A powerful AI tool for interpreting complex scientific graphs, extracting statistical insights, and generating publication-ready captions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install graph-interpretation
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install graph-interpretation using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Scientific Graph Interpreter?
The Scientific Graph Interpreter is a specialized component within the Openclaw Skills ecosystem designed to bridge the gap between complex visual data and professional communication. It allows researchers and developers to automate the interpretation of diverse visualization types, ranging from survival curves in clinical trials to heatmaps in genomics. By analyzing the structural and statistical components of a chart, this skill produces accurate, context-aware explanations that adhere to rigorous academic and clinical standards.
Whether you are preparing a manuscript for a high-impact journal or presenting phase 3 trial results to clinicians, this skill provides the precision needed to extract hazard ratios, p-values, and confidence intervals directly from visual assets. It serves as an essential bridge for transforming raw data visualizations into structured narratives, making it an invaluable asset for those utilizing Openclaw Skills in a research or data science environment.
Scientific Graph Interpreter Use Cases
- Automating the creation of figure captions for academic journals and research posters.
- Translating complex statistical results into plain-language summaries for patients and non-technical stakeholders.
- Conducting critical appraisals of research figures to identify common visualization pitfalls like axis distortion or missing error bars.
- Batch processing large sets of images for clinical trial reports to ensure consistent reporting of primary and secondary endpoints.
How Scientific Graph Interpreter Works
- The user inputs a graph image or raw data points along with specific context such as the research field and target audience.
- The skill identifies the graph architecture, such as Kaplan-Meier, Forest plots, or ROC curves, to determine the relevant statistical elements to extract.
- It performs a structured analysis to calculate or identify key metrics including effect sizes, significance levels, and distribution medians.
- The extracted data is processed through audience-specific templates to generate tailored explanations ranging from technical deep-dives to patient-friendly summaries.
- The final output is formatted as a structured JSON object, a publication-ready caption, or a batch report for further integration into research workflows.
Scientific Graph Interpreter Setup
To integrate this functionality into your environment, ensure you have the core scripts available in your project directory. You can utilize the Openclaw Skills CLI to run interpretations directly from your terminal.
# Install required dependencies
pip install -r requirements.txt
# Run a survival curve analysis via CLI
python scripts/graph_interpreter.py \
--image survival_curve.png \
--type kaplan_meier \
--context "oncology_phase3" \
--output result.json
Scientific Graph Interpreter Data Schema & Taxonomy
The skill organizes interpretation data into a clean, hierarchical structure for easy programmatic access:
| Element | Description |
|---|---|
| Primary Outcome | Measures such as Hazard Ratio, Odds Ratio, or Mean Difference. |
| Statistical Indicators | P-values, 95% Confidence Intervals, and sample sizes (N). |
| Contextual Metadata | Target audience levels (Researcher, Clinician, Patient) and journal styles. |
| Appraisal Metrics | Quality checks for axis scaling, error bar presence, and statistical transparency. |
Scientific Graph Interpreter Advanced Features
- Multi-Audience Generation: Produce tiered explanations for different stakeholder levels from a single visual input.
- Regulatory Compliance Support: Generate summary reports that adhere to international guidelines such as ICH E3.
- Automated Quality Auditing: Detect and flag common graph pitfalls such as truncated Y-axes or improper 3D effects.
- Publication-Ready Templating: Apply specific journal styles (e.g., Nature, Lancet) to automatically formatted figure captions.
SKILL.md
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