graph-interpretation
Use when interpreting scientific graphs and charts, explaining data visualizations for research presentations, writing figure captions for publications, or analyzing trends in clinical research data. Converts complex visual data into clear, accurate explanations for academic papers, clinical reports, and public presentations.
npx skills add majiayu000/claude-skill-registry --skill graph-interpretation --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.
# Scientific Graph Interpreter Interpret and explain scientific graphs, charts, and data visualizations for research publications, clinical presentations, and academic communications with precision and clarity. ## Quick Start ```python from scripts.graph_interpreter import GraphInterpreter interpreter = GraphInterpreter() # Comprehensive graph analysis analysis = interpreter.interpret( image_path="figure_1.png", graph_type="kaplan_meier", context="oncology_phase3_trial", audience="clinicians" ) print(analysis.statistical_summary) print(analysis.clinical_significance) print(analysis.suggested_caption) ``` ## Core Capabilities ### 1. Multi-Type Graph Analysis ```python analysis = interpreter.analyze( graph_type="forest_plot", data={ "studies": ["Study A", "Study B", "Study C"], "effect_sizes": [1.2, 0.8, 1.5], "confidence_intervals": [[1.0, 1.4], [0.6, 1.0], [1.2, 1.8]], "overall_effect": 1.15, "heterogeneity_p": 0.04 } ) ``` **Supported Graph Types:** | Graph Type | Common Use | Key Elements to Extract | |------------|------------|------------------------| | **Kaplan-Meier** | Survival analysis | Median survival, HR, 95% CI, log-rank p | | **Forest Plot** | Meta-analysis | Effect si
- Quick Start
- Core Capabilities
- 1. Multi-Type Graph Analysis
- 2. Statistical Interpretation
- 3. Audience-Specific Explanations
- 4. Figure Caption Generation
- 5. Critical Appraisal
- CLI Usage
- Common Patterns
- Pattern 1: Clinical Trial Primary Endpoint
- Pattern 2: Meta-Analysis Forest Plot
- Pattern 3: Diagnostic Accuracy ROC
- Quality Checklist
- Best Practices
Comprehensive analysis python scripts/graph_interpreter.py \ Generate publication caption Batch process figures
What does the graph-interpretation skill do?
Use when interpreting scientific graphs and charts, explaining data visualizations for research presentations, writing figure captions for publications, or analyzing trends in clinical research data. Converts complex visual data into clear, accurate explanations for academic papers, clinical reports, and public presentations.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill graph-interpretation --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.