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 (global install): replace ~/.codex/skills with ~/.claude/skills
# Claude Code (project-local install): 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