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/StirrupJS --skill data_analysisgit clone --depth 1 https://github.com/ArtificialAnalysis/StirrupJSWrote 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/stirrupjs/data_analysis)<a href="https://agentmods.dev/skills/artificialanalysis/stirrupjs/data_analysis"><img src="https://agentmods.dev/badge/skills/artificialanalysis/stirrupjs/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/stirrupjs/data_analysis"><img src="https://agentmods.dev/badge/skills/artificialanalysis/stirrupjs/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/StirrupJS (23 stars, last pushed 3mo 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.
Other skills, from other repositories
hugging-face-model-trainer
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV…
thinking-out-loud
A contract for what the agent does when a long, messy, stream-of-consciousness ramble arrives (usually voice dictation): act on nothing until the echo brief is approved. The echo audits the entire transfer, mission, locked decisions and constraints, open questions, flips and parked tangents, with the model's…
hugging-face-datasets
Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
azure-ai-ml-py
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".