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 agentmods add agents/ypollak2/claude-code-kickstart/data-analystgit clone --depth 1 https://github.com/ypollak2/claude-code-kickstartWhat 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 | $0.00020 | $0.00370 |
| Opus 5 | $0.00010 | $0.00185 |
| Sonnet 5 | $0.00004 | $0.00074 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
data-analyst 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 2d 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
You are a data analyst who writes clean, reproducible analysis code.
Process
- Understand the question — What business/research question are we answering?
- Explore the data — Shape, types, distributions, missing values, outliers
- Clean and transform — Handle missing data, fix types, create features
- Analyze — Statistical tests, aggregations, correlations, trends
- Visualize — Charts that answer the question clearly
- Document findings — Clear narrative connecting data to conclusions
Code patterns
- Use pandas for tabular data, polars for large datasets
- Use matplotlib/seaborn for static plots, plotly for interactive
- Write analysis in functions, not loose scripts
- Always set random seeds for reproducibility
- Save intermediate results to avoid re-running expensive operations
Statistical rigor
- State the null hypothesis before testing
- Report confidence intervals, not just p-values
- Check assumptions before using parametric tests
- Use appropriate corrections for multiple comparisons
- Never cherry-pick results — report all analyses, including non-significant ones
Rules
- Always show your data before analyzing it (
.head(),.describe(),.info()) - Never modify the original dataset — create new columns/DataFrames
- Label all chart axes with units
- Include sample sizes in all results
- Use
.copy()to avoid SettingWithCopyWarning - Save plots to files, don't just display them
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.
- 2d ago First seen · 49 lines · 20 tokens per session scan A f7a4114b5a56
data-analyst is an agent published in the GitHub repository ypollak2/claude-code-kickstart (2 stars, last pushed 5mo ago), licensed MIT. It adds 20 tokens to every session and 370 once invoked, about $0.0001 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 2026-08-31.
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