xlsx

xlsx is a skill for Claude Code, Codex from sinaptik-ai/starpod. It costs 74 tokens per session (1,894 once invoked), scanned A, original, MIT.

A toolkit for working with spreadsheet files such as Excel workbooks, CSV files, and tab-separated files, including data analysis, formulas, formatting, and charts.

In plain words
What is it for?
Opening spreadsheets, summarizing data, creating Excel formulas, preserving formatting, building charts, and converting between tabular file formats.
Why use it?
It provides a consistent way to read, clean, update, and analyze tabular data while keeping spreadsheet calculations updateable.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Opening spreadsheets, summarizing data, creating Excel formulas, preserving formatting, building charts, and converting between tabular file formats.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sinaptik-ai/starpod/xlsx
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 sinaptik-ai/starpod --skill xlsx
Clone the repo
git clone --depth 1 https://github.com/sinaptik-ai/starpod

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.

agentmods badge for xlsx

README.md
[![agentmods](https://agentmods.dev/badge/skills/sinaptik-ai/starpod/xlsx/github.svg)](https://agentmods.dev/skills/sinaptik-ai/starpod/xlsx)
Your own site
<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.

agentmods 80×15 button for xlsx

Your own site · 80×15
<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>
Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,894 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.
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.

ModelPer sessionOnce 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

Measured 12d ago against content hash ded6ab838531, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 5 executable files (scripts/office/__init__.py, scripts/office/pack.py, scripts/office/soffice.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

crates/starpod/skills/xlsx/SKILL.md · 212 lines

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")

Read the full file on GitHub · 212 lines

Files

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.

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.

  1. 12d ago First seen · 212 lines · 74 tokens per session scan A ded6ab838531

Subscribe to this mod's changes

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.