本文へ移動
cccskills
無料GitHub で公開

academic-pdf-redaction

Redact text from PDF documents for blind review anonymization

インストール方法を見る

含まれるファイル(1)

  • SKILL.md3.7 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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")

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

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.

日本語の概要は準備中です。原文の説明を表示しています。

benchflow-ai/skillsbench1,8422026年10月13日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

benchflow-ai/skillsbench1,8422026年10月13日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

benchflow-ai/skillsbench1,8422026年10月13日 更新

Analyze failed GitHub Action jobs for a pull request.

日本語の概要は準備中です。原文の説明を表示しています。

benchflow-ai/skillsbench1,8422026年10月13日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

benchflow-ai/skillsbench1,8422026年10月13日 更新

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.

日本語の概要は準備中です。原文の説明を表示しています。

benchflow-ai/skillsbench1,8422026年10月13日 更新

benchflow-ai のスキルをすべて見る

このスキルの問題を報告する