Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
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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
Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
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。
Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.
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。
Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.
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。
Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
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。
Audit whether a website can be found, crawled, and cited by AI answer engines such as ChatGPT Search, Perplexity, Google AI Overviews, and Microsoft Copilot. Use when someone asks why their brand is missing from AI answers, whether AI crawlers can read their site, how to get cited by ChatGPT or Perplexity, or asks for a GEO or AEO (generative / answer engine optimization) review. Produces a citation baseline across buyer-intent prompts, a crawler-access check, a citability review of named pages, and a ranked fix list. Not for keyword rank tracking, paid search, or pages behind a login.
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。
Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.
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。