An automated, local data-analysis pipeline that cleans, tags, and processes student score sheets from Excel, producing professional diagnostic reports and rich visual charts without needing external API dependencies.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install score-analyzer-skill
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-analyzer-skill 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 Analyzer?
The Score Analyzer is a robust, self-contained Python-driven pipeline designed for educators, institutions, and administrative agents. Developed as a highly adaptable component of the Openclaw Skills ecosystem, it automatically processes Excel sheets containing grade and student performance data. By employing powerful local intelligence along with pandas, matplotlib, and seaborn, it produces complex statistical summaries, narrative reports, and up to 12 advanced diagnostic charts without requiring costly external LLM API calls during script runtime.
By deploying this asset within Openclaw Skills, you can bypass manual spreadsheet manipulation. The skill automatically normalizes complex headers, handles data cleaning, tags edge-case student performance, calculates dynamic percentile-based thresholds, and generates high-impact visual representations. It ultimately packages ready-to-use HTML, Word, and Markdown documents into a single distributable ZIP file.
Score Analyzer Use Cases
- Automated processing of mid-term or final exam grade spreadsheets.
- Identifying student learning gaps through advanced statistical plotting like boxplots and heatmaps.
- Generating individual student progress reports with personalized tag metrics (e.g., performance alerts).
- Formulating relative performance evaluations using percentile-based (P20 and P80) thresholds rather than static, arbitrary grade lines.
How Score Analyzer Works
- Data Normalization & Extraction: The agent processes the input Excel spreadsheet, intelligently restructuring complex or merged multi-level headers into a clean, uniform long-format CSV.
- Cleaning & Enrichment: The script filters invalid entries and tags specific student behavior profiles (e.g., lopsided subject profiles).
- Dynamic Threshold Evaluation: It computes percentile-based pass and excellence bounds (P20/P80) to accommodate varying exam difficulties.
- Data Verification: A mandatory check validates file structure and dynamic metrics before moving to generation.
- Visualization Generation: It generates up to 12 localized statistical charts (radar plots, heatmaps, CDFs) tailored to whether data groupings (classes, majors) are present.
- Document Assembly & Packaging: The Markdown report content is synthesized with base64-embedded visual charts into a portable HTML document and a structured Word file, compressed into a single ZIP archive.
Score Analyzer Setup
Ensure your environment has Python 3.10+ and the required native system packages (such as Chinese fonts if plotting in CJK environments).
# Install required dependencies
pip install pandas matplotlib seaborn openpyxl python-docx numpy scipy
# Optional: Ensure CJK fonts are available on Debian/Ubuntu if charts require Chinese characters
sudo apt update && sudo apt install -y fonts-noto-cjk
Score Analyzer Data Schema & Taxonomy
The Skill enforces strict intermediate schemas to guarantee robust report assembly:
Intermediate Long-Format CSV Schema (reports/data.csv)
| Column Name | Type | Description |
|---|---|---|
student_id |
String | Unique identifier for each student |
student_name |
String | Student's name |
student_class |
String | Optional grouping attribute (class/major) |
subject |
String | Evaluated subject or category |
value |
Float | Numeric grade value |
Dynamic Thresholds JSON (reports/dynamic_thresholds.json)
- D-G (P80): Dynamic Excellent Line (threshold surpassing bottom 80%)
- D-E (P20): Dynamic Passing Line (threshold surpassing bottom 20%)
- Pass Rates: Subject-specific dynamic passing rates
Score Analyzer Advanced Features
- Dynamic Percentile Thresholding: Evaluates exams based on relative performance (P20/P80) instead of absolute scores to ensure fair evaluation of high-difficulty tests.
- Adaptive Visualizations: Checks for data groupings to dynamically toggle between generating 6 or 9 base visual plots (supporting up to 12 inline chart placeholders).
- Resilient Header Mapping: Bypasses rigid algorithmic extraction when encountering nested or merged Excel headers, leveraging pandas and agent processing to map columns accurately.
- Bundled Offline Distribution: Outputs fully contained HTML reports with inline base64-encoded charts and standard DOCX word records, completely packaged into a portable ZIP.
SKILL.md
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