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 agentmods add skills/duyet/oma/spreadsheet-xlsxnpx skills add duyet/oma --skill spreadsheet-xlsxgit clone --depth 1 https://github.com/duyet/omaWrote 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/duyet/oma/spreadsheet-xlsx)<a href="https://agentmods.dev/skills/duyet/oma/spreadsheet-xlsx"><img src="https://agentmods.dev/badge/skills/duyet/oma/spreadsheet-xlsx.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00074 | $0.00789 |
| Opus 5 | $0.00037 | $0.00394 |
| Sonnet 5 | $0.00015 | $0.00158 |
| Haiku 4.5 | $0.00007 | $0.00079 |
Grade A, and why
spreadsheet-xlsx 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
spreadsheet-xlsx
Produce a genuine .xlsx (not a CSV renamed) with proper types, number
formats, a frozen/bold header row, and auto-sized columns. Use openpyxl —
it's pure Python, has no system dependencies, and installs in one step.
Reusable helpers are in snippets.py — copy them in rather than
rewriting boilerplate each time.
Setup
uv pip install openpyxl # pure-python, no apt packages needed
If the data is already in a pandas DataFrame, df.to_excel(path, index=False, engine="openpyxl") is the fast path — but you still want the formatting pass
below, so openpyxl directly is usually clearer.
Rules for a workbook that doesn't look amateur
- Write typed cells, not strings. Put real numbers, dates, and booleans in
cells so Excel can sum/sort them. Never write
"1,234"or"$1,234"as text — write1234and set a number format. - Number formats, not pre-formatted strings. Currency
'"$"#,##0.00', thousands'#,##0', percent'0.0%'(store0.25, not25), dates'yyyy-mm-dd'. - Header row: bold, frozen (
ws.freeze_panes = "A2"), and turn on an autofilter over the data range. - Column widths: size to the longest value in each column (see snippet) — default widths clip everything.
- One sheet per logical table. Name sheets meaningfully (
ws.title), <= 31 chars, no[]/\*?:. Add a small summary sheet first when there are many. - Totals go in a labeled row using real
=SUM(B2:B100)formulas, not a hardcoded number, so they stay correct if the user edits cells.
Minimal example
from openpyxl import Workbook
from openpyxl.styles import Font
# from snippets.py:
from snippets import write_table, autosize, apply_number_format
wb = Workbook()
ws = wb.active
ws.title = "Revenue"
rows = [
{"Region": "EMEA", "Revenue": 128400.0, "Growth": 0.12},
{"Region": "APAC", "Revenue": 98200.0, "Growth": -0.03},
]
write_table(ws, rows) # bold+frozen header, autofilter
apply_number_format(ws, "Revenue", '"$"#,##0')
apply_number_format(ws, "Growth", "0.0%")
autosize(ws)
wb.save("/workspace/revenue.xlsx")
What ships with it
1 file 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.
- 5d ago First seen · 70 lines · 74 tokens per session scan A 355388fec893
spreadsheet-xlsx is a skill published in the GitHub repository duyet/oma (5 stars, last pushed yesterday), licensed Apache-2.0. It adds 74 tokens to every session and 789 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
dcf-model
Build discounted cash flow valuation workbooks in Excel.
audit-xls
Audit a spreadsheet for formula accuracy, errors, and common mistakes. Scopes to a selected range, a single sheet, or the entire model (including financial-model integrity checks like BS balance, cash tie-out, and logic sanity). Triggers on "audit this sheet", "check my formulas", "find formula errors", "QA this…
google-drive-sheets
Find, read, export, edit, and manage the user's Google Drive, Docs, Sheets, and Slides through per-user OAuth.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
excel-basic-statistics-and-routing
Skill "excel-basic-statistics-and-routing" from OpenSenseNova/SenseNova-Skills, covering skill steps, 保存区间提取与汇总结果 and 保存筛选与统计结果.
dynamic-percentage-and-large-file-analysis
根据文件行数动态切换大文件处理策略(Parquet转换),通过逐行扫描或列匹配提取关键指标并计算占比、均值等统计量,最终输出结构化Excel报告及可视化图表。.