What is sn-da-excel-workflow?

A step-by-step workflow for analysing Excel workbooks with multiple sheets. It covers reading data, cleaning it, filtering and combining results, and exporting reports, with a separate strategy for large files.

Counting rows across sheets, choosing direct reading or Parquet caching, cleaning values, filtering data, calculating cross-sheet summaries, and exporting Excel or CSV results.

It provides a repeatable path from a spreadsheet to a cleaned summary without loading unnecessarily large workbooks into memory.

sn-da-excel-workflow — Skill for Claude Code and Codex by OpenSenseNov

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 sn-da-excel-workflow

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-da-excel-workflow/github.svg)](https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-da-excel-workflow)

Your own site

<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-da-excel-workflow"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-da-excel-workflow/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/sn-da-excel-workflow"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/sn-da-excel-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 348 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,476 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. 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.

Model	Per session	Once invoked
Fable 5.1	$0.00348	$0.03476
Opus 5	$0.00174	$0.01738
Sonnet 5	$0.00070	$0.00695
Haiku 4.5	$0.00035	$0.00348

Measured 12d ago against content hash 47348dffc7c9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

sn-da-excel-workflow scanned grade A with 0 findings 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 12d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/sn-da-excel-workflow/SKILL.md · 289 lines

How it starts

The opening of the file, as written. The whole thing — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Excel Data Analysis Workflow

End-to-end workflow for structured Excel analysis. Each step maps to a capability sub-skill that can be loaded for detailed patterns.

Workflow

Step 1 — Count rows across all sheets (lightweight, no full load)

Count rows per sheet without loading data into memory. Use openpyxl read_only mode — this works for any file size.

import openpyxl, gc

wb = openpyxl.load_workbook(file_path, read_only=True, data_only=True)
total_rows = 0
sheet_info = {}
for name in wb.sheetnames:
    ws = wb[name]
    row_count = sum(1 for _ in ws.iter_rows(min_row=2, values_only=True))
    total_rows += row_count
    sheet_info[name] = row_count
    print(f"Sheet '{name}': {row_count} rows")
wb.close()
print(f"总行数={total_rows}")

⚠️ Do NOT use pd.read_excel() to count rows — it loads all data into memory, which will OOM on large files.

→ capability: excel-reading/multi-sheet-reading

Step 2 — Large file gate (CRITICAL — choose strategy by row count)

total_rows	Strategy	What to do
< 10k	Direct read	`df = pd.read_excel(file_path, sheet_name=target_sheet)`
10k – 100k	Parquet cache	`pd.read_excel()` once → `df.to_parquet()` → all later reads from Parquet
>= 100k	STOP. Load `sn-da-large-file-analysis` skill	Read its SKILL.md, then follow its streaming read + Parquet pattern. Do NOT use `pd.read_excel()` at all — it will OOM or timeout on 100k+ rows.

For >= 100k rows:

read_file(path="<skills_base>/sn-da-large-file-analysis/SKILL.md")

Then use stream_excel_to_parquet() from that skill — it reads via openpyxl iter_rows in 50k-row chunks with constant memory.

For 10k – 100k rows (only):

import pandas as pd
parquet_path = "/tmp/_auto_parquet.parquet"
df = pd.read_excel(file_path, sheet_name=target_sheet)
df.to_parquet(parquet_path, engine="pyarrow")
del df; gc.collect()
df = pd.read_parquet(parquet_path)

→ capability: excel-reading/large-excel-reading

Read the full file on GitHub · 289 lines

Files

What ships with it

44 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.

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.

12d ago First seen · 289 lines · 348 tokens per session scan A 47348dffc7c9

Subscribe to this mod's changes

sn-da-excel-workflow is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,515 stars, last pushed yesterday), licensed MIT. It adds 348 tokens to every session and 3,476 once invoked, about $0.0017 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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