
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)
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| Installs | 41 |
|---|---|
| repo stars | ★ 1 |
| Last updated | July 31, 2026 |
| Repository | jurgendn/agent-skills ↗ |
What it does
Helps with security tasks.
Files
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-plannerfirst. - 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 listQuality 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.
{
"skill_name": "submission-readiness-audit",
"evals": [
{
"id": 1,
"prompt": "I'm submitting tomorrow. Audit my paper checklist from these notes: abstract claims state-of-the-art, Table 1 only has 3 datasets, limitations section is missing, appendix has hyperparameters but no seed details, and Figure 2 is not referenced in the text.",
"expected_output": "A submission readiness audit with verdict, blockers, major/minor issues, claim-evidence consistency, and final action list.",
"files": []
},
{
"id": 2,
"prompt": "Before camera-ready, I need to check consistency: the intro says 20% improvement, results table says 18.7 average, conclusion says broadly applicable, and experiments are only on English datasets. What should I fix?",
"expected_output": "An audit that flags numerical inconsistency and overbroad claims with concrete fixes.",
"files": []
},
{
"id": 3,
"prompt": "Act as a final pre-submit reviewer for an anonymous ML conference paper. What categories of issues should I check if I can only spend two hours?",
"expected_output": "A prioritized final checklist focused on blockers and major preventable reviewer objections.",
"files": []
}
]
}