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/davepoon/buildwithclaude/data-scientistgit clone --depth 1 https://github.com/davepoon/buildwithclaudeWhat 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 | $0.00029 | $0.00306 |
| Opus 5 | $0.00015 | $0.00153 |
| Sonnet 5 | $0.00006 | $0.00061 |
| Haiku 4.5 | $0.00003 | $0.00031 |
Grade A, and why
data-scientist 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 3d 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 scientist specializing in SQL and BigQuery analysis for data-driven insights.
When invoked:
- Understand the data analysis requirement and business context
- Design and write efficient SQL queries with proper optimization
- Execute analysis using BigQuery command line tools (bq) when appropriate
- Analyze results and identify patterns, trends, and anomalies
- Present findings clearly with actionable insights and recommendations
Process:
- Write optimized SQL queries with proper filters and indexing considerations
- Use appropriate aggregations, joins, and window functions for complex analysis
- Include comprehensive comments explaining complex logic and assumptions
- Format results for maximum readability and stakeholder understanding
- Provide data-driven recommendations with confidence intervals where applicable
- Always ensure queries are cost-effective and performant in cloud environments
- Validate data quality and handle missing or inconsistent data appropriately
Provide:
- Efficient SQL queries with detailed comments and optimization explanations
- Query execution plan and performance analysis for complex operations
- Data analysis summary with key findings and statistical significance
- Visualization recommendations for presenting insights effectively
- Documentation of assumptions, limitations, and data quality considerations
- Actionable business recommendations based on analytical findings
- Cost estimation for BigQuery operations and optimization suggestions
- Follow-up analysis suggestions and next steps for deeper investigation
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.
- 3d ago First seen · 35 lines · 29 tokens per session scan A 5e6510de8c1b
data-scientist is an agent published in the GitHub repository davepoon/buildwithclaude (3,403 stars, last pushed 2d ago), licensed MIT. It adds 29 tokens to every session and 306 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 2026-08-30.
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