Skip to content
cccskills
FreePublished on GitHub

academic-pdf-redaction

Redact text from PDF documents for blind review anonymization

The English summary is being prepared. Showing the original description.

See how to install

Files included (1)

  • SKILL.md3.7 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

PDF Redaction for Blind Review

Redact identifying information from academic papers for blind review.

CRITICAL RULES

  1. PRESERVE References section - Self-citations MUST remain intact
  2. ONLY redact specific text matches - Never redact entire pages/regions
  3. VERIFY output - Check that 80%+ of original text remains

Common Pitfalls to AVOID

# ❌ WRONG - This removes ALL text from the page:
for block in page.get_text("blocks"):
    page.add_redact_annot(fitz.Rect(block[:4]))

# ❌ WRONG - Drawing rectangles over text:
page.draw_rect(fitz.Rect(0, 0, 600, 100), fill=(0,0,0))

# ✅ CORRECT - Only redact specific search matches:
for rect in page.search_for("John Smith"):
    page.add_redact_annot(rect)

Patterns to Redact (Before References Only)

IMPORTANT: Use FULL names/phrases, not partial matches!

  • ✅ "John Smith" (full name)
  • ❌ "Smith" (partial - would incorrectly match "Smith et al." citations in References)
  1. Author names - FULL names only (e.g., "John Smith", not just "Smith")
  2. Affiliations - Universities, companies (e.g., "Duke University")
  3. Email addresses - Pattern: *@*.edu, *@*.com
  4. Venue names - Conference/workshop names (e.g., "ICML 2024", "ICML Workshop")
  5. arXiv identifiers - Pattern: arXiv:XXXX.XXXXX
  6. DOIs - Pattern: 10.XXXX/...
  7. Acknowledgement names - Names in "Acknowledgements" section
  8. Equal contribution footnotes - e.g., "Equal contribution", "* Equal contribution"

PyMuPDF (fitz) - Recommended Approach

import fitz
import os

def redact_with_pymupdf(input_path: str, output_path: str, patterns: list[str]):
    """Redact specific patterns from PDF using PyMuPDF."""
    doc = fitz.open(input_path)
    original_len = sum(len(p.get_text()) for p in doc)

    # Find References page - stop redacting there
    references_page = None
    for i, page in enumerate(doc):
        if "references" in page.get_text().lower():
            references_page = i
            break

    for page_num, page in enumerate(doc):
        if references_page is not None and page_num >= references_page:
            continue  # Skip References section

        for pattern in patterns:
            # ONLY redact exact search matches
            for rect in page.search_for(pattern):
                page.add_redact_annot(rect, fill=(0, 0, 0))
        page.apply_redactions()

    os.makedirs(os.path.dirname(output_path), exist_ok=True)
    doc.save(output_path)
    doc.close()

    # MUST verify after saving
    verify_redaction(input_path, output_path)

REQUIRED: Verification Function

Always run this after ANY redaction to catch errors early:

import fitz

def verify_redaction(original_path, output_path):
    """Verify redaction didn't corrupt the PDF."""
    orig = fitz.open(original_path)
    redc = fitz.open(output_path)

    orig_len = sum(len(p.get_text()) for p in orig)
    redc_len = sum(len(p.get_text()) for p in redc)

    print(f"Original: {len(orig)} pages, {orig_len} chars")
    print(f"Redacted: {len(redc)} pages, {redc_len} chars")
    print(f"Retained: {redc_len/orig_len:.1%}")

    # DEFENSIVE CHECKS - fail fast if something went wrong
    if len(redc) != len(orig):
        raise ValueError(f"Page count changed: {len(orig)} -> {len(redc)}")
    if redc_len < 1000:
        raise ValueError(f"PDF corrupted: only {redc_len} chars remain!")
    if redc_len < orig_len * 0.7:
        raise ValueError(f"Too much removed: kept only {redc_len/orig_len:.0%}")

    orig.close()
    redc.close()
    print("✓ Verification passed")

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.

The English summary is being prepared. Showing the original description.

benchflow-ai/skillsbench1.9KUpdated Oct 13, 2026

AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.

The English summary is being prepared. Showing the original description.

benchflow-ai/skillsbench1.9KUpdated Oct 13, 2026

Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.

The English summary is being prepared. Showing the original description.

benchflow-ai/skillsbench1.9KUpdated Oct 13, 2026

Analyze failed GitHub Action jobs for a pull request.

The English summary is being prepared. Showing the original description.

benchflow-ai/skillsbench1.9KUpdated Oct 13, 2026

Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.

The English summary is being prepared. Showing the original description.

benchflow-ai/skillsbench1.9KUpdated Oct 13, 2026

Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.

The English summary is being prepared. Showing the original description.

benchflow-ai/skillsbench1.9KUpdated Oct 13, 2026

See all skills by benchflow-ai

Report a problem with this skill