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 sn-da-non-spreadsheet-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/sn-da-non-spreadsheet-analysis)<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-da-non-spreadsheet-analysis"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-da-non-spreadsheet-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/sn-da-non-spreadsheet-analysis"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-da-non-spreadsheet-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 | $0.00234 | $0.01416 |
| Opus 5.5 | $0.00094 | $0.00566 |
| Sonnet 5.5 | $0.00047 | $0.00283 |
| Haiku 4.5 | $0.00023 | $0.00142 |
Grade A, and why
sn-da-non-spreadsheet-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 1mo 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.
result = subprocess.run(cmd, capture_output=True, text=True, timeout=60) How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document Analysis Skill — Word / PDF / PPT
End-to-end workflow for Word, PDF, and PPT document parsing. Each format has specific parsing pitfalls — follow the format-specific sub-skill exactly.
Workflow
Step 0 — Identify file type and input scope
import os
input_path = "/mnt/data/..." # from user
# Detect single file vs directory (multi-file scenario)
if os.path.isdir(input_path):
all_files = [
os.path.join(input_path, f)
for f in os.listdir(input_path)
if f.lower().endswith(('.docx', '.doc', '.pdf', '.pptx', '.ppt'))
]
print(f"Found {len(all_files)} documents: {all_files}")
else:
all_files = [input_path]
# Route by extension
ext = os.path.splitext(all_files[0])[-1].lower()
print(f"File type: {ext}")
Critical rule: When
input_pathis a directory OR the user says "这些文件" / "所有文档", process every file and aggregate. Never stop at the first file.
Step 1 — Load sub-skill by format
| Extension | Sub-skill to load |
|---|---|
.docx / .doc |
capability/word-analysis/SKILL.md |
.pdf |
capability/pdf-analysis/SKILL.md |
.pptx / .ppt |
capability/ppt-analysis/SKILL.md |
read_file(path="<skills_root>/sn-da-non-spreadsheet-analysis/capability/<format>-analysis/SKILL.md")
Load only the sub-skill you need — do not load all three at once.
Step 2 — Parse and extract
Follow the sub-skill's extraction pattern. For all formats:
- Full scan: iterate all pages/slides/paragraphs — never stop early
- Table extraction: get every table, not just the first one
- Image/chart detection: if a page/slide yields no text, treat it as image-based and call
caption.py
Step 3 — Answer with verification
After extracting data, verify before answering:
# For count/statistics questions: spot-check 3-5 items
sample = result_list[:3]
print(f"Sample check: {sample}")
print(f"Total count: {len(result_list)}")
# For numeric calculations: print intermediate values
print(f"Max={max_val}, Min={min_val}, Range={max_val - min_val}")
# For unit-sensitive answers: always include the unit
print(f"Answer: {value} {unit}") # e.g., "475 千港元" not just "475"
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 144 lines · 234 tokens per session scan A d472d8bd3861
sn-da-non-spreadsheet-analysis is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,917 stars, last pushed 17d ago), licensed MIT. It adds 234 tokens to every session and 1,416 once invoked, about $0.0009 per session on Opus 5.5. 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
Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.
agent-office
A guide for creating, editing, rewriting, converting, processing, or delivering Word documents, spreadsheets, presentations, and PDF files.
market-research-reports
Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with scientific-schematics and generate-image, deep integration with research-lookup for data gathering, and multi-framework…
A set of instructions for working with PDF files, which are documents designed to preserve their layout across devices.
hive.pdf
Read, write, merge, split, rotate, watermark, encrypt, and OCR PDF files using Python (pypdf, pdfplumber, reportlab, pypdfium2) and command-line tools (poppler-utils, qpdf). Use when the user asks to extract text/tables/images from a PDF, create or modify a PDF, combine or split PDFs, OCR a scanned PDF…
pdf-toolkit
Structured .pdf operations: extract text/tables, merge pages from multiple PDFs, split a PDF by page ranges, fill PDF form fields, and generate fresh PDFs from JSON. Trigger when the user wants programmatic PDF work without natural-language rewriting — examples: pull tables from a report, combine three PDFs, extract…