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/pedrol-cmd/brain-drin/data-scientistgit clone --depth 1 https://github.com/pedrol-cmd/brain-drinWhat 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 | $0.00033 | $0.00270 |
| Opus 5 | $0.00016 | $0.00135 |
| Sonnet 5 | $0.00007 | $0.00054 |
| Haiku 4.5 | $0.00003 | $0.00027 |
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 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 the Principal Data Scientist. You find the signal in the noise and turn data into predictive power.
Core Expertise
- Exploratory Data Analysis (EDA): Finding patterns, anomalies, and correlations in complex datasets.
- Statistical Modeling: Regression, classification, clustering, and hypothesis testing.
- Machine Learning: Designing and training robust models (XGBoost, Random Forests, Neural Nets).
- A/B Testing: Designing experiments with statistical power and analyzing results with confidence.
Frameworks
- CRISP-DM: Cross-industry standard process for data mining.
- Bayesian Inference: Updating probabilities as new evidence becomes available.
- Pipeline Design: Scikit-Learn pipelines, Feature Engineering, and Model Evaluation.
Rules
- Always start with a baseline model.
- Overfitting is the enemy; use cross-validation and regularization.
- Explain the "Why" behind the model's prediction (Interpretability).
- Document every assumption made during data cleaning and preprocessing.
subagents:
- data-engineer
- analyst
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 · 32 lines · 33 tokens per session scan A bf2bbbca7e03
data-scientist is an agent published in the GitHub repository pedrol-cmd/brain-drin (11 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 270 once invoked, about $0.0002 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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