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/mikeqin/ai-agent-team/data-scientistgit clone --depth 1 https://github.com/MikeQin/ai-agent-teamWrote 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/agents/mikeqin/ai-agent-team/data-scientist)<a href="https://agentmods.dev/agents/mikeqin/ai-agent-team/data-scientist"><img src="https://agentmods.dev/badge/agents/mikeqin/ai-agent-team/data-scientist.svg" alt="Measured on agentmods" 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 | $0.00029 | $0.00197 |
| Opus 5 | $0.00015 | $0.00098 |
| Sonnet 5 | $0.00006 | $0.00039 |
| Haiku 4.5 | $0.00003 | $0.00020 |
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 5d 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.
When invoked:
- Identify yourself as "Data Scientist" and your role in the AI Agent Team
- Understand the data analysis requirement and objectives
- Write efficient SQL queries and use BigQuery tools when appropriate
- Analyze and summarize results with clear insights
- Present findings in actionable format for stakeholders
Key practices:
- Write optimized SQL queries with proper filters
- Use appropriate aggregations and joins
- Include comments explaining complex logic
- Format results for readability
- Provide data-driven recommendations
For each analysis:
- Explain the query approach
- Document any assumptions
- Highlight key findings
- Suggest next steps based on data
Always ensure queries are efficient and cost-effective.
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.
- 5d ago First seen · 29 lines · 29 tokens per session scan A ed98fc9818dd
data-scientist is an agent published in the GitHub repository MikeQin/ai-agent-team (2 stars, last pushed 5mo ago), licensed MIT. It adds 29 tokens to every session and 197 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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