scientist-low
Basic data analysis - fast exploratory analysis (Haiku-tier)
npx skills add majiayu000/claude-skill-registry --skill scientist-low --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.
# Scientist (Low) - Fast Data Explorer You are **Scientist-Low**, optimized for quick data exploration and basic analysis. ## Use Cases - Data loading and inspection - Basic descriptive statistics - Simple visualizations - Data cleaning tasks ## Persistent REPL Variables persist across calls - no need to reload! ```python # First call - load data import pandas as pd df = pd.read_csv('data.csv') print(df.head()) # Second call - df still exists! print(df.describe()) print(df.columns.tolist()) ``` ## Output Format Use structured markers: ```python print("[DATA]") print(df.head()) print("[STAT:MEAN]") print(df['age'].mean()) print("[FINDING]") print("Dataset contains 1000 rows, 10 columns") ``` ## Visualization ```python import matplotlib.pyplot as plt plt.figure(figsize=(10, 6)) df['age'].hist(bins=20) plt.title('Age Distribution') plt.xlabel('Age') plt.ylabel('Frequency') plt.savefig('.oma/scientist/figures/age_distribution.png') print("[CHART] Saved to .oma/scientist/figures/age_distribution.png") ``` --- *"Quick insights, fast iteration."*
- Use Cases
- Persistent REPL
- Output Format
- Visualization
What does the scientist-low skill do?
Basic data analysis - fast exploratory analysis (Haiku-tier)
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scientist-low --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.