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 yujiachen-y/agent-bundle --skill data-analysisgit clone --depth 1 https://github.com/yujiachen-y/agent-bundleWrote 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/yujiachen-y/agent-bundle/data-analysis)<a href="https://agentmods.dev/skills/yujiachen-y/agent-bundle/data-analysis"><img src="https://agentmods.dev/badge/skills/yujiachen-y/agent-bundle/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/yujiachen-y/agent-bundle/data-analysis"><img src="https://agentmods.dev/badge/skills/yujiachen-y/agent-bundle/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 | $0.00015 | $0.00258 |
| Opus 5.5 | $0.00006 | $0.00103 |
| Sonnet 5.5 | $0.00003 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
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.
What it actually says
Data Analysis
You are given a data analysis task by the user. Follow these steps:
-
Write a Python script to
/workspace/analysis.pythat:- Creates or reads the data as described by the user
- Performs the requested analysis using pandas/numpy
- Prints summary statistics to stdout
- Saves any charts to
/workspace/chart.pngusing matplotlib (usesavefig, notshow)
-
Run the script in the sandbox using Bash:
cd /workspace && python3 analysis.py -
Read the Bash output to get the printed results.
-
Return a clear summary of the findings, referencing any generated files.
Rules:
- Always use
/workspace/as the working directory. - Use
matplotlib.use('Agg')before importing pyplot (no display server). - Save figures with
plt.savefig('/workspace/chart.png', dpi=100, bbox_inches='tight'). - If the user provides CSV data inline, write it to
/workspace/data.csvfirst. - If the Bash command fails, return the error output and suggest fixes.
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 · 32 lines · 15 tokens per session scan A d8cbd0f144b9
data-analysis is a skill published in the GitHub repository yujiachen-y/agent-bundle (10 stars, last pushed 4mo ago), licensed MIT. It adds 15 tokens to every session and 258 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
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