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 ArtificialAnalysis/Stirrup --skill data_analysisgit clone --depth 1 https://github.com/ArtificialAnalysis/StirrupWrote 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/artificialanalysis/stirrup/data_analysis)<a href="https://agentmods.dev/skills/artificialanalysis/stirrup/data_analysis"><img src="https://agentmods.dev/badge/skills/artificialanalysis/stirrup/data_analysis/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/artificialanalysis/stirrup/data_analysis"><img src="https://agentmods.dev/badge/skills/artificialanalysis/stirrup/data_analysis.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 | $0.00044 | $0.01076 |
| Opus 5.5 | $0.00018 | $0.00430 |
| Sonnet 5.5 | $0.00009 | $0.00215 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
data_analysis 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 data_analysis — 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Analysis Skill
Comprehensive data analysis toolkit using Polars - a blazingly fast DataFrame library. This skill provides instructions, reference documentation, and ready-to-use scripts for common data analysis tasks.
Iteration Checkpoints
| Step | What to Present | User Input Type |
|---|---|---|
| Data Loading | Shape, columns, sample rows | "Is this the right data?" |
| Data Exploration | Summary stats, data quality issues | "Any columns to focus on?" |
| Transformation | Before/after comparison | "Does this transformation look correct?" |
| Analysis | Key findings, charts | "Should I dig deeper into anything?" |
| Export | Output preview | "Ready to save, or any changes?" |
Quick Start
import polars as pl
from polars import col
# Load data
df = pl.read_csv("data.csv")
# Explore
print(df.shape, df.schema)
df.describe()
# Transform and analyze
result = (
df.filter(col("value") > 0)
.group_by("category")
.agg(col("value").sum().alias("total"))
.sort("total", descending=True)
)
# Export
result.write_csv("output.csv")
When to Use This Skill
- Loading datasets (CSV, JSON, Parquet, Excel, databases)
- Data cleaning, filtering, and transformation
- Aggregations, grouping, and pivot tables
- Statistical analysis and summary statistics
- Time series analysis and resampling
- Joining and merging multiple datasets
- Creating visualizations and charts
- Exporting results to various formats
Skill Contents
Reference Documentation
Detailed API reference and patterns for specific operations:
reference/loading.md- Loading data from all supported formatsreference/transformations.md- Column operations, filtering, sorting, type castingreference/aggregations.md- Group by, window functions, running totalsreference/time_series.md- Date parsing, resampling, lag featuresreference/statistics.md- Correlations, distributions, hypothesis testing setupreference/visualization.md- Creating charts with matplotlib/plotly
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 · 158 lines · 44 tokens per session scan A 365334b22d4d
data_analysis is a skill published in the GitHub repository ArtificialAnalysis/Stirrup (661 stars, last pushed 6d ago), licensed MIT. It adds 44 tokens to every session and 1,076 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to data_analysis, differing in 0 lines, and is treated as a copy.
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