data-science-tools
Documentation of available data science libraries (scipy, numpy, pandas, sklearn) and best practices for statistical analysis, regression modeling, and organizing analysis scripts. **CRITICAL:** All analysis scripts MUST be placed in reports/{topic}/scripts/, NOT in root scripts/ directory.
npx skills add majiayu000/claude-skill-registry --skill data-science-tools --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Science Tools Skill ## Purpose This skill documents the data science ecosystem available in this project, including: - Which Python libraries are installed and available - How to use them for statistical analysis and regression - **WHERE to place analysis scripts** (reports/{topic}/scripts/ - NOT root scripts/) - Best practices for reproducible data science ## 🚨 CRITICAL: Script Organization Rule **ALL regression, modeling, and analysis scripts MUST go in:** ``` reports/{topic}_{timestamp}/scripts/ ``` **NEVER in:** ``` scripts/ ❌ (root scripts/ is only for reusable utilities) ``` See [Script Organization Best Practices](#script-organization-best-practices) section below. ## Available Libraries ### Installed in `.venv` Virtual Environment The following data science libraries are installed and ready to use: | Library | Version | Purpose | |---------|---------|---------| | **numpy** | Latest | Numerical computing, arrays, linear algebra | | **scipy** | 1.16.3+ | Scientific computing, optimization, statistics | | **pandas** | 2.3.3+ | Data manipulation, DataFrames, time series | | **scikit-learn** | 1.7.2+ | Machine learning, regression, clustering | ### Activating the Virtual
- Purpose
- 🚨 CRITICAL: Script Organization Rule
- Available Libraries
- Installed in .venv Virtual Environment
- Activating the Virtual Environment
- Common Use Cases
- 1. Regression Modeling (scipy.optimize.curvefit)
- 2. Model Comparison
- 3. Data Manipulation with Pandas
- 4. Statistical Analysis
- Script Organization Best Practices
- Directory Structure
- When to Place Scripts in reports/{topic}/scripts/ ✅ ALWAYS for Analysis
- When to Use scripts/ (Root Level) ⚠️ ONLY for Reusable Utilities
source .venv/bin/activate && python scripts/your_script.py
source .venv/bin/activate && python scripts/analysis.py
Always activate venv first
source .venv/bin/activate
python scripts/your_script.py
pip list | grep -E "(numpy|scipy|pandas|scikit)"
python -c "from scipy.optimize import curve_fit; print('✓ scipy.optimize available')"
pip install numpy scipy pandas scikit-learn
cd reports/elbiler_danmark_20251031/scripts/
source ../../../.venv/bin/activateWhat does the data-science-tools skill do?
Documentation of available data science libraries (scipy, numpy, pandas, sklearn) and best practices for statistical analysis, regression modeling, and organizing analysis scripts. **CRITICAL:** All analysis scripts MUST be placed in reports/{topic}/scripts/, NOT in root scripts/ directory.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-science-tools --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.