# acl-org/aclpubcheck

Tools for checking ACL paper submissions

Repository: https://github.com/acl-org/aclpubcheck
Canonical: https://ross.abutalabs.com/products/aclpubcheck
Language: Python
License: MIT
License Family: permissive
Last push: 2025-12-06T18:23:29+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 55, release rhythm 35, longevity 100
- inputs: {"age_days": 1637, "days_push": 270, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1015, forks 61 (observed 2026-08-28T04:03:14.309949+00:00)

## What it is
A Python CLI tool that checks ACL conference paper PDFs for formatting violations such as font errors, margin violations, and author formatting issues. It is used by authors before submission and by publication chairs to validate camera-ready papers against ACL style guidelines.

## Use cases
- check my ACL paper pdf for formatting errors before submission
- validate camera-ready paper against ACL style guidelines
- find margin violations in a conference paper pdf
- detect font and citation formatting errors in a latex paper pdf
- verify paper formatting for acl long/short/demo papers

## When to choose
- you are submitting to an ACL venue and need to verify formatting compliance
- you are a publication chair batch-checking accepted papers
- you want to catch margin, font, or citation issues in a PDF before camera-ready upload

## When to avoid
- your paper does not use the ACL LaTeX style file
- you need general PDF linting unrelated to ACL formatting rules
- you want to check anonymous, line-numbered review versions of papers

## Facets
- artifact type: cli-tool
- maturity: active
- function: pdf, parser, developer-tools
- domain: documentation, pdf, developer-tools
- platform: python, cli, cross-platform
- tags: acl, camera-ready, formatting-checker, academic-publishing, latex, pdf-validation, natural-language-processing

## Member repositories
- acl-org/aclpubcheck (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:14.309949+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:11:25.374163+00:00, confidence not recorded.
  - readme: https://github.com/acl-org/aclpubcheck (fetched 2026-08-28T04:03:14.309949+00:00, sha 79c01c40c8db)
- Data as of 2026-08-30T08:39:29.467469+00:00.