data-science-python
Python data science: notebook structure, data validation, reproducibility, and model documentation
69
60%
Does it follow best practices?
Impact
76%
1.11xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/data-science-python/SKILL.mdQuality
Discovery
32%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description reads as a list of topic areas rather than a functional skill description. It lacks action verbs describing what the skill does, has no 'Use when...' clause to guide selection, and could benefit from more specific trigger terms like 'Jupyter', 'pandas', or '.ipynb'. The noun-phrase style makes it unclear whether this skill creates notebooks, reviews them, or provides best practices.
Suggestions
Add explicit action verbs describing what the skill does, e.g., 'Reviews and structures Jupyter notebooks for reproducibility, validates data pipelines, and documents ML models.'
Add a 'Use when...' clause with trigger terms, e.g., 'Use when the user asks about Jupyter notebooks, data science workflows, pandas pipelines, sklearn models, .ipynb files, or reproducible analysis.'
Include common user-facing terms like 'Jupyter', 'pandas', 'machine learning', 'DataFrame', '.ipynb' to improve trigger term coverage and distinctiveness.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Python data science) and some areas (notebook structure, data validation, reproducibility, model documentation), but these are more like topic categories than concrete actions. No verbs describing what the skill actually does. | 2 / 3 |
Completeness | Partially addresses 'what' through topic listing but lacks any explicit 'when' clause or trigger guidance. Per rubric guidelines, a missing 'Use when...' clause caps completeness at 2, and the 'what' is also weak (topics rather than actions), warranting a score of 1. | 1 / 3 |
Trigger Term Quality | Includes relevant keywords like 'Python', 'data science', 'notebook', 'data validation', 'reproducibility', and 'model documentation', but misses common variations users might say like 'Jupyter', 'pandas', 'sklearn', 'machine learning', '.ipynb', or 'data analysis'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of Python data science with notebook structure and reproducibility provides some specificity, but could overlap with general Python coding skills, data analysis skills, or documentation skills. The scope is broad enough to create potential conflicts. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-crafted data science skill that is concise, actionable, and well-organized. Its main strength is providing executable code examples alongside practical conventions without over-explaining concepts Claude already knows. The only notable weakness is the lack of explicit validation checkpoints and error recovery feedback loops between workflow stages, particularly around feature engineering and model evaluation steps.
Suggestions
Add explicit validation/feedback loops between workflow stages, e.g., 'After feature engineering, validate: assert no data leakage between train/test splits' and 'If evaluation metrics are below threshold, revisit feature engineering before proceeding.'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. It doesn't explain what notebooks are, what pandas does, or other concepts Claude already knows. Every section delivers actionable information without padding. The model documentation section is a concise checklist rather than verbose explanation. | 3 / 3 |
Actionability | Provides fully executable code examples for data validation (assert statements and pandera), reproducibility (seed setting), and sklearn pipelines. Concrete naming conventions (model_rf_20260115_v1.pkl) and specific tool recommendations (MLflow, pandera) make guidance immediately actionable. | 3 / 3 |
Workflow Clarity | The notebook structure provides a clear sequence, and data validation includes a validation step after loading. However, the overall data science workflow lacks explicit validation checkpoints between stages (e.g., validate after feature engineering, validate model outputs before saving). There's no feedback loop for error recovery when validation fails. | 2 / 3 |
Progressive Disclosure | For a skill under 50 lines with no need for external references, the content is well-organized into clearly labeled sections that serve as a concise overview. Each section is appropriately scoped and the structure supports easy navigation without requiring separate files. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- ucdavis/ai-skills-registry
- Commit
c0b2e4b
- Reviewed
Table of Contents
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