auto-paper-improvement-loop
Auto Paper Improvement Loop runs 2 rounds of autonomous review-fix-recompile cycles to enhance paper quality. Each round uses fresh LLM review to catch structural issues, theoretical inconsistencies, and presentation gaps, then implements fixes and recompiles. Optional style reference and edit whitelist let you steer structural choices or constrain which files the loop modifies.
Auto Paper Improvement Loop autonomously refines papers through 2 rounds of GPT review, fix implementation, and recompilation.
AI-generated summary based on this skill's SKILL.md
Install
wanshuiyin/Auto-claude-code-research-in-sleep/auto-paper-improvement-loop · repository language: Python
git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep
cp -r Auto-claude-code-research-in-sleep/skills/auto-paper-improvement-loop ~/.claude/skills/auto-paper-improvement-loopnpx skillfed install wanshuiyin/Auto-claude-code-research-in-sleep/auto-paper-improvement-loopFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How does auto-paper-improvement-loop improve my paper automatically?
Auto Paper Improvement Loop runs 2 rounds of autonomous review-fix-recompile cycles. Each round uses fresh LLM review to identify structural issues, theoretical inconsistencies, and presentation gaps, then automatically implements fixes and recompiles your manuscript. This iterative approach progressively enhances paper quality without manual intervention between rounds.
Can I polish my paper with AI review using this tool?
Yes. Auto Paper Improvement Loop applies AI-driven review feedback to polish writing, fix claims, and enhance presentation. The tool autonomously identifies weaknesses in each cycle and applies targeted improvements, letting you refine your manuscript from draft to submission-ready state through iterative enhancement.
What happens during each improvement cycle?
Auto Paper Improvement Loop performs review, fix, and recompile steps in each cycle. The LLM reviews your paper to spot issues, generates fixes for those problems, and recompiles the updated manuscript. With 2 full rounds, you get multiple passes of fresh review and refinement to progressively strengthen your paper's quality.
Can I constrain which files the loop modifies?
Yes. Auto Paper Improvement Loop supports an edit whitelist that lets you constrain edits to specific paths or operations during resubmit or camera-ready phases. This gives you fine-grained control over which sections the tool can modify, protecting critical content while allowing targeted refinement elsewhere.
Does auto-paper-improvement-loop track changes across review rounds?
Auto Paper Improvement Loop maintains an audit trail of all review rounds and fixes, enabling state recovery if needed. This record-keeping lets you understand what changed across cycles and recover previous manuscript states if necessary during your iterative refinement process.
How can I steer the improvement process?
Auto Paper Improvement Loop offers optional style reference and edit whitelist parameters to guide structural choices and control which files the loop modifies. These steering mechanisms let you align the autonomous improvement process with your preferences while maintaining the efficiency of multi-round review-fix cycles.
SKILL.md
rendered from the published skill — quoted content, verbatim
Auto Paper Improvement Loop: Review → Fix → Recompile
> 🔒 Do not wrap this skill in /loop, /schedule, or CronCreate. It
> already loops internally (review → fix → recompile) with its own round
> structure and a deliberate fresh-reviewer bias guard each round (no
> codex-reply). Re-asking it to "improve the paper" on a
> wall-clock timer produces no new signal — quality changes when the review
> changes, not when the clock ticks — and a timed re-run that also accepts its
> own output to decide when to stop crosses into self-acquittal
> (acceptance-gate.md). Schedule the external wait that precedes it, not the
> improvement loop. See
>
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