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.
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.
npx skills add OpenSenseNova/SenseNova-Skills --skill word-analysisgit clone --depth 1 https://github.com/OpenSenseNova/SenseNova-SkillsWrote 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.
[](https://agentmods.dev/skills/opensensenova/sensenova-skills/word-analysis)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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( 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")
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.
- 11d ago First seen · 270 lines · 52 tokens per session scan A a7d512e21699
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.
Other skills, from other repositories
feishu
A toolkit for working with Feishu, also called Lark, a workplace collaboration platform. It covers documents, spreadsheets, files, wikis, approvals, calendars, and contacts.
ha-data-analytics
A local-first data-analysis and reporting skill for CSV and spreadsheet files. It produces decision-ready analyses and shareable offline reports while separating facts, calculations, interpretations, and recommendations.
office-docx
Use when the user asks to create, edit, inspect, polish, verify, or deliver Word .docx documents, Google Docs-targeted drafts, business briefs, forms, reports, tables, checklists, redraft-ready document sections, or PDF/Word source-to-DOCX transformations.
office-pptx
Use when the user asks to create, inspect, verify, polish, or deliver PowerPoint .pptx decks, Google Slides-targeted deck artifacts, strategy narratives, operating reviews, pitch decks, teaching decks, section slides, bullet slides, or source-to-PPTX transformations.
office-xlsx
Use when the user asks to create, inspect, verify, analyze, format, or deliver Excel .xlsx workbooks, Google Sheets-targeted spreadsheet artifacts, trackers, budgets, models, tables, dashboards, formulas, CSV/TSV-to-XLSX conversions, or spreadsheet-ready data packs.
youdaonote
A command-line skill for managing Youdao Cloud Notes, a Chinese note-taking service. It supports notes, to-do items, saved web pages, searches, and folders.