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AI-assisted peer review tools, workflows, and quality standards
Passed 19 of 23 test cases with the skill, and 17 of 23 without it.
3 fixed16 passed either way3 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
A scientific journal editor is designing an automated manuscript triage system before reviewer assignment. What specific pre-assignment screening checks can AI tools perform at this editor-triage stage?
| Checked | Without skill | With skill |
|---|---|---|
| The editor-side workflow incorporates screening for desk rejection scope thresholds, reviewer expertise matching, conflict of interest detection, and duplicate or image manipulation screening. | Missed | Passed |
The instructions your model reads when the skill loads.
A skill for leveraging AI-assisted tools in the peer review process, covering both author-side self-review and editor-side manuscript screening. Addresses tool selection, prompt engineering for review tasks, limitations and biases of LLM-generated reviews, quality assurance workflows, and ethical guidelines for AI use in peer review.
AI-assisted peer review tools operate at multiple stages of the publication pipeline. Understanding where automation adds genuine value and where it introduces risk is essential for responsible adoption.
Where AI assists in peer review:
Author-side (pre-submission):
- Grammar and style checking (Grammarly, Writefull)
- Statistical result verification (statcheck, GRIM/SPRITE)
- Reference completeness checking
- Plagiarism detection (iThenticate, Turnitin)
- Readability scoring
- Structural completeness (IMRAD compliance)
Editor-side (triage and assignment):
- Desk rejection screening (scope, quality threshold)
- Reviewer matching (expertise alignment)
- Conflict of interest detection
- Duplicate submission detection
- Plagiarism and image manipulation screening
Reviewer-side (review assistance):
- Paper summarization for rapid assessment
- Statistical claim verification
- Reference checking (do cited papers support claims)
- Comparison with related work
- Structured review template generation
Post-review:
- Decision consistency analysis
- Review quality assessment
- Revision compliance checking
test cases passed, vs 17 / 23 without
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