Best use case
peer-review-simulator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Skill for simulating peer review feedback on manuscripts
Teams using peer-review-simulator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/peer-review-simulator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How peer-review-simulator Compares
| Feature / Agent | peer-review-simulator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Skill for simulating peer review feedback on manuscripts
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Peer Review Simulator Skill ## Purpose Simulate peer review feedback on manuscripts to identify potential issues and improve quality before submission. ## Capabilities - Analyze manuscript quality - Identify methodological issues - Check statistical validity - Assess presentation clarity - Generate reviewer comments - Prioritize improvements ## Usage Guidelines 1. Load manuscript 2. Analyze structure 3. Review methodology 4. Check statistics 5. Generate feedback 6. Prioritize issues ## Process Integration Works within scientific discovery workflows for: - Pre-submission review - Quality improvement - Manuscript refinement - Author preparation ## Configuration - Review criteria - Strictness levels - Focus areas - Output formats ## Output Artifacts - Review reports - Comment summaries - Issue prioritization - Improvement suggestions
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