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 vasilyu1983/AI-Agents-public --skill ai-ml-data-sciencegit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/ai-ml-data-science)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-ml-data-science"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-ml-data-science/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/vasilyu1983/ai-agents-public/ai-ml-data-science"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-ml-data-science.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
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 →
- medium Excessive Agency · line 99 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.
- medium Excessive Agency · line 104 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 | $0.00042 | $0.03074 |
| Opus 5.5 | $0.00017 | $0.01230 |
| Sonnet 5.5 | $0.00008 | $0.00615 |
| Haiku 4.5 | $0.00004 | $0.00307 |
Grade A, and why
ai-ml-data-science 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 11d 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Science Engineering Suite
Frame the decision and prediction timestamp before choosing models; features must exist at scoring time.
Quick Reference
| Need | Default Direction |
|---|---|
| reproducible Python workflow | uv plus scripts or git-friendly notebooks (marimo for reactive/diffable notebooks) |
| fast local analysis | DuckDB plus Polars; check installed API against Polars docs before adapting older examples |
| data contracts | Pandera or GX Core at dataset boundaries |
| tabular baseline | linear or logistic model plus tree-based candidate; add a tabular foundation model only after the licence and size lookup in references/modelling-patterns.md §3.1 |
| feature engineering | explicit train-serve-safe transforms |
| unlabeled text corpus | embed -> UMAP -> HDBSCAN -> c-TF-IDF; use topic-level LLM labels when document-level labels are unnecessary |
| tuning | Optuna only after the baseline is stable |
| evaluation | slices, threshold, calibration, uncertainty |
| handoff | model card, evaluation report, failure modes, monitoring expectations |
When To Use This Skill
- exploring datasets and checking modelling feasibility
- designing feature pipelines and leakage controls
- choosing and comparing model families
- clustering unlabeled text and discovering topics before a taxonomy or labeling effort exists
- building reproducible experiment workflows
- producing evaluation reports, model cards, and handoff artifacts
- reviewing whether an experiment is genuinely ready for production handoff
- explaining responsible-AI modelling mechanics: fairness and intersectionality, privacy, interpretability, poisoning, memorization, human oversight, and environmental trade-offs
- designing general multimodal models: contrastive image-text learning, fusion, VQA/document/video systems, diffusion control, adaptation, and quality-latency trade-offs
Route Elsewhere
- serving, retraining automation, monitoring, or incident response -> ai-mlops
- forecasting and temporal validation -> ai-ml-timeseries
- lakehouse or batch ingestion infrastructure -> data-lake-platform
- streaming infrastructure (Kafka, Flink, CDC) -> data-streaming
- prompting, fine-tuning, or LLM-system design -> ai-llm or ai-rag
What ships with it
33 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.
- agents/openai.yaml 385 B
- assets/eda/template-eda.md 1.5 KB
- assets/evaluation/template-evaluation-report.md 2.3 KB
- assets/evaluation/template-model-card.md 1.2 KB
- assets/features/template-feature-engineering.md 2.0 KB
- assets/project/template-quick.md 1.1 KB
- assets/project/template-standard.md 3.9 KB
- assets/review/experiment-review-template.md 4.0 KB
- data/sample-model-spec.json 5.4 KB
- data/sources.json 23 KB
- learnings.consolidated.md 2.6 KB
- learnings.md 761 B
- references/class-imbalance-patterns.md 13 KB
- references/data-contracts-lineage.md 12 KB
- references/eda-best-practices.md 5.7 KB
- references/evaluation-patterns.md 10 KB
- references/feature-engineering-patterns.md 3.2 KB
- references/feature-freshness-streaming.md 10 KB
- references/graph-model-evaluation.md 8.6 KB
- references/hyperparameter-optimization.md 14 KB
- references/interpretability-explainability.md 16 KB
- references/llm-data-pipeline.md 4.2 KB
- references/ml-diagrams.md 15 KB
- references/modelling-patterns.md 21 KB
- references/multimodal-modeling.md 13 KB
- references/production-feedback-loops.md 14 KB
- references/reproducibility-checklist.md 12 KB
- references/responsible-ai-mechanics.md 14 KB
- references/text-clustering-topic-modeling.md 17 KB
- scripts/leakage_scan.py 11 KB runs code
- scripts/ml_toolkit.py 25 KB runs code
- scripts/README.md 4.9 KB
- scripts/test_leakage_regressions.py 4.3 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.
- 11d ago Changed · +2 lines 1108329cda83
- 22d ago Changed · -44 lines · +2 tokens per session 0083ec66e3b3
- 1mo ago Changed · +4 lines 8908788b4d4d
- 1mo ago Changed · +11 lines · +4 tokens per session 141f03413635
- 1mo ago First seen · 226 lines · 36 tokens per session scan A 7e5c2f26d03e
ai-ml-data-science is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (101 stars, last pushed 11d ago), licensed MIT. It adds 42 tokens to every session and 3,074 once invoked, about $0.0002 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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