anonymize-paper

Files (5)

• LICENSE
• SKILL.md
• camera-ready-reversal.md · references/
• leak-catalog.md · references/
• scan_anonymization.py · scripts/

SKILL.md (first lines)

# Anonymize Paper

> **LFL 本地化说明（2026-10-10）**：源自 ShaishavMaisuria/research-paper-lifecycle-skills@f9a0ed8f0f1770fb0321c5b022d55ed34c939927（MIT，LICENSE 随附）。scripts/scan_anonymization.py 纯 stdlib，本机已实测（与 preflight-check 的 anonymization 段结果一致，check-id 共享属实；独立运行支持 --venue/--blind）。

Make a LaTeX submission genuinely double-blind — then undo it cleanly after
acceptance. Anonymization leaks are documented desk-reject grounds (CHI and
NeurIPS state this explicitly, including leaks in supplementary material and
linked repos), and de-anonymization done by hand routinely leaves placeholder
authors or dead `anonymous.4open.science` links in the published PDF. This
skill runs the deep sweep in both directions and records every change so the
reversal is mechanical, not archaeological.

## When to use

- "Anonymize my paper for NeurIPS / CHI / ICML / KDD ..." / "blind this"
- "Did I leak my identity anywhere?" / "double-blind check" before submission
- "Rewrite my self-citations in third person"
- "My code/data links identify me — what do I do?"
- After acceptance: "de-anonymize", "restore the authors", "prepare the
  camera-ready author block"
- Called from `tailor-to-venue` (anonymization sweep step) or before
  `preflight-check` (final gate).

## Inputs

1. The main `.tex` file (with `\documentclass`); `\input`/`\include` files
   are followed automatically.
2. The venue profile `venues/conferences/<venue>-<year>.yml` (schema in
   `venues/schema.yml`) — supplies the blind level (single/double/triple) and
   the `cfp_url`. No profile? Ask the user for the blind level or create a
   profile with `parse-cfp`.
3. Optional but recommended: the compiled PDF (metadata check), the
   supplementary directory, and the author/institution names to grep for.

## Process — anonymize (submission)

1. **Resolve the blind level, then re-verify it live — mandatory.** Read the
   venue profile; fetch the `cfp_url` and confirm the blind level and the
   venue's anonymization policy wording (what counts as a violation, whether
   acknowledgments must be removed, whether anonymized artifact links are
   allowed). Single-blind venues (e.g. SIGSPATIAL) need *no* anonymization —
   tell the user and stop instead of mangling a fine paper.

2. **Run the deep scan:**

   ```
   python3 scripts/scan_anonymization.py paper.tex \
       --venue venues/conferences/<venue>-<year>.yml \
       --supplementary <supp-dir> --names "Jane Doe,Example University"
   ```

   Flags: `--blind double` (no profile), `--pdf paper.pdf` (explicit PDF),
   `--no-pdf`, `--json`, `--strict` (warnings also fail), `--force` (scan at
   single-blind venues anyway), `--no-inputs`. Exit codes: 0 clean, 1 leaks
   found, 2 bad arguments. The scanner covers: author/affiliation/email/
   ORCID/`\thanks` blocks, acknowledgments, funding/grant ids, identifying
   links, bare emails, first-person self-citations, institutional
   self-references, `pdfauthor`, LaTeX comments, `.bib` files, home-directory
   paths, compiled-PDF metadata bytes, and the supplementary tree.

3. **Fix findings with reversible edits.** Work through ERRORs first, then
   WARNs (each is a judgment call — discuss, don't bulk-delete). For the fix
   recipe per leak class — author block, acknowledgments, self-citations,
   repo/dataset links, metadata, supplementary — follow
   [references/leak-catalog.md](references/leak-catalog.md). Two rules:
   - Prefer the `\ifanon` toggle so the camera-ready flip is one line; fall
     back to a `submission-anon` git branch when source will be uploaded
     (arXiv, supplementary zips) — toggles leak the real names in source.
   - Rewrite self-citations in third person ("Doe et al. [12] showed"), never
     as "Anonymous [12]" — unless the cited work is itself unpublished.

4. **Record the reversal manifest.** For every change write one line into
   `anonymization-manifest.md` (kept OUT of the submission zip): what was
   removed/rewritten, file:line, and the exact original text (grant numbers,
   acknowledgment paragraph, real repo URL). Format in
   [references/camera-ready-reversal.md](references/camera-ready-reversal.md).

5. **Sweep the supplementary material.** Re-run step 2 with `--supplementary`
   after fixes. Ship code as a clean export (no `.git`), clear notebook
   outputs/metadata, remove LICENSE/AUTHORS copyright names, and host
   artifacts on an anonymized mirror (anonymous.4open.science) — never Drive/
   Dropbox/GitHub links that expose the account.

6. **Verify the compiled PDF.** Recompile, re-run the scan so the PDF
   metadata check runs on the fresh PDF, and do the manual pass the scanner
   cannot: figures with lab logos or terminal screenshots showing usernames,
   dataset descriptions that name the institution, watermarks.

7. **Re-run until clean, then gate.** Iterate scan → fix → scan to exit 0.
   Then run the full `preflight-check` (it validates the documentclass
   invocation and the rest of the desk-reject surface).

## Process — de-anonymize (camera-ready)

8. After acceptance, reverse using the manifest plus:

   ```
   python3 scripts/scan_anonymization.py paper.tex --mode camera-ready
   ```

   This flags the leftovers: placeholder/empty author blocks, lingering
   `anonymous.4open.science` links, `[review,anonymous]` class options,
   `neurips_<year>` without `[final]`, `\anontrue` toggles, "omitted for
   review" wording, and missing acknowledgments. Restore the author block so
   it matches the copyright form (ACM eRights / IEEE eCF) EXACTLY — walk
   [references/camera-ready-reversal.md](references/camera-ready-reversal.md)
   — then hand off to `prepare-camera-ready` for the venue rail.

## Output

- A findings report (text or `--json`) with severity, check id, `file:line`.
- The edited `.tex`/supplementary files (with the user's approval, one leak
  class at a time) plus `anonymization-manifest.md` for the reversal.
- At camera-ready: a leftover report and the restored sources.

## Relationship to preflight-check

`preflight-check` runs the same source-level anonymization checks (shared
check ids, `anonymization/*`) as one gate among many; this skill is the deep

From deyi2026/llm-first-loop, MIT.

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