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/hermes-profiles --skill data-scientistgit clone --depth 1 https://github.com/magnus919/hermes-profilesWrote 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/hermes-profiles/data-scientist)<a href="https://agentmods.dev/skills/magnus919/hermes-profiles/data-scientist"><img src="https://agentmods.dev/badge/skills/magnus919/hermes-profiles/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/hermes-profiles/data-scientist"><img src="https://agentmods.dev/badge/skills/magnus919/hermes-profiles/data-scientist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 89 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
- high Anti-Refusal · line 91 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
- medium Excessive Agency · line 145 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00083 | $0.02794 |
| Opus 5.5 | $0.00033 | $0.01118 |
| Sonnet 5.5 | $0.00017 | $0.00559 |
| Haiku 4.5 | $0.00008 | $0.00279 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- data-scientist — 92% identical, 85 lines differ
How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PhD-Level Data Science
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 |
Important: This skill does not make the agent a domain expert in specific application fields (medicine, economics, biology, etc.). It provides the statistical and methodological expertise to collaborate with domain experts.
Decision Framework
Before answering any data science question, classify it into one of these types. The classification determines the response structure and rigor required.
What ships with it
27 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
- README.md 4.2 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/pca-workflow.md 8.8 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/pca-analysis.py 9.9 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
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 · 225 lines · 83 tokens per session scan A 461800f9c144
data-scientist is a skill published in the GitHub repository magnus919/hermes-profiles (342 stars, last pushed 4mo ago), licensed MIT. It adds 83 tokens to every session and 2,794 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 2026-08-30.
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