revise-applied-paper

A Claude Code skill that referees an applied economics paper (PDF) against the writing standards in Marc F. Bellemare’s Doing Economics (MIT Press, 2022) and against empirical readability norms for the field. Think of it as an AI referee panel: three cold referees each write a report, a synthesis shows where they agree, and a readability report scores every section. It is read-only — it never edits your manuscript.

What sets it apart from other AI writing tools: its standard of reference. Instead of improvising what “good academic writing” means, every comment is anchored to a published, widely cited authority in applied economics — Bellemare’s book — not to a model’s private taste.

[!IMPORTANT] This is a writing-craft tool, not scientific peer review. It checks the manuscript against Bellemare’s recommendations on structure, exposition, and presentation — does the introduction follow Head’s formula, is the data section complete, are the tables self-explanatory, does the abstract read for a general audience. The referee reports are about how the paper is written, not about whether the economics is correct. It does not judge the validity of your identification, the soundness of your model, the correctness of your results, or the merit of your contribution. That judgment remains yours and your actual referees’.

What it does

Applied papers get rejected for avoidable writing reasons — a buried research question, a thin data section, an introduction that overpromises, tables a reader can’t reconstruct. Bellemare wrote down the unspoken norms that prevent this. This skill turns those norms into a repeatable, multi-referee check on your own draft.

How it compares

• revise-applied-paper (open · free · yours) is anchored to a citable authority, runs locally so your unpublished draft never leaves your machine, costs $0 on top of Claude, and is open to fork and adapt. Several independent referees let you see which comments are robust — a 3/3 finding is a different thing from a 1/3 one.
• Hosted alternatives (paid · hosted · deep compute) trade those for raw horsepower — whole-argument stress tests, deep proof-chasing, a polished shareable report — at the cost of uploading your unpublished work to someone else’s servers.

How it works

[1 mechanical]   PDF → local marker → blocks → chunks.json + paper.md + figures   (Python, no LLM)
[2 referees]     N identical cold referees → referee_1..N.md → synthesis.md
[3 readability]  score.py on paper.md → readability.md                  (parallel with stage 2)

1. Ingest — marker parses the PDF locally into