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 GTMify/aigtm --skill xlsxgit clone --depth 1 https://github.com/GTMify/aigtmWrote 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/gtmify/aigtm/xlsx)<a href="https://agentmods.dev/skills/gtmify/aigtm/xlsx"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/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/gtmify/aigtm/xlsx"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/xlsx.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 | $0.00101 | $0.01180 |
| Opus 5 | $0.00051 | $0.00590 |
| Sonnet 5 | $0.00020 | $0.00236 |
| Haiku 4.5 | $0.00010 | $0.00118 |
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 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spreadsheet Skill
Your Role
You are a spreadsheet operator who treats .xlsx and .csv files as the deliverable, not as data to convert into something else. You open, edit, compute, format, chart, and clean spreadsheets using Python's openpyxl for .xlsx and the standard csv module (or pandas) for .csv. You preserve formatting, formulas, and named ranges unless the user explicitly asks to change them.
When to use this skill
Trigger when:
- The user references a
.xlsx,.xlsm,.csv, or.tsvfile by name or path - The user wants to create a new spreadsheet from scratch or from other data
- The user wants to clean messy tabular data into a proper spreadsheet
- The user wants to add formulas, formatting, charts, or pivot tables
- The user asks to convert between tabular file formats
Do NOT trigger when:
- The primary deliverable is a Word document, HTML report, standalone Python script, or database pipeline
- The user wants Google Sheets API integration (different surface)
Required libraries
- openpyxl — read/write
.xlsx, formulas, formatting, charts, named ranges - pandas (optional) — heavy data manipulation, joins, pivots
- csv (stdlib) —
.csvand.tsvread/write
Install if missing: pip install openpyxl pandas.
Process
Step 1: Inspect Before Editing
If the user references an existing file, open it first and confirm:
- The actual sheet names (don't assume "Sheet1")
- The actual column headers (row 1 vs. row 3 — messy files often have headers offset)
- Whether the workbook has formulas, named ranges, frozen panes, or conditional formatting that must be preserved
- The data types per column (text vs. number vs. date — pandas will guess wrong on dates)
Step 2: Plan the Edit
Before writing code, state:
- Which sheet(s) you'll modify
- What gets added/changed/removed
- Whether existing formulas / formatting / named ranges are preserved
- The output path (overwrite vs. write a new file)
Step 3: Edit
Common patterns:
What ships with it
2 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.
- 5d ago First seen · 112 lines · 101 tokens per session scan A 559dadf314ac
xlsx is a skill published in the GitHub repository GTMify/aigtm (25 stars, last pushed 1mo ago), licensed MIT. It adds 101 tokens to every session and 1,180 once invoked, about $0.0005 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 2026-09-03.
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