An AI-powered performance analysis tool that transforms raw student exam data into professional, multi-dimensional visual charts and research-ready Word documents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install score-analysis
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 score-analysis 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 Score Analysis Skill?
The Score Analysis Skill is a highly capable asset designed for the Openclaw Skills ecosystem to streamline the breakdown of academic exam data. By integrating data detection, statistical tracking, and visual modeling, this skill eliminates the manual labor involved in parsing raw student metrics. It intelligently processes Excel or CSV formats, gracefully accommodating complex data tables containing merged cells and multi-lingual headers to output standardized structures.
Built with high technical compliance, the skill provides teachers and administrators with granular insights into horizontal peer class performance and vertical historical trends. It ensures rigorous accuracy by enforcing user-led confirmation protocols during data ingestion, bridging the gap between automated execution and data integrity.
Score Analysis Skill Use Cases
- Evaluating class-wide exam results across multiple subjects to diagnose academic strengths and systemic imbalances.
- Tracking longitudinal performance trends and individual student ranking stability over a multi-exam timeline.
- Identifying borderline or critical students who fall just short of key undergraduate pass boundaries.
- Compiling standardized, presentation-ready Word documents and optional slide decks for administrative evaluation panels.
How Score Analysis Skill Works
- Data Ingestion: The skill scans source files, auto-detects complex header formats, maps appropriate student and subject fields, and converts data into a clean JSON structure.
- Verification Loop: A statistical baseline summary featuring subject averages and outlier notifications is generated and presented to the user for structural confirmation.
- Multi-Dimensional Analytics: Once validated, processing engines perform horizontal performance comparisons against peer classes and vertical trend tracking across past timelines.
- Targeted Visualization: The script identifies critical student cohorts and produces dedicated visualization elements, including specialized grouped radar charts and subject average bar charts.
- Automated Documentation: Visualizations and structured metrics are embedded directly into a compiled research-style Word document utilizing robust three-line tables.
- Optional Deck Export: If requested, external helper scripts are triggered to map the findings into an active presentation file.
Score Analysis Skill Setup
To leverage this skill within the Openclaw Skills framework, you need to set up the necessary Python libraries for handling data frames, spreadsheet processing, and chart rendering. Run the following command in your terminal:
pip install python-docx matplotlib pandas numpy openpyxl
Score Analysis Skill Data Schema & Taxonomy
The skill manages inputs and operations through a structured workspace layout. File outputs and underlying code are organized via the following taxonomy:
Generated Artifacts
| File / Directory Name | Description |
|---|---|
*_standardized.json |
Cleaned and structuralized version of input student data. |
*_analysis.json |
Full computational output including computed means and progress attribution metrics. |
charts/ |
Target directory housing subject average bars and grouped radar charts. |
*_report.docx |
Main formal report featuring embedded graphics and research-style highlight layout boxes. |
*_presentation.pptx |
Optional automated slide deck compiling core analytic points. |
File Infrastructure
- scripts/: Contains underlying script resources (
create_template.py,generate_report_from_template.py,generate_radar_charts.py,generate_report.py) to handle explicit execution steps. - references/: Stores reference protocols and operational boundaries (
analysis_framework.md). - assets/: Maintains default document layouts (
report_template.docx) to ensure standardized output aesthetics.
Score Analysis Skill Advanced Features
- Intelligent Cell Ingestion: Smooth processing of tables with tricky layouts, mixed Chinese/English labels, and merged row blocks.
- Algorithmic Imbalance Detection: Automatic evaluation of student metrics to pinpoint top-tier volatility or subject-specific underperformance.
- Dynamic Outlier Handling: Built-in omission configuration that filters out absences, transfers, and medical exemptions from skewing core mathematical averages.
- Flexible Score Adjustments: Native parsing capability that handles combined raw and adjusted scores matching custom school branding demands.
SKILL.md
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