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Data Science Specialist | Skills Pool

스킬 파일

Data Science Specialist

Machine learning pipelines, data analysis, statistical modeling, and data visualization in Python. Use when asked to build an ML model, analyze a dataset, create data visualizations, write a Jupyter notebook, implement a data pipeline, tune hyperparameters, evaluate model performance, or work with pandas, scikit-learn, PyTorch, or TensorFlow.

daryllundy0 스타2026. 3. 20.

직업
카테고리: 데이터 분석

스킬 내용

Activation criteria

User language explicitly matches trigger phrases such as build a model, analyze this dataset, machine learning.
The requested work fits this skill's lane: ML models, data analysis, Jupyter notebooks, pandas/scikit-learn/PyTorch/TensorFlow.
The task stays inside this skill's boundary and avoids adjacent areas called out as out of scope: Data infrastructure/pipelines at scale (use database-specialist or dedicated tooling).

First actions

Glob('**/*.ipynb', '**/*.csv', '**/*.parquet', '**/requirements.txt') — find notebooks, data files, dependencies
Identify: task type (classification, regression, clustering, NLP, CV, EDA), data size, and compute environment
Confirm: is this exploratory analysis, a production pipeline, or model evaluation?

Decision rules

If the request is predictive modeling: define the target variable, evaluation metric, and validation strategy before choosing a model.

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Data Science Specialist

npx skillvault add daryllundy/daryllundy-claude-skills-library-claude-skills-data-science-specialist-skill-md

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작성자: daryllundy
스타: 0
업데이트: 2026. 3. 20.
직업

이 페이지의 내용

01Activation criteria

If data quality is unclear: profile the dataset first and flag missingness, skew, leakage risk, and outliers before modeling.

If the task shifts from analysis to production service design: surface the handoff to application or platform specialists explicitly.

Output contract

For analysis: notebook or script with clear section headers; visualizations with axis labels and titles
For ML models: include train/val/test split logic, evaluation metrics appropriate to task type, and a brief interpretation of results
Always include: data shape checks, null value handling, and feature type validation

Constraints

NEVER train on test data — enforce strict train/val/test separation
Scope boundary: data infrastructure and pipeline orchestration (Airflow, dbt) belongs to database-specialist or separate tooling

Reference

references/legacy-agent.md: ML pipeline patterns, visualization templates, model evaluation frameworks, feature engineering approaches

02

First actions

03Decision rules

04Output contract

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