AI-research-feedback skill

A skill set for academic work: paper review, pre-analysis plan check, grant proposal review, code audit and slide decks.

  • 560 ★ on the repository
  • Last commit
  • MIT
Skill

Extended access

Why this level

  • Multi-agent runs spend quota and read the paper and code sources of the project.
  • The typo-check skill offers to apply edits to `.tex` files: review the diff before accepting.
All reasons and checks

claesbackman/ai-research-feedback

Install

In your terminal, with SkillFoxx CLI

npx skillfoxx add skills/ai-research-feedback

Detects the agents on your machine, shows the access level and pins the version.

Other ways to install

Access and checks

How we review

Extended accessThe tool runs code or makes network calls and only reads external data.

  • Multi-agent runs spend quota and read the paper and code sources of the project.
  • The typo-check skill offers to apply edits to `.tex` files: review the diff before accepting.

This is third-party code. Review the repository files before installing.

What it does

AI-research-feedback is thirteen Claude Code skills for researchers. The review skills read a document and write a structured report: `review-paper` runs eight agents for a referee-style review, `review-paper-light` quickly checks contribution and identification, `review-paper-checks` hunts typos and inconsistencies, and there are checks for pre-analysis plans and grant proposals. Other skills audit analysis code (`review-paper-code`, `audit-analysis`), explain changes, prepare short paper versions and slide decks in Quarto, Beamer and PowerPoint. `pdf-to-markdown` converts PDFs to text. Reports are saved in the project `reviews/` folder with the date in the filename.

Who it is for. Economists and other researchers preparing a paper for submission who want independent text and code checks before reviewers see it.

Good fit when

  • A draft is ready and a pre-submission review should list its weak spots.
  • A pre-analysis plan or grant proposal is checked against registry or funder requirements.
  • Analysis code is audited: does it produce the numbers the paper reports?
  • A paper becomes a policy brief or slides with live figures.
  • Spelling and internal references are proofread before submission with no editorial changes.

Not a fit when

  • You need one short paragraph fix: a multi-agent run is too heavy.
  • The work is not academic, for example marketing copy.
  • Your plan limit is small: the full review launches eight agents.

Example request

Run /review-paper-light on draft.tex and show the main concerns about contribution and identification.

Limitations

The review skills run only by typing `/name`, since Claude must not start a multi-agent check by itself. A full review spends a large share of quota. Reports go to `reviews/` and the skills deliberately ignore old drafts and earlier reports. The slide-deck skills and `pdf-to-markdown` may be picked up by the agent on its own. Beamer and PowerPoint decks need LaTeX and PowerPoint itself. The material is tuned to empirical economics.

How to disable. Delete the folders you no longer want from `~/.claude/skills/` (for example `review-paper`) and restart Claude Code. Reports in `reviews/` stay in the project.

FAQ

How do I install one skill?

Copy its folder from `Skills/` to `~/.claude/skills/<name>`. All but the three deck skills are a single `SKILL.md`.

Why do skills not run by themselves?

The review skills set `disable-model-invocation: true`, so a multi-agent run starts only on an explicit command.

Where do reports go?

To the project `reviews/` folder, for example `PRE_SUBMISSION_REVIEW_[date].md`. A rerun adds a `-v2` suffix.

How does light differ from the full review?

Light runs two agents and checks contribution, identification and causal overclaiming. The full review uses eight agents.

What about the decks?

Three skills build one deck in Quarto reveal.js, LaTeX Beamer and PowerPoint, with figures the presenter steps through.

Discussion

No questions or reviews yet.

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README in short

The README groups skills into text feedback, code feedback, communicating results and utilities, with install options and a command table listing agent counts and report names.

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