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Magnus Hedemark
487f8923dc
PhD-level data science expertise with decision framework, five reference documents (statistical methodology, experimental design, causal inference, regression modeling, Bayesian workflow), five automation scripts (power analysis, assumption diagnostics, model comparison, effect size calculator, experimental design generator), and two report templates. Python default with --engine r flag for R output. Dual language support.
89 lines
4.2 KiB
Markdown
89 lines
4.2 KiB
Markdown
# Data Scientist Agent Skill
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An [Agent Skills](https://agentskills.io)-compatible skill that enables any AI agent to operate at PhD-level expertise in data science, statistics, and machine learning.
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## What This Skill Provides
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When loaded, this skill transforms how an agent reasons about data science problems:
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- **Classifies questions** into advice, analysis, research, design, review, or methodology — and applies the appropriate level of rigor
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- **Checks assumptions before methods** — the core PhD-level principle that separates good analysis from bad
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- **Reaches for the right reference** — statistical tests, experimental designs, causal inference, regression models, Bayesian workflow
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- **Runs power analysis, assumption diagnostics, model comparison, and effect size calculations** with real scripts
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- **Generates analysis reports and experimental plans** in pre-registration format
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## Skill Structure
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```
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data-scientist/
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├── SKILL.md # Decision framework & trigger conditions
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├── references/
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│ ├── statistical-methodology.md # Test selection, assumptions, effect sizes
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│ ├── experimental-design.md # Design taxonomy, power, A/B testing
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│ ├── causal-inference-framework.md # DAGs, potential outcomes, identification
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│ ├── regression-modeling.md # Model hierarchy, diagnostics, GLMs
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│ └── bayesian-workflow.md # Prior, MCMC, model comparison
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├── scripts/
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│ ├── power-analysis.py # Sample size / detectable effect calculator
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│ ├── assumption-diagnostics.py # Model assumption checking
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│ ├── model-comparison.py # AIC/BIC/CV model comparison
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│ ├── effect-size-calculator.py # Effect sizes with confidence intervals
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│ └── experimental-design.py # Randomization schedule generator
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└── assets/
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├── report-template.md # Analysis report standard format
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└── experimental-plan-template.md # Pre-registration-style planning
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```
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## Triggers
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Load this skill when the task involves:
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- **Statistical methods:** hypothesis testing, regression, Bayesian analysis, p-values, confidence intervals
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- **Research design:** experiments, A/B testing, power analysis, sample size, randomization
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- **Causal questions:** effect estimation, causality, treatment effects, identification strategies
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- **Modeling:** machine learning, prediction, model selection, cross-validation
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- **General:** "analyze this data," "what model should I use," "review this analysis"
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## Usage Examples
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```bash
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# Power analysis for a t-test
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python scripts/power-analysis.py --design ttest-ind --effect-size 0.5 --alpha 0.05 --power 0.80
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# Power analysis with R output
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python scripts/power-analysis.py --design anova --k 3 --effect-size 0.25 --engine r
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# Effect size from means and SDs
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python scripts/effect-size-calculator.py --design cohens-d --mean1 10 --mean2 8 --sd1 2.5 --sd2 2.8 --n1 30 --n2 30
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# Model comparison
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python scripts/model-comparison.py --models "OLS AIC=1200 BIC=1220 k=5" "GLM AIC=1190 BIC=1215 k=6"
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# Generate experimental design
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python scripts/experimental-design.py --design crd --treatments Control Treatment --n-per-group 20 --seed 42
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```
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All scripts accept `--json` for machine-readable output and `--engine r` for R equivalents.
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## Requirements
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Python 3.10+ with:
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- `scipy >= 1.10` (power analysis, effect sizes, diagnostics)
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- `numpy >= 1.24` (most scripts)
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- `statsmodels >= 0.14` (assumption diagnostics from fitted models, model comparison)
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- `pandas >= 2.0` (data loading, model comparison)
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Optional: `rpy2` for R integration via `--engine r`.
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## Domain Boundaries
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This skill provides **statistical and methodological expertise**, not domain knowledge. It is designed to collaborate with domain experts who know their application field (medicine, economics, biology, engineering, etc.) but need rigorous data science methodology applied to their problems.
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## Language Support
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All scripts default to Python computation. The `--engine r` flag outputs equivalent R code, making this skill useful in R-dominant environments.
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## License
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MIT
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