word-analysis

word-analysis is a skill for Claude Code, Codex from OpenSenseNova/SenseNova-Skills. It costs 52 tokens per session (2,165 once invoked), scanned A, original, MIT.

A Word document analysis method for reading .docx and older .doc files, including their paragraphs, tables, highlights, colors, and embedded images.

In plain words
What is it for?
It helps extract full document text, read table data, inspect highlighting and colors, compare multiple documents, and turn embedded images into captions.
Why use it?
It gathers content and formatting details that can be missed when only the main body text is extracted. Older .doc files are converted before analysis.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps extract full document text, read table data, inspect highlighting and colors, compare multiple documents, and turn embedded images into captions.

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Install with agentmods
npx agentmods add skills/opensensenova/sensenova-skills/word-analysis
About the project

SenseNova-Skills is a collection of modular skills that extend SenseNova models with office-assistant capabilities such as image generation, presentation creation, spreadsheet analysis, and research. The skills are designed for use in agent runtimes and can be combined into productivity workflows; the catalogue entries are individual skills and agents from this collection.

OpenSenseNova/SenseNova-Skills · 5,515 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add OpenSenseNova/SenseNova-Skills --skill word-analysis
Clone the repo
git clone --depth 1 https://github.com/OpenSenseNova/SenseNova-Skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for word-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/word-analysis/github.svg)](https://agentmods.dev/skills/opensensenova/sensenova-skills/word-analysis)
Your own site
<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/word-analysis"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/word-analysis/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for word-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/word-analysis"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/word-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,165 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00052 $0.02165
Opus 5 $0.00026 $0.01082
Sonnet 5 $0.00010 $0.00433
Haiku 4.5 $0.00005 $0.00216

Measured 11d ago against content hash a7d512e21699, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

word-analysis scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

subprocess.run(
skills/sn-da-non-spreadsheet-analysis/capability/word-analysis/SKILL.md · 270 lines

How it starts

The opening of the file, as written. The whole thing — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Word Analysis — .docx / .doc

Environment

from docx import Document
import os

# python-docx is available; for .doc (old format) convert via libreoffice first
def load_doc(path):
    """Load .docx directly; convert .doc to .docx first if needed."""
    if path.lower().endswith('.doc'):
        import subprocess
        out_dir = os.path.dirname(path)
        subprocess.run(
            ['libreoffice', '--headless', '--convert-to', 'docx', '--outdir', out_dir, path],
            check=True, capture_output=True
        )
        path = path.rsplit('.', 1)[0] + '.docx'
    return Document(path)

Core Method 1: Full Text Extraction

def extract_full_text(doc_path):
    """Extract all text: paragraphs + table cells, in document order."""
    doc = load_doc(doc_path)
    lines = []

    # Iterate paragraphs and tables in body order
    from docx.oxml.ns import qn
    for block in doc.element.body:
        tag = block.tag.split('}')[-1]
        if tag == 'p':
            # Paragraph
            from docx.text.paragraph import Paragraph
            para = Paragraph(block, doc)
            text = para.text.strip()
            if text:
                lines.append(text)
        elif tag == 'tbl':
            # Table
            from docx.table import Table
            tbl = Table(block, doc)
            for row in tbl.rows:
                row_text = '\t'.join(cell.text.strip() for cell in row.cells)
                if row_text.strip():
                    lines.append(row_text)

    return '\n'.join(lines)

# Usage
text = extract_full_text("/mnt/data/doc.docx")
print(text[:2000])  # preview first 2000 chars

Core Method 2: Table Extraction (Structured)

import pandas as pd

def extract_all_tables(doc_path):
    """Extract all tables from a Word document as list of DataFrames."""
    doc = load_doc(doc_path)
    tables = []

    for i, tbl in enumerate(doc.tables):
        rows = []
        for row in tbl.rows:
            rows.append([cell.text.strip() for cell in row.cells])
        if not rows:
            continue
        # Use first row as header if it looks like a header
        df = pd.DataFrame(rows[1:], columns=rows[0]) if rows else pd.DataFrame()
        tables.append((i, df))
        print(f"Table {i}: {df.shape[0]} rows × {df.shape[1]} cols")
        print(df.head(3))

    return tables

# Usage
tables = extract_all_tables("/mnt/data/doc.docx")

Read the full file on GitHub · 270 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 270 lines · 52 tokens per session scan A a7d512e21699

Subscribe to this mod's changes

word-analysis is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,515 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 2,165 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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