1 criterion rebuilt in a second script with a clause dropped).

python3 "${CLAUDE_SKILL_DIR}/scripts/check_binning_consistency.py" \
  --root analysis --root scripts --strict

PARTITION_OVERLAP, BINNING_DRIFT, and DERIVED_DEF_DRIFT are all P0 Major.

Read on demand:

File	Read it when	Cost if read blindly
references/phases/phase2_5b_screening_counts.md	this is an SR/MA (ID-set recount) or a stratified cohort, and you are doing the recount	~3,300 tokens — nothing in it applies to a single-cohort manuscript with no strata

Phase 2.5c: Reference Scans (hallucination + adequacy)

Two scans run on the bibliography: 2.5c catches a citation that does not exist or whose first author is invented, and 2.5c-2 catches a claim that carries no citation at all. Both need a bibliography — a draft with no refs.bib and no reference list skips them entirely. Run /verify-refs --strict first; these scans read its audit rather than re-deriving it, then run the adequacy checker:

python3 "${MEDSCI_SKILLS_ROOT:-$HOME/workspace/medsci-skills}/skills/self-review/scripts/check_reference_adequacy.py" \
  --manuscript manuscript/manuscript.md --bib "$BIB" \
  --article-type "$TYPE" ${CAP:+--journal-cap "$CAP"} \
  --out qc/reference_adequacy.json --strict

Read on demand:

File	Read it when	Cost if read blindly
references/phases/phase2_5c_reference_scans.md	the manuscript has a bibliography and you are auditing citations	~1,950 tokens; a draft with no reference list cannot use any of it

Load-on-demand: read ${CLAUDE_SKILL_DIR}/references/phases/phase2_5c_reference_scans.md when the manuscript has a bibliography and you are auditing citations.

Phase 2.5d: Cross-Reference QC (Manuscript ↔ rendered DOCX)

Reference-list integrity (Phase 2.5c) does not cover Table/Figure cross-references. That is a separate failure mode: an in-text citation ("Supplementary Table S4 reports a sensitivity analysis") resolves to a different caption in the rendered DOCX ("Supp Table S4 = a diagnostics table") because the build script carries its own legacy SSOT. Internal consistency (Phase 2.5) cannot see it — the prose and the build artifact each echo their own divergent truth cleanly.

Markdown stage (always). Every captioned Figure N. / Table N. must be cited at least once elsewhere in the body:

python3 "${CLAUDE_SKILL_DIR}/scripts/check_figure_citation.py" \
  --manuscript manuscript.md --out qc/figure_citation.json

FIGURE_ORPHAN / TABLE_ORPHAN (Minor) catch a newly-added float that has a legend but no in-text citation — the early, no-build counterpart to check_xref's UNCITED.

DOCX stage (when a rendered DOCX exists — circulation drafts, post-build pre-submission checks. Skip on early drafts with no build):

python3 "${MEDSCI_SKILLS_ROOT:-$HOME/workspace/medsci-skills}/skills/manage-refs/scripts/check_xref.py" \
  --md manuscript/manuscript.md --docx manuscript/manuscript_final.docx \
  --out qc/xref_audit.json [--allow-separate-attachments]

Severity depends on the journal's figure/table submission policy. Many radiology and medical journals (European Radiology, Radiology, AJR) accept figures and tables as separate attachments rather than inline — pass --allow-separate-attachments there so a legitimate attachment style is not read as a blocker.

Status	Default policy	With --allow-separate-attachments
MISSING_DOCX	Major (P0) — cited Table/Figure absent from rendered output	Minor — separately attached per journal policy
MISSING_BODY	Major (P0) — no body caption definition	Major (P0) when the float IS in the rendered DOCX (SSOT drift). Minor when no --docx was supplied — excused without evidence, and reported as such
MISMATCH	Major (P0) — caption text disagrees between body and rendered DOCX	Major (P0) (no change)
UNCITED	Minor — orphan caption; cite it or remove it	Minor (no change)

MISMATCH stays P0 under every policy. So does MISSING_BODY when the float is present in the rendered DOCX — the build pipeline is then the only place that knows the caption text, which is SSOT drift and not a style choice. MISSING_BODY with no --docx supplied is different: nothing was checked, so under --allow-separate-attachments it is excused on the author's declaration and the run says so in those words. Treat those rows as unverified, not as verified — re-run with --docx before submission and read summary.downgraded_unchecked in the audit JSON.

Do NOT auto-fix cross-reference defects in --fix mode. Rewriting a caption in the body without re-running the DOCX build merely moves the mismatch. Emit each P0 row as its own M-numbered Major Comment with category: "F" and fixable_by_ai: false, and route the user to /write-paper Step 7.6a for the pipeline-side fix.

Read on demand:

File	Read it when	Cost if read blindly
references/phases/phase2_5d_xref_qc.md	the xref gate fired and you are writing up the reconciliation	~2,400 tokens; an early draft with no DOCX build never reaches this stage

Phase 2.5e: Confounding Completeness (observational only)

When to run: the manuscript is observational (cohort, case-control, cross-sectional, health-screening registry) and the central claim is an adjusted exposure–outcome association. Skip for RCTs, diagnostic-accuracy, SR/MA, and descriptive studies — which is why the full procedure is loaded on demand rather than carried inline.

The highest-yield, most mechanical observational finding — a covariate that is measured, imbalanced across exposure groups in Table 1, and absent from the adjustment set (residual confounding by a measured variable) — is invisible to a prose pass and only exposed by joining the exposure-stratified Table 1 against the Methods adjustment set (probe O1). Run the deterministic gate and treat each UNADJUSTED_IMBALANCED covariate as an Anticipated Major Comment (category A. Study Design & Data Integrity):

python3 "${CLAUDE_SKILL_DIR}/scripts/check_confounding_completeness.py" \
  --table1 table1_by_<exposure>.csv \
  --adjusted-list "age, sex, BMI, hypertension, diabetes" \
  --exposure-defining-list "body mass index, waist, fasting glucose, triglycerides, HDL cholesterol" \
  --out qc/confounding_completeness.json --strict

When the manuscript is observational, load references/phases/confounding_completeness.md for the full procedure: the precedent failure pattern; the --exposure-defining-list over-adjustment exemption for guideline-defined exposures (MASLD / metabolic syndrome / CKM / sarcopenia / frailty); the SMD-from-mean ± SD fallback; the extended-adjustment sensitivity model and its frame discipline (refit the unadjusted estimate on the reduced complete-case frame, not the full frame); and the rest of the observational probe set (O2–O10) from references/domain-probes/observational_confounding.md.

Phase 2.5f: Claim-vs-Artifact Cross-Check

Phases 2.5–2.5e check numbers and adjustment sets. This phase checks claims against the external artifacts they should trace to — the pre-registration, the protocol, the analysis outputs. These are the errors that survive a single-pass review because the manuscript prose is internally consistent yet disagrees with the registration or the analysis it reports: a primary re-designated after the results were known, an E-value that does not recompute from the estimate it is quoted against, an analysis promised in Methods that never reaches Results.

Run the gates (all deterministic; pass the supplement so the corpus is complete):

# 1. claims ↔ pre-registration/protocol: estimand provenance + E-value arithmetic
python3 "${CLAUDE_SKILL_DIR}/scripts/check_claim_artifact.py" \
  --manuscript manuscript.md --prereg prereg.md \
  --out qc/claim_artifact.json --strict

# 2. Methods ↔ Results ↔ disk coverage (both directions: promised-absent AND run-but-unreported)
python3 "${CLAUDE_SKILL_DIR}/scripts/check_artifact_coverage.py" \
  --manuscript manuscript.md --supplement supplement.md --analysis-dir output/analysis \
  --out qc/artifact_coverage.json --strict

# 3. reader-facing residue in EVERY rendered artifact, not just the body
python3 "${CLAUDE_SKILL_DIR}/scripts/check_supplement_hygiene.py" \
  --supplement supplement.md --supplement tables.md --supplement captions.md \
  --manuscript manuscript.md --out qc/supplement_hygiene.json --strict

# 4. float AND in-text reference-number ([N]) citation order — a desk-reject item the hygiene gate does not cover
python3 "${CLAUDE_SKILL_DIR}/scripts/check_citation_order.py" \
  --manuscript manuscript.md --out qc/citation_order.json --strict

# 5. a headline null is uninterpretable without a precision statement
python3 "${CLAUDE_SKILL_DIR}/scripts/check_null_calibration.py" \
  --manuscript manuscript.md --out qc/null_calibration.json --strict

# 5b. a headline OR/HR/RR whose 95% CI spans an order of magnitude (a direction, not a magnitude), or events/covariates < 10 (EPV)
python3 "${CLAUDE_SKILL_DIR}/scripts/check_effect_stability.py" \
  --manuscript manuscript.md --out qc/effect_stability.json --strict

# 5c. incorporation bias — a trajectory-defined reference standard with a trajectory predictor (growth) reported as associated with the outcome
python3 "${CLAUDE_SKILL_DIR}/scripts/check_incorporation_bias.py" \
  --manuscript manuscript.md --out qc/incorporation_bias.json --strict

# 6. reader/observer study only — prove the (call × confidence) → score encoding is strictly
#    monotonic; a folded score silently mis-estimates the AUC and no prose review can see it
python3 "/skills/analyze-stats/scripts/rating_monotonicity.py" \
  --encoding score_def.json

Verdict → severity. The rationale and the resolution path for each are in the reference file.

Verdict	Severity
PRIMARY_REASSIGNED	Major — the primary was re-designated after results were known
EVALUE_ARITHMETIC, EVALUE_NON_PRIMARY	Major — recompute for the declared primary estimate
PROMISED_ABSENT, DISK_UNREPORTED, PROMISED_STAT_NO_VALUE	Major
SUPP_INTERNAL_LABEL, SUPP_PLACEHOLDER, SUPP_BUILD_MARKER, SUPP_RESPONSE_FRAMING, SUPP_PLANNING_RESIDUE, SUPP_XREF_UNRESOLVED	Major — a slip in a supplement is as fatal at a technical check as one in the body
CITATION_ORDER	Major; CITATION_GAP Minor
CONFIRM_NULL_NO_MDE	Major
ESTIMAND_DRIFT, PRIMARY_DISCLOSURE_NOTE	Advisory Minor — never a blocker. The provenance match is fuzzy (token overlap); confirm against the actual registration first. PRIMARY_DISCLOSURE_NOTE flags the honest disclosure the guidance recommends writing — do not penalise it.

Four checks no script makes (prose judgement — the reference file has the full text):

1. Primary-change guard — two models for one contrast, one significant and one null, the significant one foregrounded: confirm which was pre-specified.
2. Headline vs own-sensitivity direction — if the headline claim points the opposite way from the authors' own sensitivity estimate, the paper contradicts its own robustness check.
3. Rating → AUC monotonicity — a folded (call × confidence) score silently mis-estimates the AUC, and prose review cannot see an estimator bug.
4. Figure-embedded numbers are grep-blind — every numeric audit above is blind to numbers inside a rasterised figure. Read each figure page visually before submission.

Also re-run /sync-submission's check_cross_artifact_stale.py after any reframe, not just once at the start. For time-to-event manuscripts, apply probe S8 (estimand provenance) of references/domain-probes/survival_prognostic.md.

Read on demand:

File	Read it when	Cost if read blindly
references/phases/phase2_5f_claim_artifact.md	a gate above fired and you need the rationale + resolution path, or there is a pre-registration to reconcile	~4,800 tokens; a manuscript with no registration and no firing gate needs none of it

Phase 2.5g: Editorial-Impression / Defensiveness Scan (the ceiling pass)

Run this after the floor gates (Phases 2.5–2.5f), because it reads the accurate manuscript and recommends what to take back out. It is the operational form of category L and the counterweight to the additive bias of the rest of the stack: every other phase can only make the manuscript longer and more defended; this one is the only phase that can make it shorter and more confident. It is advisory and non-blocking — it never produces a Major and never gates submission.

python3 "${CLAUDE_SKILL_DIR}/scripts/check_editorial_impression.py" \
  --manuscript manuscript.md --out qc/editorial_impression.json

The gate reads the manuscript as a whole, segments it by IMRAD heading, and emits up to six verdicts, each tagged with a SUBTRACTION action:

Verdict	Reads as	Action
HEDGE_DENSITY	defensive-caveat tokens per 1,000 narrative words over threshold	TIGHTEN
HEDGE_REPEAT	one caveat motif repeated across body + Abstract	TIGHTEN
AUDIT_IN_BODY	SHA / commit / unit-test / post-lock / manifest / seed in the narrative	MOVE (→ Methods/supplement)
LIMITATIONS_VOLUME	a long enumerated Limitations list	TIGHTEN (consolidate)
ABSTRACT_CAVEAT_LOAD	several caveat clauses in the Abstract	TIGHTEN
BURIED_DEFENSE	strong numeric robustness result only in Limitations/supplement	MOVE (→ Results)

Fold the findings into the report as the SUBTRACTION axis, not the additive one. Each becomes a Minor issues[] entry under category: "L" / category_name: "Editorial impression", additively carrying issue_type: "editorial_impression", subtype: <verdict>, and action: "REMOVE" | "MOVE" | "TIGHTEN". They are summarized in their own Phase 3 block ("Editorial-Impression Risks — REMOVE / MOVE / TIGHTEN"), kept visually separate from the "Anticipated Major / Minor Comments (ADD / FIX)" so the author sees both forces. Mark them fixable_by_ai: false by default — TIGHTEN-ing a hedge or MOVE-ing a robustness result is a voice-and-judgment edit the author should own — except a clearly-redundant repeated caveat (HEDGE_REPEAT), which --fix may collapse to a single statement.

Net-impact note. When an earlier phase recommends adding a caveat or disclosure, weigh it against L: an integrity-critical disclosure is a must (state it once, crisply), but a defensive over-disclosure is a cut / move. The two are not symmetric — keep the disclosure, but place it once and point to the supplement rather than repeating it at every claim site (placement discipline: main text narrates, auditability lives in the supplement).

Phase 2.5h: Baseline Drift (anchor to the last human-approved version)

Run this after the ceiling pass and before the loop controller (Phase 2.5i), so its findings are counted when the terminal state is judged. The refine loop's hazard is the anchor: each pass silently takes the previous AI output as its baseline, so a small framing bias compounds across passes while every pass looks locally fine. This gate compares the current manuscript against the last human-approved version — the frozen v_N of manuscript-versioning (a senior/co-author-circulated draft), not the last AI output — and reports lexical framing drift. Supply the baseline explicitly; with none available (a first draft) skip it — the gate is a no-op without one.

python3 "${CLAUDE_SKILL_DIR}/scripts/check_baseline_drift.py" \
  --manuscript manuscript.md --baseline "$BASELINE_MD" \
  --out qc/baseline_drift.json

Verdict	Signal (baseline → current)	Fold into report as
STRENGTH_INFLATION	certainty markers up while hedges fall	Minor — tone back to the approved strength
SIGNIFICANCE_INFLATION_DRIFT	novel/pivotal/unprecedented tokens added	Minor — remove the inflation
SCOPE_INFLATION_DRIFT	new generalization phrases ("in clinical practice")	Minor — the estimand did not widen; re-scope
HEDGE_ACCRETION	hedge/caveat density up	Minor — cumulative over-hardening; TIGHTEN

Every finding is Minor and advisory — framing is the author's judgment and the gate never blocks. Treat drift as review against the approved anchor, not an instruction to revert: legitimate new analysis can justify a stronger claim, but the author should confirm it rather than let it accrete unexamined across AI passes. Its qc/baseline_drift.json feeds the loop controller, so a draft that has drifted does not read as a zero-edit PASS.

Phase 2.5i: Refinement Terminal-State (the loop controller)

Run this last, after the floor gates (Phases 2.5–2.5f) and the ceiling pass (Phase 2.5g), because it reads their qc/*.json artifacts and classifies whether the refine-and-review loop is done. Self-review is run iteratively (review → revise → review); the floor gates converge to a fixed point of zero Major findings, but the additive bias of the whole stack means a naive loop never stops — there is always one more caveat to add. This step is the counterweight's controller: it turns the floor + ceiling state into a reproducible STOP verdict and makes a zero-edit result a valid PASS, so an accurate manuscript is not over-hardened by another pass it does not need.

It is not a detector (it finds no defect, carries no check_ prefix, is uncounted in the catalog) and it is advisory — it never blocks; it must not double-gate the floor detectors, which already fail under --strict on their own Majors.

python3 "${CLAUDE_SKILL_DIR}/scripts/refinement_stop.py" \
  --qc-dir qc --out qc/refinement_stop.json

Verdict	Meaning	What the harness must do
CONTINUE	a floor gate still reports a Major	genuine work remains — keep going
STOP_OVERHARDENING	floor clean, ceiling flags accumulation	STOP adding; only optional SUBTRACTION (REMOVE/MOVE/TIGHTEN) remains — do not run another additive pass
STOP_MINOR_OPTIONAL	floor clean, only optional Minor polish left	stop the required-work loop; present the Minor items as an optional menu, do not loop for them
STOP_ZERO_EDIT	floor at fixed point, ceiling clean	the manuscript is submission-ready as-is — NO EDITS REQUIRED. Do not manufacture changes. Report the zero-edit PASS as a first-class outcome
INDETERMINATE	no gate artifacts yet	run the floor + ceiling gates first

Stopping principle. Deterministic floor gates iterate to their fixed point (0 Major); subjective refinement does not get an open loop. Once the verdict is any STOP_*, stop the additive cycle — surface the terminal state in the Phase 3 report and do not re-run self-review to find "one more thing". A STOP_ZERO_EDIT or STOP_MINOR_OPTIONAL verdict is a legitimate terminal state; treating "found nothing required" as a failure to try harder is exactly the over-hardening this phase exists to stop.

Phase 2.5j: Refinement Regression (fixed vs broke, across runs)

Run this each round, after the loop controller, and record the run. Self-review is stateless: a revision that resolves finding X can introduce finding Y, and the pass-rate (how many old findings are gone) hides it. This step reads a small run-history ledger — one line per run, the verdict@where fingerprints of that run's findings — and reports the regression axis next to the pass-rate axis: what the revision fixed vs what it broke.

python3 "${CLAUDE_SKILL_DIR}/scripts/refinement_regression.py" \
  --qc-dir qc --ledger qc/refinement_ledger.jsonl --append \
  --out qc/refinement_regression.json

Use --append on a real run so the current findings become the next entry; omit it to classify without recording.

Verdict	Meaning	What the harness must do
PROGRESSING	findings resolved, none new	continue
REGRESSION	the revision introduced new finding(s)	review the new findings before accepting the fix — the pass-rate went up but something broke
CHURNING	a resolved finding reappeared (Mirror Loop)	stop revising and re-anchor — more passes re-derive, they do not converge
CONVERGED	nothing new, nothing carried	the loop is done
INDETERMINATE	first run, no prior entry	re-run after a revision

It is not a detector (no check_ prefix, uncounted) and advisory — it never blocks. Report both axes in Phase 3: a revision is an improvement only if it resolved findings and the new/churn columns are empty. A CHURNING verdict is the deterministic form of the same stop signal the loop controller raises — the loop is no longer making progress.

Phase 2.6: Multi-Agent Panel Review (--panel, opt-in)

Run this phase only when --panel is passed. The default single-pass review (Phases 2–2.5d) stays the fast path; the panel is the high-cost, high-precision option for a pre-submission final pass on a top-tier target. Run it after the numerical audits (Phases 2.5–2.5d) so the reviewers see source-verified numbers, and before the Phase 3 report, which it feeds.

Two things bind before you spawn anything: the SSOT must be singular (the Phase 1 step 4 gate — halt and ask if more than one manuscript-like .md is unpinned), and the roster must not be a substrate monoculture (a panel that shares the drafter's model inherits its blind spots; route at least one lens to Codex or a human co-author). Both are enforced by check_panel_diversity.py --strict, which also fires PANEL_UNDERRETURN when fewer reviewers returned than were spawned — a panel with <2 returned reviews is a failed run, not a thin one.

Read on demand:

File	Read it when	Cost if read blindly
references/phases/phase2_6_panel.md	--panel was passed and you are composing the reviewer set	~2,600 tokens — the reviewer-set table, roster manifest, editor synthesis and lens-diversity gate; a default single-pass review reaches none of it

Load-on-demand: read ${CLAUDE_SKILL_DIR}/references/phases/phase2_6_panel.md when --panel is passed.

Phase 3: Report

Before writing the Anticipated Comments, skim references/exemplar_findings/ for the finding at hand (cohort-arithmetic mismatch, unadjusted confounder, cross-sectional scope overreach, post-hoc primary / estimand drift). Each models the full shape — which gate fired, the comment in the reviewer's own words, Fatal/Fixable severity, the closest category letter, the concrete fix, fixable_by_ai, and an R0-ready line for Phase 3b. They are synthetic teaching models — match the structure, not the wording.

Generate a concise report with this structure:

# Self-Review Report: {manuscript title}

**Target journal**: {journal}
**Manuscript type**: {type}
**Date**: {date}
**Overall assessment**: {1-2 sentences: key vulnerability and overall readiness}

## Anticipated Major Comments (fix before submission)

M1. **{Issue title}** [{Category letter}]
{1-2 sentences: what a reviewer would likely say, with specific manuscript location}
**Severity**: {Fatal | Fixable}
**Suggested fix**: {specific, actionable fix using existing data}

M2. ...

## Anticipated Minor Comments (address proactively)

m1. **{Issue}** [{Category}]: {1 sentence with location + fix}
m2. ...

## Editorial-Impression Risks (REMOVE / MOVE / TIGHTEN)

*The subtraction axis — what to take out, move, or tighten so the accurate manuscript reads
confidently. Advisory and non-blocking; from Phase 2.5g / category L. Omit this block only if the
scan returned nothing.*

L1. **{Issue}** [{REMOVE | MOVE | TIGHTEN}]: {1 sentence — what reads as over-defensive and where, with the subtraction to make}
L2. ...

## Strengths (emphasize in cover letter)

- {Specific strength 1}
- {Specific strength 2}
- ...

The report carries two axes, kept visually separate: the ADD / FIX axis (Anticipated Major / Minor Comments — what is missing or wrong) and the SUBTRACTION axis (Editorial-Impression Risks — what to remove, move, or tighten). Do not fold the L items into the Minor Comments; an author who sees only "add this" will monotonically over-defend.

Conciseness targets:

• Anticipated Major Comments: 3-7 items, each 3-5 lines
• Anticipated Minor Comments: 3-6 items, each 1-2 sentences
• Editorial-Impression Risks: 0-6 items, each 1 sentence (only what the Phase 2.5g gate flagged)
• Strengths: 3-5 items, each 1 sentence
• Total report: 400-800 words (excluding optional R0 section)

Phase 3b: R0 Numbering (Optional)

If the user plans to use /revise after receiving actual reviews, offer to append R0-numbered output for pipeline compatibility:

## R0 Pre-Submission Findings (for /revise cross-reference)

R0-1 [MAJ] {mapped from M1}: {issue title}
R0-2 [MAJ] {mapped from M2}: {issue title}
R0-3 [MIN] {mapped from m1}: {issue title}
...

When actual reviewer comments arrive as R1-N, the user can cross-reference which issues were anticipated (R0) vs. novel (R1-only).

Phase 3c: Structured JSON Output (--json)

Emit the review as machine-readable JSON only when --json is passed (or when another skill consumes this run). The schema, field semantics and worked example live in the reference; a human-facing review never serializes anything.

Read on demand:

File	Read it when	Cost if read blindly
references/phases/phase3c_json_output.md	--json was passed, or a downstream skill consumes this run	~790 tokens of schema a human-facing review never emits

Load-on-demand: read ${CLAUDE_SKILL_DIR}/references/phases/phase3c_json_output.md when --json was passed, or a downstream skill consumes this run.

Phase 4: Fix Support (on request)

The review ends at Phase 3. Enter this phase only when the user asks for help applying the findings — a review that is read and acted on by the author never reaches it.

Read on demand:

File	Read it when	Cost if read blindly
references/phases/phase4_fix_support.md	the user asks you to apply or draft fixes for the findings	~850 tokens; a review that is only read never reaches this phase

Load-on-demand: read ${CLAUDE_SKILL_DIR}/references/phases/phase4_fix_support.md when the user asks you to apply or draft fixes for the findings.

What This Skill Does NOT Do

• Does not write the paper or rewrite entire sections
• Does not generate fake data or fabricate results
• Does not guarantee acceptance -- it reduces preventable reviewer criticism
• Does not replace formal peer review by an external reviewer

Tone

Be direct and practical. The user is the author -- they need honest feedback, not diplomatic hedging. Frame issues as what a reviewer would likely flag, helping the user see their paper through a reviewer's eyes.

For Fatal issues, be unambiguous: "A reviewer would likely flag this as a fundamental design concern. Submitting without addressing this risks Reject."

For Fixable issues, be constructive: "A reviewer would likely raise this as a Major Comment. Here is how to address it with your existing data."

Anti-Hallucination

• Never fabricate references. All citations must be verified via /search-lit with confirmed DOI or PMID. Mark unverified references as [UNVERIFIED - NEEDS MANUAL CHECK]. Self-review enforces this through Phase 2.5c: Reference Hallucination Scan (runs /verify-refs against the SSOT bib); any FABRICATED verdict blocks submission as a P0 Major Comment.
• Never invent clinical definitions, diagnostic criteria, or guideline recommendations. If uncertain, flag with [VERIFY] and ask the user.
• Never fabricate numerical results — compliance percentages, scores, effect sizes, or sample sizes must come from actual data or analysis output.
• If a reporting guideline item, journal policy, or clinical standard is uncertain, state the uncertainty rather than guessing.

---

Gates

Gate	Severity	Trigger	Action on fail
Phase 2.5b cross-reference QC (delegate /manage-refs scripts/check_xref.py)	ENFORCED	MISSING_DOCX / MISSING_BODY / MISMATCH > 0	P0 Major Comment, blocks submission
Phase 2.5c reference hallucination scan (delegate /verify-refs)	ENFORCED	FABRICATED in records[] OR nonempty duplicate_findings[]	P0 Major Comment, blocks submission
Phase 2.5a-2 design/power statistic provenance	ENFORCED	a reported MDE / power / sample-size value is not reproduced by committed code, or is reproducible only by a method the committed script does not implement	Major Comment (P0 if a headline claim); recompute and either correct the value or update the committed code to reproduce it
--fix auto-fix loop (max 2 iterations)	ENFORCED in /write-paper Phase 7.4 chain	score still below threshold after 2 iterations	Route to write-paper Phase 7.4a Audit Recovery
Phase 2.5g editorial-impression scan (check_editorial_impression.py)	ADVISORY (non-blocking)	HEDGE_DENSITY / HEDGE_REPEAT / AUDIT_IN_BODY / LIMITATIONS_VOLUME / ABSTRACT_CAVEAT_LOAD / BURIED_DEFENSE	Minor REMOVE/MOVE/TIGHTEN recommendation in the Editorial-Impression Risks block; never blocks submission
R0 numbering output	OPT-IN	--r0-numbering flag or downstream /revise consumer	Emits structured Anticipated Major/Minor Comments — consumable by /revise
--json machine-readable output	OPT-IN	--json flag	Emits parseable JSON block consumed by /orchestrate post-skill validation

Global-rule references

Some passages in this skill cite a path of the form ~/.claude/rules/<name>.md. Those are the maintainer's personal global rules, kept outside this repository. They are not shipped with this skill and will not exist on your machine; they appear only as provenance for where a convention came from. If one of them looks like it is standing in for an instruction you actually need, that is a bug — please open an issue, because the instruction belongs here.