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 skills add magnus919/agent-skills --skill data-scientistgit clone --depth 1 https://github.com/magnus919/agent-skillsWrote 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/skills/magnus919/agent-skills/data-scientist)<a href="https://agentmods.dev/skills/magnus919/agent-skills/data-scientist"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/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/skills/magnus919/agent-skills/data-scientist"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/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 | $0.00145 | $0.03481 |
| Opus 5.5 | $0.00058 | $0.01392 |
| Sonnet 5.5 | $0.00029 | $0.00696 |
| Haiku 4.5 | $0.00015 | $0.00348 |
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.
This is a copy
92% identical to data-scientist — 85 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PhD-Level Data Science
Routing Boundaries
This skill owns general statistical and machine-learning methodology. Route to
actuarial-risk-modeling when the primary context is insurance, claims, reserving,
solvency, credibility, risk classification, tail risk, or financial-risk statistical
modeling, because those tasks require domain-specific exposure, development, calibration,
and governance checks. Route to financial-modeling for deterministic operating models,
unit economics, SaaS metrics, pricing scenarios, fundraising, and cash-flow analysis.
Remain here when those contexts are incidental and the core question is general inference,
causal design, experimentation, or model methodology.
When Not to Use
- Do not use this skill as the primary owner for insurance, actuarial, claims, reserving, solvency, credibility, tail-risk, or financial-risk statistical modeling; use
actuarial-risk-modeling. - Do not use it for deterministic operating models, unit economics, SaaS metrics, pricing scenarios, fundraising, or cash-flow analysis; use
financial-modeling.
Core Competencies
A PhD-level data scientist masters eight competency domains. This skill encodes all of them. When loaded, the agent operates within this scope:
| # | Competency | What It Enables |
|---|---|---|
| 1 | Mathematical & Statistical Foundations | Probability theory, statistical inference, linear algebra, optimization, asymptotic theory — the language in which all methods are expressed |
| 2 | Research Design & Methodology | Formulating testable questions, study design (observational vs experimental), power analysis, bias identification, preregistration |
| 3 | Statistical Modeling & Inference | Parametric and nonparametric methods, regression (linear, GLM, mixed, GAM, nonparametric), Bayesian inference, time series, survival analysis, multivariate methods |
| 4 | Machine Learning & Computational Methods | Supervised/unsupervised/deep/reinforcement learning, learning theory, model selection, regularization, ensembles, transformers, probabilistic ML |
| 5 | Causal Inference & Experimentation | DAGs, potential outcomes, identification strategies (IV, RDD, DID, matching, synthetic control), A/B testing, sensitivity analysis |
| 6 | Reproducibility & MLOps | Version control, environment management, pipeline orchestration, experiment tracking, model deployment, monitoring |
| 7 | Communication & Impact | Scientific writing, visualization, uncertainty communication, stakeholder translation, peer review, grant writing |
| 8 | Research Leadership | Identifying novel research questions, literature synthesis, mentoring, cross-disciplinary collaboration, ethical conduct |
What ships with it
29 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/experimental-plan-template.md 4.8 KB
- assets/report-template.md 3.6 KB
- evals/evals.json 11 KB
- README.md 5.0 KB
- references/bayesian-workflow.md 10 KB
- references/causal-inference-framework.md 14 KB
- references/data-science-coding-workflow.md 14 KB
- references/docker-experiment-isolation.md 7.5 KB
- references/experimental-campaign-protocol.md 25 KB
- references/experimental-design.md 9.8 KB
- references/interpretability-sources.md 1.2 KB
- references/interpretability-workflow.md 2.1 KB
- references/pytorch-integration.md 19 KB
- references/regression-modeling.md 8.9 KB
- references/sklearn-integration.md 16 KB
- references/statistical-methodology.md 11 KB
- references/subagent-experiment-supervision.md 14 KB
- scripts/assumption-diagnostics.py 17 KB runs code
- scripts/detect-compute.py 15 KB runs code
- scripts/Dockerfile 428 B
- scripts/effect-size-calculator.py 15 KB runs code
- scripts/experimental-design.py 12 KB runs code
- scripts/model-comparison.py 9.6 KB runs code
- scripts/power-analysis.py 20 KB runs code
- scripts/test_campaign_protocol.sh 4.6 KB runs code
- scripts/test_detect_compute.sh 7.3 KB runs code
- scripts/test_references_completeness.sh 6.1 KB runs code
- scripts/test_supervision_protocol.sh 5.3 KB runs code
- templates/interpretability-report.md 760 B
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 Changed · +5 lines 891e1bc1ec41
- 1mo ago Changed · +16 tokens per session 641d7160b595
- 1mo ago First seen · 275 lines · 129 tokens per session scan A e6705446feff
data-scientist is a skill published in the GitHub repository magnus919/agent-skills (151 stars, last pushed yesterday), licensed MIT. It adds 145 tokens to every session and 3,481 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to data-scientist, differing in 85 lines, and is treated as a copy.
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