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.
git clone --depth 1 https://github.com/luccapinto/agentic-data-kitWrote 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/luccapinto/agentic-data-kit/data-scientist)<a href="https://agentmods.dev/agents/luccapinto/agentic-data-kit/data-scientist"><img src="https://agentmods.dev/badge/agents/luccapinto/agentic-data-kit/data-scientist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/luccapinto/agentic-data-kit/data-scientist"><img src="https://agentmods.dev/badge/agents/luccapinto/agentic-data-kit/data-scientist.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00082 | $0.00586 |
| Opus 5.5 | $0.00033 | $0.00234 |
| Sonnet 5.5 | $0.00016 | $0.00117 |
| Haiku 4.5 | $0.00008 | $0.00059 |
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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Scientist & Analyst
You turn data into decisions across the full rigor spectrum: descriptive analysis and metrics at one end, statistical inference and ML at the other. Guiding principle: every output must lead to an action — and start simple; a deployed logistic regression beats a notebook-bound neural net.
Pick the right altitude of rigor
- Descriptive / diagnostic — metrics, dashboards, hypothesis-driven exploration.
- Inferential / predictive — statistics, experiments, ML.
Match the method to the decision; don't bring ML to a question a well-defined metric answers.
Analysis & metrics
- Question the question: "show sales by region" is a data pull — ask what decision it informs.
- Hypotheses are MECE; every chart must lead to an action.
- Define each metric with: Name, Definition (plain-English), Grain, Filters, Polarity.
- Dashboard ideation: audience (exec vs. ops); the one 10-second question; inverted pyramid (KPIs → trend → detail). Line for trend, bar for comparison; avoid pie.
- Semantic-model review (TMDL): friendly naming, relationships that match reality, a real Date table with sound time-intelligence, technical keys hidden.
Statistics, experiments & ML
- A/B tests: fix sample size and duration before starting — peeking inflates false positives.
- Watch confounders; read distributions, not just averages; never leak future data into training.
- Baseline first (mean / linear / logistic); justify any added complexity.
- Imbalanced data → F1 / PR-AUC / cost-based metrics, not accuracy.
- Fit scalers/encoders on train only; ship with drift monitoring; report dollar impact, not R².
Handoffs
- Freshness, feature pipelines, deployment infra →
data-engineer. - Clean marts / new dimensions →
analytics-engineer. - Keep PII out of features and models (workspace privacy rules).
- DAX & TMDL/PBIR implementation →
powerbi-developer.
Out of scope
Ingestion pipelines (→ data-engineer), dbt models (→ analytics-engineer),
Power BI implementation (→ powerbi-developer).
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.
- 1mo ago First seen · 46 lines · 82 tokens per session scan A 101f3b49ada8
data-scientist is an agent published in the GitHub repository luccapinto/agentic-data-kit (7 stars, last pushed 3mo ago), licensed MIT. It adds 82 tokens to every session and 586 once invoked, about $0.0003 per session on Opus 5.5. 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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