Agent skill · Code Review & Quality

auto-paper-improvement-loop

Autonomously improve a generated paper via Gemini review through gemini-review MCP → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill auto-paper-improvement-loop-wanshuiyin-auto-claude-code-res --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 13 KB
Bundled scripts: none
Path: skills/ai-llm/auto-paper-improvement-loop-wanshuiyin-auto-claude-code-res/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

> Override for Codex users who want **Gemini**, not a second Codex agent, to act as the reviewer. Install this package **after** `skills/skills-codex/*`. # Auto Paper Improvement Loop: Review → Fix → Recompile Autonomously improve the paper at: **$ARGUMENTS** ## Context This skill is designed to run **after** Workflow 3 (`/paper-plan` → `/paper-figure` → `/paper-write` → `/paper-compile`). It takes a compiled paper and iteratively improves it through external LLM review. Unlike `/auto-review-loop` (which iterates on **research** — running experiments, collecting data, rewriting narrative), this skill iterates on **paper writing quality** — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation. ## Constants - **MAX_ROUNDS = 2** — Two rounds of review→fix→recompile. Empirically, Round 1 catches structural issues (4→6/10), Round 2 catches remaining presentation issues (6→7/10). Diminishing returns beyond 2 rounds for writing-only improvements. - **REVIEWER_MODEL = `gemini-review`** — Gemini reviewer invoked through the local `gemini-review` MCP bridge. Set `GEMINI_REVIEW_MODEL` if you need a specific Gemini model override. - **REV

What's inside
Steps it walks through
  1. Context
  2. Constants
  3. Inputs
  4. State Persistence (Compact Recovery)
  5. Workflow
  6. Step 0: Preserve Original
  7. Step 1: Collect Paper Text
  8. Step 2: Round 1 Review
  9. Step 2b: Human Checkpoint (if enabled)
  10. Step 3: Implement Round 1 Fixes
  11. Step 4: Recompile Round 1
  12. Step 5: Round 2 Review
  13. Step 5b: Human Checkpoint (if enabled)
  14. Step 6: Implement Round 2 Fixes
Ships with 1 file
  • metadata.json
Commands it runs
cp paper/main.pdf paper/main_round0_original.pdf
Collect all sections in order
for f in paper/sections/*.tex; do
echo "% === $(basename $f) ==="
cat "$f"
done > /tmp/paper_full_text.txt
cd paper && latexmk -C && latexmk -pdf -interaction=nonstopmode -halt-on-error main.tex
cp main.pdf main_round1.pdf
cp main.pdf main_round2.pdf
echo "Pages: $PAGES (limit: 9 main body for ICLR/NeurIPS)"
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About this skill
What does the auto-paper-improvement-loop skill do?

Autonomously improve a generated paper via Gemini review through gemini-review MCP → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill auto-paper-improvement-loop-wanshuiyin-auto-claude-code-res --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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