data-analysis-sci
Data collection, analysis, and interpretation for scientific inquiry. Covers measurement and units, data recording and organization, descriptive and inferential statistics, graphical representation, error analysis, drawing valid conclusions from evidence, and recognizing the limits of data. Use when collecting, analyzing, visualizing, or interpreting scientific data at any level.
npx skills add majiayu000/claude-skill-registry --skill data-analysis-sci --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 Analysis for Science Data analysis is where observations become evidence. Raw measurements are not conclusions -- they are the raw material from which conclusions are carefully extracted. This skill covers the full pipeline from recording measurements to drawing valid conclusions, with emphasis on honest reporting, appropriate statistical reasoning, and the discipline of knowing where the data end and interpretation begins. **Agent affinity:** wu (precision and error analysis), mcclintock (data interpretation) **Concept IDs:** sci-measurement-units, sci-data-tables-graphs, sci-error-analysis, sci-evidence-conclusions ## The Analysis Pipeline | Stage | What happens | Key question | |---|---|---| | 1. Measurement | Quantitative observation with instruments | What is being measured, with what tool, to what precision? | | 2. Recording | Data captured in organized form | Are the data recorded systematically and in real time? | | 3. Description | Summary statistics and visualization | What do the data look like? | | 4. Inference | Statistical tests | Are observed patterns real or noise? | | 5. Conclusion | Interpretation and limitation | What can the data tell us, and what can the
- The Analysis Pipeline
- Stage 1 -- Measurement
- SI Units and Dimensional Consistency
- Significant Figures
- Accuracy vs. Precision
- Stage 2 -- Recording
- Data Tables
- Data Integrity
- Stage 3 -- Description
- Descriptive Statistics
- Graphical Representation
- Stage 4 -- Inference
- The Logic of Statistical Testing
- Common Tests
What does the data-analysis-sci skill do?
Data collection, analysis, and interpretation for scientific inquiry. Covers measurement and units, data recording and organization, descriptive and inferential statistics, graphical representation, error analysis, drawing valid conclusions from evidence, and recognizing the limits of data. Use when collecting, analyzing, visualizing, or interpreting scientific data at any level.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-analysis-sci --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.