submission-audit

Installation
SKILL.md

Submission Audit

Overview

Use this skill for late-stage manuscript QA. It is narrower than manuscript-optimizer: do not use it to redesign a paper from scratch. Use it when the structure mostly exists and the main task is to catch the failures that survive normal revision cycles.

The core rule is simple: never treat a clean-looking manuscript as submission-ready until the front half, figures, legends, methods, supplement, and venue expectations have been checked against each other.

Use the helper script when you want a fast local pass over figure citations:

python ~/.codex/skills/submission-audit/scripts/check_figure_refs.py path/to/manuscript.md
# Claude Code users: replace ~/.codex/skills with ~/.claude/skills

When To Use

Use this skill when:

  • The draft is near submission, resubmission, or internal circulation
  • Figures and legends are mostly finalized
  • The paper needs a last pass for overclaim, missing references, or cross-section drift
  • A revision round compressed the prose and may have dropped supporting detail
  • The supplement exists and may no longer match the main text

Do not use this skill for:

  • Early brainstorming
  • Initial section drafting
  • Citation discovery from scratch
  • Heavy structural rewrites that belong in manuscript-optimizer

Audit Order

  1. Front-half alignment
    • check title, abstract, introduction, and discussion against the actual Results
    • flag any claim stronger than the downstream evidence
  2. Figure and legend coverage
    • verify that every main-figure panel and supplementary panel cited in the paper actually exists
    • verify that panel letters, metrics, datasets, and numbers agree across figure, legend, and main text
  3. Methods and supplement anchoring
    • check that methods are cited where needed from Results
    • check that supplementary figures, tables, and notes are referenced precisely enough to be usable
  4. Terminology and metrics
    • enforce one canonical name per concept
    • check abbreviations, metric naming, domain-shift labels, cohort names, and model names
  5. Risk pass
    • overclaim
    • evidence gaps
    • unsupported mechanism language
    • venue-specific style drift
  6. Nature Portfolio preflight when relevant
    • reporting-summary readiness
    • data and code availability statements
    • accession IDs, repositories, and disclosure of sharing restrictions
    • image-integrity and raw-data readiness
    • AI-use disclosure
    • preprint, related-manuscript, and conference-proceedings disclosure
  7. Reviewer-side rejection pass
    • contribution sufficiency
    • writing clarity and reproducibility
    • empirical strength
    • evaluation completeness
    • design or framework soundness

Required Checks

  • Does every substantive abstract claim map to a figure, table, or supplement item?
  • Does every Results subsection cite the correct panel range?
  • Does every figure legend still reflect the current plot content?
  • Are Methods cross-references present where interpretation depends on setup or metric definition?
  • Is the supplement indexed precisely enough, including panel letters when needed?
  • Are strong causal or mechanism words used only where direct evidence exists?
  • Are title, abstract, and discussion consistent about the paper's actual contribution type?
  • If the target is Nature Portfolio, are the reporting-summary inputs, data/code statements, image-integrity materials, and disclosure items actually ready rather than merely planned?
  • If a submission form or portal draft already exists, do the title, abstract, keywords, availability statements, and related metadata still match the manuscript exactly?
  • Has the paper been pressure-tested against the main rejection dimensions: insufficient contribution, weak clarity, weak empirical effect, incomplete evaluation, and questionable design?

Finding Format

Report findings in this order:

  • High: submission-blocking or claim-distorting issues
  • Medium: credibility or reader-friction issues
  • Low: consistency and polish issues

Each finding should include:

  • exact file reference
  • what is wrong
  • why it matters
  • the minimum safe fix

If no major problems exist, say that explicitly and then list only the residual risks or final checks still worth doing.

Common Failure Modes

  • Abstract promise stronger than Results support
  • Figure panel mentioned in text but not actually indexed or explained
  • Legend still describing an old version of the plot
  • Supplementary figure cited at whole-figure level when the argument depends on one panel
  • Metric names drifting between sections
  • Discussion slipping into mechanism-level language not earned by the evidence
  • Nature Portfolio submission blocked late by missing accession IDs, undeclared sharing restrictions, undisclosed AI use, or missing raw image support
  • Submission-form title or abstract drifting away from the latest manuscript
  • The manuscript reading cleanly on the surface while still failing a reviewer-style contribution or evaluation check

Output Standard

End the audit with:

  • a one-sentence readiness assessment
  • the top remaining risk
  • the next highest-leverage fix before submission
Related skills
Installs
10
GitHub Stars
156
First Seen
Apr 13, 2026