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paper-anonymizer-academic-pdf-redactionpaper anonymizer academic PDF redaction 搜索

Agent Skill

paper-anonymizer-academic-pdf-redaction 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,352

周安装

137

GitHub Stars

公开资料未说明

下载量

824
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:paper-anonymizer-academic-pdf-redaction(paper anonymizer academic PDF redaction 搜索)
来源仓库:https://github.com/lnj22/paper-anonymizer-academic-pdf-redaction
安装命令:
openclaw skills install paper-anonymizer-academic-pdf-redaction
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install paper-anonymizer-academic-pdf-redaction

简介

自动编辑 PDF 文档内容以实现学术匿名盲审。

  • 适用于论文投稿前的文本脱敏处理需求。paper-anonymizer-academic-pdf-redaction 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 通过 clawhub 安装,依赖特定解析库运行。
  • 可能涉及文件读写操作,需确认本地或远程路径权限。
  • 建议在使用前验证是否支持目标 PDF 格式及隐私合规性。

SKILL.md

name
academic-pdf-redaction
description
Redact text from PDF documents for blind review anonymization

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")

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

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按下载量换算721

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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