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 smith-network-solutions/threadknot --skill xlsxgit clone --depth 1 https://github.com/smith-network-solutions/threadknotWrote 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/smith-network-solutions/threadknot/xlsx)<a href="https://agentmods.dev/skills/smith-network-solutions/threadknot/xlsx"><img src="https://agentmods.dev/badge/skills/smith-network-solutions/threadknot/xlsx/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/smith-network-solutions/threadknot/xlsx"><img src="https://agentmods.dev/badge/skills/smith-network-solutions/threadknot/xlsx.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00084 | $0.01919 |
| Opus 5.5 | $0.00034 | $0.00768 |
| Sonnet 5.5 | $0.00017 | $0.00384 |
| Haiku 4.5 | $0.00008 | $0.00192 |
Grade A, and why
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 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.
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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Excel workbooks (.xlsx)
openpyxl for anything that reads or edits. XlsxWriter only when writing a very large file from scratch, where it is faster and lighter but cannot read.
The single fact that causes the most wasted time:
openpyxl does not calculate. Writing
=SUM(B2:B10)stores the formula string. The cached value stays empty until a real spreadsheet application opens and recalculates the file.load_workbook(data_only=True)returnsNonefor every formula cell in a file that Excel has never opened.
So: if the user needs a workbook whose numbers are readable by other tools, run
scripts/recalc.py after writing it. If you need to read values from a file
someone sent you, data_only=True works — Excel already cached them.
Before editing a workbook, look at it
scripts/inspect_xlsx.py budget.xlsx
Prints each sheet with its dimensions, frozen panes, column widths, a preview grid, where the formulas are, merged ranges, named ranges, conditional formatting and charts. Do this first on any file you did not create — a spreadsheet's structure is rarely what the file name suggests.
scripts/inspect_xlsx.py budget.xlsx --sheet Q1 --range A1:F20 # focus
scripts/inspect_xlsx.py budget.xlsx --formulas # every formula
Creating a workbook
from openpyxl import Workbook
from openpyxl.styles import Font, Alignment, PatternFill, Border, Side
from openpyxl.utils import get_column_letter
wb = Workbook()
ws = wb.active
ws.title = "Q1" # sheet names: <=31 chars, no : \ / ? * [ ]
ws.append(["Region", "Revenue", "Cost", "Margin"])
for cell in ws[1]:
cell.font = Font(bold=True, color="FFFFFF")
cell.fill = PatternFill("solid", fgColor="1F4E79")
cell.alignment = Alignment(horizontal="center")
for row, (region, revenue, cost) in enumerate(data, start=2):
ws.cell(row=row, column=1, value=region)
ws.cell(row=row, column=2, value=revenue).number_format = '#,##0.00'
ws.cell(row=row, column=3, value=cost).number_format = '#,##0.00'
ws.cell(row=row, column=4, value=f"=B{row}-C{row}").number_format = '#,##0.00'
ws.freeze_panes = "A2" # header stays visible
ws.auto_filter.ref = ws.dimensions
ws.column_dimensions["A"].width = 18 # openpyxl does NOT auto-size columns
wb.create_sheet("Notes")
wb.save("budget.xlsx")
What ships with it
6 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 · 181 lines · 84 tokens per session scan A 34cbf50f3df1
xlsx is a skill published in the GitHub repository smith-network-solutions/threadknot (8 stars, last pushed 13d ago), licensed Apache-2.0. It adds 84 tokens to every session and 1,919 once invoked, about $0.0003 per session on Opus 5.5. 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.
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