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 eric861129/SKILLS_All-in-one --skill csv-data-summarizergit clone --depth 1 https://github.com/eric861129/SKILLS_All-in-oneWrote 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/eric861129/skills_all-in-one/csv-data-summarizer)<a href="https://agentmods.dev/skills/eric861129/skills_all-in-one/csv-data-summarizer"><img src="https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/csv-data-summarizer/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/eric861129/skills_all-in-one/csv-data-summarizer"><img src="https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/csv-data-summarizer.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 | $0.00025 | $0.01262 |
| Opus 5.5 | $0.00010 | $0.00505 |
| Sonnet 5.5 | $0.00005 | $0.00252 |
| Haiku 4.5 | $0.00003 | $0.00126 |
Grade A, and why
csv-data-summarizer 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.
This is a copy
100% identical to csv-data-summarizer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CSV Data Summarizer
This Skill analyzes CSV files and provides comprehensive summaries with statistical insights and visualizations.
When to Use This Skill
Claude should use this Skill whenever the user:
- Uploads or references a CSV file
- Asks to summarize, analyze, or visualize tabular data
- Requests insights from CSV data
- Wants to understand data structure and quality
How It Works
⚠️ CRITICAL BEHAVIOR REQUIREMENT ⚠️
DO NOT ASK THE USER WHAT THEY WANT TO DO WITH THE DATA. DO NOT OFFER OPTIONS OR CHOICES. DO NOT SAY "What would you like me to help you with?" DO NOT LIST POSSIBLE ANALYSES.
IMMEDIATELY AND AUTOMATICALLY:
- Run the comprehensive analysis
- Generate ALL relevant visualizations
- Present complete results
- NO questions, NO options, NO waiting for user input
THE USER WANTS A FULL ANALYSIS RIGHT AWAY - JUST DO IT.
Automatic Analysis Steps:
The skill intelligently adapts to different data types and industries by inspecting the data first, then determining what analyses are most relevant.
-
Load and inspect the CSV file into pandas DataFrame
-
Identify data structure - column types, date columns, numeric columns, categories
-
Determine relevant analyses based on what's actually in the data:
- Sales/E-commerce data (order dates, revenue, products): Time-series trends, revenue analysis, product performance
- Customer data (demographics, segments, regions): Distribution analysis, segmentation, geographic patterns
- Financial data (transactions, amounts, dates): Trend analysis, statistical summaries, correlations
- Operational data (timestamps, metrics, status): Time-series, performance metrics, distributions
- Survey data (categorical responses, ratings): Frequency analysis, cross-tabulations, distributions
- Generic tabular data: Adapts based on column types found
-
Only create visualizations that make sense for the specific dataset:
- Time-series plots ONLY if date/timestamp columns exist
- Correlation heatmaps ONLY if multiple numeric columns exist
- Category distributions ONLY if categorical columns exist
- Histograms for numeric distributions when relevant
What ships with it
8 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 · 150 lines · 25 tokens per session scan A 1599338ac2db
csv-data-summarizer is a skill published in the GitHub repository eric861129/SKILLS_All-in-one (52 stars, last pushed 6mo ago), licensed MIT. It adds 25 tokens to every session and 1,262 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to csv-data-summarizer, differing in 0 lines, and is treated as a copy.
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