AI-research-feedback，一个给 Claude Code 用的学术审稿技能。

核心能同时跑六个审稿智能体，分别盯语法、前后一致性、公式、图表和论证漏洞。

还能指定 QJE、AER 这类期刊，模拟对应审稿人的挑剔程度，最后合成一份结构化审稿报告。

GitHub：http://github.com/claesbackman/AI-research-feedback…

除了完整审稿，还有轻量快查版、论文和代码一致性检查、基金申请书审阅。

一条 curl 命令就装好，可直接读 LaTeX 源文件来审，经济、金融方向的期刊也支持。

claesbackman/AI-research-feedback

Source: https://github.com/claesbackman/AI-research-feedback

Using AI to get feedback on your research

A collection of Claude Code skills for academic research review. This tool was developed by Claes Bäckman.

Skills in this folder

• Skills/review-paper.md: Full referee-style paper review command.
• Skills/review-paper-light.md: Fast 2-agent paper check.
• Skills/review-paper-code.md: Paper-code reproducibility and alignment review.
• Skills/review-pap.md: Pre-analysis plan review command.
• Skills/review-grant.md: Grant proposal review command.

Skills

review-paper — Pre-Submission Referee Report

Runs a rigorous pre-submission review of an academic paper, simulating the scrutiny of a specific journal’s editorial board. Six specialized review agents run in parallel and consolidate their findings into a single structured report.

What it reviews:

Installation:

mkdir -p ~/.claude/commands && curl -o ~/.claude/commands/review-paper.md \
  https://raw.githubusercontent.com/claesbackman/AI-research-feedback/main/Skills/review-paper.md

For a project-local install:

mkdir -p .claude/commands && curl -o .claude/commands/review-paper.md \
  https://raw.githubusercontent.com/claesbackman/AI-research-feedback/main/Skills/review-paper.md

Usage:

/review-paper
/review-paper QJE
/review-paper JF path/to/main.tex

Supported journals:

If no journal is specified, the command applies high general standards without a specific journal persona. If no path is provided, it auto-detects the main .tex file.

Output:

Saves a consolidated report to PRE_SUBMISSION_REVIEW_[YYYY-MM-DD].md in the current directory, automatically appending -v2, -v3, and so on if a file already exists.

Customization:

• Add journals or fields by editing the recognized journal names list in the skill file.
• Add project-specific context in your prompt or in a local CLAUDE.md file.
• Adjust folder discovery or save paths directly in the skill if your project structure differs from the default assumptions.

Requirements:

• Claude Code with access to the general-purpose subagent.
• A LaTeX paper. The skill reads .tex files and optionally inspects figure and table files.

review-paper-light — Quick Paper Check

Runs a fast 2-agent pre-submission check for an economics paper. It focuses on contribution, identification, causal overclaiming, and unsupported claims, and is designed for quick iteration before a full review.

Installation:

mkdir -p ~/.claude/commands && curl -o ~/.claude/commands/review-paper-light.md \
  https://raw.githubusercontent.com/claesbackman/AI-research-feedback/main/Skills/review-paper-light.md

For a project-local install:

mkdir -p .claude/commands && curl -o .claude/commands/review-paper-light.md \
  https://raw.githubusercontent.com/claesbackman/AI-research-feedback/main/Skills/review-paper-light.md

Usage:

/review-paper-light
/review-paper-light path/to/main.tex

If no path is provided, the command auto-detects the main .tex file.

Output:

Saves a short prioritized report to QUICK_REVIEW_[YYYY-MM-DD].md in the current directory, automatically versioning the filename if one already exists.

Requirements:

• Claude Code with access to the general-purpose subagent.
• A LaTeX paper.

review-paper-code — Paper-Code Reproducibility Review

Runs a paper-code review for empirical research projects. It discovers the main LaTeX paper and analysis code, checks reproducibility and code quality, maps the paper’s main empirical claims to the code, and writes a constructive report highlighting strengths, gaps to verify, and concrete next steps.

What it reviews:

Usage:

/review-paper-code
/review-paper-code path/to/main.tex
/review-paper-code path/to/main.tex path/to/code_dir
/review-paper-code path/to/main.tex path/to/code_dir full

Review depth:

• main: default; focuses on main scripts and core outputs
• full: reviews all detected code files in scope

Output:

Writes a report to code_review_report.md in the current working directory.

Requirements:

• Claude Code with access to the general-purpose subagent.
• A LaTeX paper plus Stata, R, or Python analysis code.

review-pap — Pre-Analysis Plan Review

Runs a 6-agent pre-submission review of a pre-analysis plan (PAP). The command auto-detects the main PAP and supporting files, then evaluates writing quality, specification completeness, internal consistency, identification strategy, statistical analysis, implementation details, and registry or journal fit.

Installation:

mkdir -p ~/.claude/commands && curl -o ~/.claude/commands/review-pap.md \
  https://raw.githubusercontent.com/claesbackman/AI-research-feedback/main/Skills/review-pap.md

For a project-local install:

mkdir -p .claude/commands && curl -o .claude/commands/review-pap.md \
  https://raw.githubusercontent.com/claesbackman/AI-research-feedback/main/Skills/review-pap.md

Usage:

/review-pap
/review-pap AEA
/review-pap QJE path/to/pap.tex

Supported targets:

• Trial registries: AEA, EGAP, OSF, ClinicalTrials, ISRCTN
• Journal standards: AER, QJE, JPE, RESTUD, AEJ, JEEA
• General standards: top-journal, working-paper

If no target is specified, the command defaults to top-journal. If no path is provided, it auto-detects the main PAP file.

Supporting files it can inspect:

• Power calculations and sample-size worksheets
• Survey instruments and questionnaires
• Randomization protocols and sampling frames
• Code skeletons and mock tables
• Data dictionaries and ethics materials

Output:

Saves a consolidated report to PAP_REVIEW_[YYYY-MM-DD].md in the current directory.

Requirements:

• Claude Code with access to the general-purpose subagent.
• A PAP in a readable format such as .md, .txt, or .tex. The skill can also attempt to work with .pdf and .docx, while noting accessibility limitations if needed.

review-grant — Grant Proposal Review

Runs a 6-agent pre-submission panel review of a grant proposal. The command auto-detects the main proposal and supporting documents, then evaluates clarity, compliance signals, internal consistency, significance, innovation, research design, feasibility, budget logic, team readiness, and fit to the target funder or program.

Installation:

mkdir -p ~/.claude/commands && curl -o ~/.claude/commands/review-grant.md \
  https://raw.githubusercontent.com/claesbackman/AI-research-feedback/main/Skills/review-grant.md

For a project-local install:

mkdir -p .claude/commands && curl -o .claude/commands/review-grant.md \
  https://raw.githubusercontent.com/claesbackman/AI-research-feedback/main/Skills/review-grant.md

Usage:

/review-grant
/review-grant NSF
/review-grant NIH path/to/proposal.pdf

Supported funders/programs:

• US federal science and health: NSF, NIH, ERC, HorizonEurope
• General proposal standards: major-funder, foundation

If no target is specified, the command defaults to major-funder. If no path is provided, it auto-detects the main proposal file.

Supporting files it can inspect:

• Budgets and budget justifications
• Timelines and workplans
• Biosketches, CVs, and personnel documents
• Data-management plans, mentoring plans, and facilities statements
• Letters of support, appendices, and supplementary materials

Output:

Saves a consolidated report to GRANT_PROPOSAL_REVIEW_[YYYY-MM-DD].md in the current directory.

Requirements:

• Claude Code with access to the general-purpose subagent.
• A proposal in a readable format such as .md, .txt, or .tex. The skill can also attempt to work with .pdf and .docx, while noting accessibility limitations if needed.

License

MIT — free to use, adapt, and share.