Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
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。
Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks
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。
A user may ask you to create, edit, or analyze the contents of a .docx file. A .docx file is essentially a ZIP archive containing XML files and other resources that you can read or edit. You have different tools and workflows available for different tasks.
Use "Text extraction" or "Raw XML access" sections below
Use "Creating a new Word document" workflow
Your own document + simple changes Use "Basic OOXML editing" workflow
Someone else's document Use "Redlining workflow" (recommended default)
Legal, academic, business, or government docs Use "Redlining workflow" (required)
If you just need to read the text contents of a document, you should convert the document to markdown using pandoc. Pandoc provides excellent support for preserving document structure and can show tracked changes:
# Convert document to markdown with tracked changes
pandoc --track-changes=all path-to-file.docx -o output.md
# Options: --track-changes=accept/reject/all
You need raw XML access for: comments, complex formatting, document structure, embedded media, and metadata. For any of these features, you'll need to unpack a document and read its raw XML contents.
python ooxml/scripts/unpack.py <office_file> <output_directory>
word/document.xml - Main document contentsword/comments.xml - Comments referenced in document.xmlword/media/ - Embedded images and media files<w:ins> (insertions) and <w:del> (deletions) tagsWhen creating a new Word document from scratch, use docx-js, which allows you to create Word documents using JavaScript/TypeScript.
docx-js.md (~500 lines) completely from start to finish. NEVER set any range limits when reading this file. Read the full file content for detailed syntax, critical formatting rules, and best practices before proceeding with document creation.When editing an existing Word document, use the Document library (a Python library for OOXML manipulation). The library automatically handles infrastructure setup and provides methods for document manipulation. For complex scenarios, you can access the underlying DOM directly through the library.
ooxml.md (~600 lines) completely from start to finish. NEVER set any range limits when reading this file. Read the full file content for the Document library API and XML patterns for directly editing document files.python ooxml/scripts/unpack.py <office_file> <output_directory>python ooxml/scripts/pack.py <input_directory> <office_file>The Document library provides both high-level methods for common operations and direct DOM access for complex scenarios.
This workflow allows you to plan comprehensive tracked changes using markdown before implementing them in OOXML. CRITICAL: For complete tracked changes, you must implement ALL changes systematically.
Batching Strategy: Group related changes into batches of 3-10 changes. This makes debugging manageable while maintaining efficiency. Test each batch before moving to the next.
Principle: Minimal, Precise Edits
When implementing tracked changes, only mark text that actually changes. Repeating unchanged text makes edits harder to review and appears unprofessional. Break replacements into: [unchanged text] + [deletion] + [insertion] + [unchanged text]. Preserve the original run's RSID for unchanged text by extracting the <w:r> element from the original and reusing it.
Example - Changing "30 days" to "60 days" in a sentence:
# BAD - Replaces entire sentence
'<w:del><w:r><w:delText>The term is 30 days.</w:delText></w:r></w:del><w:ins><w:r><w:t>The term is 60 days.</w:t></w:r></w:ins>'
# GOOD - Only marks what changed, preserves original <w:r> for unchanged text
'<w:r w:rsidR="00AB12CD"><w:t>The term is </w:t></w:r><w:del><w:r><w:delText>30</w:delText></w:r></w:del><w:ins><w:r><w:t>60</w:t></w:r></w:ins><w:r w:rsidR="00AB12CD"><w:t> days.</w:t></w:r>'
Get markdown representation: Convert document to markdown with tracked changes preserved:
pandoc --track-changes=all path-to-file.docx -o current.md
Identify and group changes: Review the document and identify ALL changes needed, organizing them into logical batches:
Location methods (for finding changes in XML):
Batch organization (group 3-10 related changes per batch):
Read documentation and unpack:
ooxml.md (~600 lines) completely from start to finish. NEVER set any range limits when reading this file. Pay special attention to the "Document Library" and "Tracked Change Patterns" sections.python ooxml/scripts/unpack.py <file.docx> <dir>Implement changes in batches: Group changes logically (by section, by type, or by proximity) and implement them together in a single script. This approach:
Suggested batch groupings:
For each batch of related changes:
a. Map text to XML: Grep for text in word/document.xml to verify how text is split across <w:r> elements.
b. Create and run script: Use get_node to find nodes, implement changes, then doc.save(). See "Document Library" section in ooxml.md for patterns.
Note: Always grep word/document.xml immediately before writing a script to get current line numbers and verify text content. Line numbers change after each script run.
Pack the document: After all batches are complete, convert the unpacked directory back to .docx:
python ooxml/scripts/pack.py unpacked reviewed-document.docx
Final verification: Do a comprehensive check of the complete document:
pandoc --track-changes=all reviewed-document.docx -o verification.md
grep "original phrase" verification.md # Should NOT find it
grep "replacement phrase" verification.md # Should find it
To visually analyze Word documents, convert them to images using a two-step process:
Convert DOCX to PDF:
soffice --headless --convert-to pdf document.docx
Convert PDF pages to JPEG images:
pdftoppm -jpeg -r 150 document.pdf page
This creates files like page-1.jpg, page-2.jpg, etc.
Options:
-r 150: Sets resolution to 150 DPI (adjust for quality/size balance)-jpeg: Output JPEG format (use -png for PNG if preferred)-f N: First page to convert (e.g., -f 2 starts from page 2)-l N: Last page to convert (e.g., -l 5 stops at page 5)page: Prefix for output filesExample for specific range:
pdftoppm -jpeg -r 150 -f 2 -l 5 document.pdf page # Converts only pages 2-5
IMPORTANT: When generating code for DOCX operations:
Required dependencies (install if not available):
sudo apt-get install pandoc (for text extraction)npm install -g docx (for creating new documents)sudo apt-get install libreoffice (for PDF conversion)sudo apt-get install poppler-utils (for pdftoppm to convert PDF to images)pip install defusedxml (for secure XML parsing)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。
概要と使いどころ
Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
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。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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。
Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
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。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
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。
Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
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。
Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.
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。