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Submission Readiness Audit

  • 41 installs
  • 1 repo stars
  • Updated July 31, 2026
  • jurgendn/agent-skills

Helps with security tasks.

About

submission-readiness-audit is a Claude Code skill for security. It helps solo builders move faster with AI-assisted development.

  • submission-readiness-audit
  • Security
  • AI-coding skill

Submission Readiness Audit by the numbers

  • 41 all-time installs (skills.sh)
  • Ranked #1,410 of 2,203 Security skills by installs in the Skillselion catalog
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
npx skills add https://github.com/jurgendn/agent-skills --skill submission-readiness-audit

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Listed on Skillselion
Installs41
repo stars1
Last updatedJuly 31, 2026
Repositoryjurgendn/agent-skills

What it does

Helps with security tasks.

Files

SKILL.mdMarkdownGitHub ↗

Submission Readiness Audit

The last pass before submission is not another writing pass. It is a risk audit: what would make reviewers distrust the paper, misunderstand the contribution, or find a preventable compliance issue?

Use this when

  • The user has a draft or near-final paper.
  • The user asks what is missing before submission.
  • The paper needs a checklist against venue or artifact requirements.
  • The user wants to catch overclaims, inconsistent numbers, missing references, or appendix drift.
  • The user is preparing camera-ready revisions.

Do not use this when

  • The paper argument is still unclear. Use paper-argument-planner first.
  • The user needs new related work prose. Use related-work-writer.
  • The user needs artifact packaging rather than paper audit. Use artifact-release-packager.

Workflow

1. Identify submission context

Record:

  • venue or format if known
  • page limit
  • anonymity requirements
  • required sections or checklists
  • artifact/reproducibility expectations
  • current draft components available

If the venue is unknown, run a general research-paper audit and mark venue-specific items as unknown.

2. Audit claims against evidence

For abstract, introduction, results, discussion, and conclusion:

  • list major claims
  • identify supporting figure/table/proof/citation
  • mark unsupported or overstated claims
  • check that conclusion does not exceed results
  • check that limitations do not contradict contributions

Unsupported claims should be narrowed, supported, or removed.

3. Audit experimental and numerical consistency

Check:

  • numbers match between text, tables, captions, and appendix
  • metrics are defined consistently
  • higher/lower-is-better is clear
  • baselines are named consistently
  • datasets and splits are described consistently
  • statistical uncertainty is reported where it matters
  • ablations correspond to claimed mechanisms

4. Audit paper mechanics

Check:

  • every figure/table is referenced in order
  • captions are self-contained
  • appendix references resolve
  • citations support the sentences they are attached to
  • notation is introduced before use
  • acronyms are defined
  • method names are consistent
  • limitations, ethics, and broader impact are present if expected
  • anonymization is preserved for double-blind submissions

5. Classify blockers

Use severity levels:

  • Blocker — likely to cause rejection, desk rejection, or serious reviewer distrust.
  • Major — weakens the paper but may be fixable quickly.
  • Minor — polish or clarity issue.
  • Venue-specific unknown — cannot verify without venue requirements.

Do not bury blockers in a long checklist.

Output format

# Submission Readiness Audit

## Verdict
Ready / Not ready / Conditionally ready

## Blockers

## Major issues

## Minor issues

## Claim-evidence consistency
| Claim | Location | Evidence | Status | Fix |
|---|---|---|---|---|

## Figures, tables, and appendix checks

## Venue/checklist items

## Final action list

Quality bar

A good audit should reduce preventable reviewer objections. It should be specific enough that the user can fix issues directly, not just feel vaguely worried.

Related skills

Securityappsec

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