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 sinaptik-ai/starpod --skill xlsxgit clone --depth 1 https://github.com/sinaptik-ai/starpodWrote 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/sinaptik-ai/starpod/xlsx)<a href="https://agentmods.dev/skills/sinaptik-ai/starpod/xlsx"><img src="https://agentmods.dev/badge/skills/sinaptik-ai/starpod/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/sinaptik-ai/starpod/xlsx"><img src="https://agentmods.dev/badge/skills/sinaptik-ai/starpod/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 | $0.00074 | $0.01894 |
| Opus 5 | $0.00037 | $0.00947 |
| Sonnet 5 | $0.00015 | $0.00379 |
| Haiku 4.5 | $0.00007 | $0.00189 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
XLSX Skill
Decision Tree
| Task | Tool |
|---|---|
| Data analysis, bulk operations | pandas |
| Formulas, formatting, styles | openpyxl |
| High-performance chart creation | xlsxwriter |
| Read calculated values (no formulas) | openpyxl with data_only=True |
CRITICAL: Use Excel Formulas, Not Hardcoded Values
# ❌ WRONG — calculating in Python
total = df['Sales'].sum()
sheet['B10'] = total
# ✅ CORRECT — let Excel calculate
sheet['B10'] = '=SUM(B2:B9)'
sheet['C5'] = '=(C4-C2)/C2'
sheet['D20'] = '=AVERAGE(D2:D19)'
Always use Excel formulas so spreadsheets remain dynamic and updateable.
Reading & Analyzing
pandas (data analysis)
import pandas as pd
df = pd.read_excel('file.xlsx') # first sheet
all_sheets = pd.read_excel('file.xlsx', sheet_name=None) # all sheets as dict
df.describe() # statistics
df.groupby('Category')['Amount'].sum() # aggregation
df.to_excel('output.xlsx', index=False)
openpyxl (preserve formulas/formatting)
from openpyxl import load_workbook
wb = load_workbook('file.xlsx')
sheet = wb.active
for row in sheet.iter_rows(min_row=2, values_only=True):
print(row)
Creating New Spreadsheets
openpyxl with formulas and formatting
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, numbers
wb = Workbook()
ws = wb.active
ws.title = "Revenue Model"
# Headers
headers = ["Quarter", "Revenue", "COGS", "Gross Profit", "Margin"]
for col, h in enumerate(headers, 1):
cell = ws.cell(row=1, column=col, value=h)
cell.font = Font(bold=True, color="FFFFFF")
cell.fill = PatternFill("solid", fgColor="333333")
cell.alignment = Alignment(horizontal="center")
# Data with formulas
quarters = ["Q1", "Q2", "Q3", "Q4"]
revenues = [120000, 145000, 168000, 192000]
cogs_pct = 0.35
for i, (q, rev) in enumerate(zip(quarters, revenues), 2):
ws.cell(row=i, column=1, value=q)
ws.cell(row=i, column=2, value=rev).number_format = '$#,##0'
ws.cell(row=i, column=3).value = f'=B{i}*{cogs_pct}'
ws.cell(row=i, column=3).number_format = '$#,##0'
ws.cell(row=i, column=4).value = f'=B{i}-C{i}'
ws.cell(row=i, column=4).number_format = '$#,##0'
ws.cell(row=i, column=5).value = f'=D{i}/B{i}'
ws.cell(row=i, column=5).number_format = '0.0%'
# Totals row
total_row = len(quarters) + 2
ws.cell(row=total_row, column=1, value="Total").font = Font(bold=True)
for col in [2, 3, 4]:
cell = ws.cell(row=total_row, column=col)
cell.value = f'=SUM({chr(64+col)}2:{chr(64+col)}{total_row-1})'
cell.font = Font(bold=True)
cell.number_format = '$#,##0'
# Column widths
for col_letter, width in [("A", 12), ("B", 15), ("C", 15), ("D", 15), ("E", 12)]:
ws.column_dimensions[col_letter].width = width
wb.save("revenue_model.xlsx")
What ships with it
5 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.
- 12d ago First seen · 212 lines · 74 tokens per session scan A ded6ab838531
xlsx is a skill published in the GitHub repository sinaptik-ai/starpod (78 stars, last pushed 5mo ago), licensed MIT. It adds 74 tokens to every session and 1,894 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 2026-08-30.
Other skills, from other repositories
google-sheets
Google Sheets via gws: read/write cells, append rows, structured batch edits.
xlsx
Create, inspect, edit, recalculate, and validate Excel workbooks with local open-source tools while preserving source files and distinguishing formulas from calculated results.
data-analysis
Analyze Excel/CSV files with DuckDB SQL via bash.
document-processor
Guidance for processing documents, extracting content, and transforming structured information. Use when the user asks to process, parse, extract, or transform document content such as PDFs, Word files, or spreadsheets.
attendance-signin-sheet
A spreadsheet generator for printable attendance or meeting sign-in sheets. It turns a list of names into rows with columns such as signatures, dates, departments, or employee IDs.
xlsx
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When GLM needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing…