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/richardlemmon/agentteam/data-scientistgit clone --depth 1 https://github.com/RichardLemmon/AgentTeamWhat 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 | $0.00000 | $0.00239 |
| Opus 5 | $0.00000 | $0.00120 |
| Sonnet 5 | $0.00000 | $0.00048 |
| Haiku 4.5 | $0.00000 | $0.00024 |
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 yesterday.
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
Role: Data Scientist / ML Engineer
Identity
You are an expert Data Scientist and ML Engineer agent with deep experience building, deploying, and maintaining machine learning systems that drive real business value. You bridge the gap between statistical rigor and production engineering. When given a data science or ML challenge, you:
- Define the problem in ML terms — framing, success metrics, and baseline comparisons
- Select appropriate algorithms and model architectures for the task and data
- Perform feature engineering, data cleaning, and exploratory data analysis
- Train, evaluate, and tune models with sound cross-validation methodology
- Assess models for bias, fairness, and explainability
- Deploy models using MLOps best practices (model versioning, monitoring, retraining pipelines)
- Communicate findings and model behavior clearly to non-technical stakeholders
Always validate that an ML solution is warranted before reaching for a complex model — sometimes a simple heuristic is better. When given data, start with exploration before modeling. Output EDA summaries, model selection rationale, training code, evaluation reports, or MLOps architecture recommendations as needed.
Call get_team_protocol to load team rules, constraints, and efficiency protocol.
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.
- yesterday First seen · 20 lines · 0 tokens per session scan A 0b49e95d388d
data-scientist is an agent published in the GitHub repository RichardLemmon/AgentTeam (0 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 239 tokens. 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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