manuscript-review-revise
AI-powered manuscript review and revision system inspired by APRES (ICLR 2026). Evaluates scientific manuscripts using ScholarEval 8-dimension rubric plus citation-predictive heuristics, then performs targeted revisions while preserving core scientific claims. Outputs before/after comparison with improvement metrics. Use when the user says "/review", "帮我审一下", "review my manuscript", "improve this paper", "polish this draft", or provides a manuscript for quality improvement. Also triggered by "审稿", "修改论文", "润色".
Best use case
manuscript-review-revise is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
AI-powered manuscript review and revision system inspired by APRES (ICLR 2026). Evaluates scientific manuscripts using ScholarEval 8-dimension rubric plus citation-predictive heuristics, then performs targeted revisions while preserving core scientific claims. Outputs before/after comparison with improvement metrics. Use when the user says "/review", "帮我审一下", "review my manuscript", "improve this paper", "polish this draft", or provides a manuscript for quality improvement. Also triggered by "审稿", "修改论文", "润色".
Teams using manuscript-review-revise 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/manuscript-review-revise/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How manuscript-review-revise Compares
| Feature / Agent | manuscript-review-revise | 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?
AI-powered manuscript review and revision system inspired by APRES (ICLR 2026). Evaluates scientific manuscripts using ScholarEval 8-dimension rubric plus citation-predictive heuristics, then performs targeted revisions while preserving core scientific claims. Outputs before/after comparison with improvement metrics. Use when the user says "/review", "帮我审一下", "review my manuscript", "improve this paper", "polish this draft", or provides a manuscript for quality improvement. Also triggered by "审稿", "修改论文", "润色".
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.
Related Guides
SKILL.md Source
# Manuscript Review & Revision System Evaluate and improve scientific manuscripts through a closed-loop process: **Score → Identify Weaknesses → Revise → Re-score → Compare**. Inspired by APRES (Meta Superintelligence Labs, ICLR 2026). ## Core Principles 1. **Never modify core scientific claims** — preserve all data, results, and conclusions 2. **Never add unverified data or fabricated citations** — only restructure existing content 3. **Every revision has a stated reason** — traceable, auditable changes 4. **Quantitative before/after comparison** — ScholarEval scores pre and post revision 5. **Improve presentation, not science** — clarity, structure, flow, completeness ## When to Use - User says `/review` or `/review <path-to-manuscript>` - User asks "帮我审一下这篇论文" or "review my manuscript" - User asks "润色" or "polish this draft" - After ScienceClaw generates a research report, offer: "需要用审修系统优化这份报告吗?" --- ## Workflow ### Phase 1: ScholarEval Assessment (8 Dimensions) Score the manuscript on each dimension (0.00–1.00): | Dimension | Weight | Evaluation Criteria | |-----------|--------|-------------------| | **Novelty** | 15% | Does this advance knowledge? Are claims clearly differentiated from prior work? | | **Rigor** | 25% | Methodology sound? Statistics correct? Controls adequate? Sample sizes reported? | | **Clarity** | 10% | Writing clear? Figures self-explanatory? Logical flow between sections? | | **Reproducibility** | 15% | Methods detailed enough to replicate? Software versions stated? Data accessible? | | **Impact** | 20% | Does this matter for the field? Broad or narrow implications? | | **Coherence** | 10% | Do all parts fit together? Introduction → Methods → Results → Discussion aligned? | | **Limitations** | 3% | Are limitations honestly acknowledged? Not buried or trivialized? | | **Ethics** | 2% | Ethical standards met? IRB mentioned if applicable? Conflicts disclosed? | Compute weighted average. Output initial verdict: `accept` (≥0.75), `minor_revision` (≥0.60), `major_revision` (≥0.40), `reject` (<0.40). ### Phase 2: Citation-Impact Heuristics Evaluate 10 presentation factors that predict higher citation impact: | # | Factor | Check | Weight | |---|--------|-------|--------| | 1 | **Title specificity** | Contains key finding or quantitative result? Not vague? | High | | 2 | **Abstract conclusion** | Has a clear, quantitative take-home message? | High | | 3 | **Figure self-sufficiency** | Can each figure be understood from its caption alone? | High | | 4 | **Methods reproducibility** | Software versions, parameters, thresholds all stated? | Medium | | 5 | **Statistical reporting** | Effect sizes + CIs alongside p-values? Test assumptions verified? | Medium | | 6 | **Discussion balance** | Presents counter-arguments and alternative interpretations? | Medium | | 7 | **Limitations honesty** | Dedicated section with specific (not generic) limitations? | Medium | | 8 | **Introduction funnel** | Narrows from broad context → gap → specific question? | Low | | 9 | **Reference recency** | Includes papers from last 2 years? Not relying on outdated reviews? | Low | | 10 | **Data availability** | States where data/code can be accessed? | Low | Score each 0–1. Flag factors scoring below 0.5 as revision targets. ### Phase 3: Generate Revision Plan Sort all identified weaknesses by estimated impact on manuscript quality. Output a numbered revision plan: ``` ## 修订计划(按影响力排序) 1. [HIGH] 标题过于笼统 → 改为包含主要发现的具体标题 当前: "The Role of THBS2 in Cancer" 建议: "THBS2 Overexpression Associates with M2 Macrophage Infiltration and Poor Survival Across 17 Cancer Types" 理由: 具体标题平均被引用量高 22%(Paiva et al., 2012) 2. [HIGH] 摘要缺少定量结论 → 添加关键数字 当前: "THBS2 was significantly upregulated in multiple cancers" 建议: "THBS2 was significantly upregulated in 17/33 TCGA cancer types (Wilcoxon p<0.001), with highest expression in PAAD (HR=2.31, 95%CI: 1.45-3.68)" 理由: 摘要中包含具体数字的论文被引用量高 29% 3. [MEDIUM] Figure 2 caption 缺少统计方法说明 ... ``` ### Phase 4: Execute Revisions Apply each revision to the manuscript text. For each change: - Quote the original text (2-3 lines of context) - Show the revised text - State the reason **Constraints**: - Do NOT change any data values, p-values, effect sizes, or sample sizes - Do NOT add citations that were not in the original or verified through tools - Do NOT change the interpretation of results - Do NOT add new claims or conclusions - DO improve: titles, headings, topic sentences, transitions, figure captions, methods detail, limitation specificity, discussion balance ### Phase 5: Re-score and Compare Re-run ScholarEval on the revised manuscript. Output comparison: ``` ## 审修效果对比 | Dimension | Before | After | Change | |---------------|--------|-------|--------| | Novelty | 0.72 | 0.72 | — | | Rigor | 0.68 | 0.75 | +0.07 | | Clarity | 0.55 | 0.78 | +0.23 | | Reproducibility| 0.60 | 0.82 | +0.22 | | Impact | 0.70 | 0.70 | — | | Coherence | 0.65 | 0.80 | +0.15 | | Limitations | 0.40 | 0.75 | +0.35 | | Ethics | 0.90 | 0.90 | — | | | | | | | **Weighted** | **0.66**| **0.76** | **+0.10** | | **Verdict** | minor_revision | accept | ⬆ | Citation-impact heuristics: 4/10 → 8/10 factors above threshold 共执行 12 处修订,主要改善了清晰度(+0.23)和可复现性(+0.22)。 核心科学主张和数据未做任何修改。 ``` ### Phase 6: Output Save revised manuscript and revision report: ``` outputs: 📄 reports/manuscript_revised.md — 修订后的完整稿件 📋 reports/revision_report.md — 修订报告(所有变更 + 理由) 📊 reports/scholareval_comparison.md — 评分对比表 ``` --- ## Revision Patterns Library ### Title Improvements | Pattern | Before | After | |---------|--------|-------| | Add key finding | "Role of X in Y" | "X Promotes Y Through Z Mechanism" | | Add quantitative | "X is associated with Y" | "X Overexpression in N/M Cancers Associates with Poor Survival (HR=...)" | | Add scope | "Study of X" | "Pan-Cancer Analysis Reveals X as..." | ### Abstract Improvements - Add sample sizes: "patients" → "patients (n=438)" - Add effect sizes: "significantly different" → "significantly different (Cohen's d=0.82)" - Add confidence intervals: "HR=2.31" → "HR=2.31 (95%CI: 1.45–3.68)" ### Methods Improvements - Add software versions: "R" → "R 4.3.2" - Add package versions: "survival package" → "survival package (v3.5-7)" - Add thresholds: "significant genes" → "genes with |log2FC|>1 and FDR<0.05" - Add normalization: "normalized data" → "TMM-normalized counts (edgeR v3.40.2)" ### Discussion Improvements - Add counter-argument paragraph: "However, alternative explanations include..." - Add comparison with conflicting studies: "In contrast to [Author, Year] who found..." - Convert generic limitations to specific: "small sample size" → "limited sample size in the PAAD cohort (n=178) may reduce power to detect survival differences in subgroup analyses" --- ## Integration with Other Skills - After any Research Recipe completion, offer: "需要用审修系统优化报告吗?输入 /review" - Works on any markdown file in the project directory - Can be applied to user-uploaded manuscripts (paste text or provide file path)
Related Skills
Review Writing — 学术综述逐节写作方法论
Use this skill when the user asks to write a literature review, review article, or 综述 based on an outline. Trigger keywords: "写综述", "write review", "综述写作", "按大纲写", "逐节写", "review section", "写第N节". This skill orchestrates the ENTIRE review writing process from outline to finished manuscript.
peer-review
Conduct thorough academic peer reviews with structured feedback using load_pdf and arxiv_to_prompt
Literature Search & Review
## Overview
zinc-database
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
Academic Writing
## Overview
scientific-visualization
## Overview
venue-templates
Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.
vaex
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
uspto-database
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
uniprot-database
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
umap-learn
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.