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hkdse-ict-report

Generate HKDSE ICT class and per-student DOCX reports from AI-graded JSON data.

Updated Aug 27, 2026
Search Tags:#docx#data-visualization#traditional-chinese#student-feedback#hkdse-ict#ai-graded#class-reporting
One-click install
npx skills add https://github.com/AKCqhzdy/dse-subject-grading --skill hkdse-ict-report
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hkdse-ict-report
Source: https://github.com/AKCqhzdy/dse-subject-grading/tree/main/skills/hkdse-ict-report
Command: npx skills add https://github.com/AKCqhzdy/dse-subject-grading --skill hkdse-ict-report

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, matplotlib, python-docx, Pillow, numpy, pydantic.

What problem does it solve?

本 Skill 專為 HKDSE ICT 考試的成績分析與報告製作而設計，能自動根據 AI 分級的 JSON 數據產出班級與個別學生的 DOCX 報告，並嵌入原始學生作答影像。

Core Features & Use Cases

  • 以 AI 分級 JSON 作為輸入，完成班級成績概覽、逐生分析與評語、以及 DOCX 報告組裝。
  • 報告內容以繁體中文呈現，只有技術識別符與檔名使用英文，便於跨系統整合。
  • 支援嵌入學生答題影像、產出圖表與成績分佈分析，適用於班級層面與個別學生的回饋。
  • 常見情境包括：產出班級綜合報告、為每位學生生成個別回饋與改進建議、以及分析等級分佈與題目表現。

Quick Start

Run the workflow to convert AI-graded JSON into both class-level and per-student DOCX reports.

Frequently Asked Questions about hkdse-ict-report

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate DOCX class reports from AI-graded JSON data?▼

You can generate DOCX class reports by loading AI-graded JSON data into the workflow, which orchestrates end-to-end generation of class-level and per-student reports with embedded student answer images and performance charts.

Can I embed original student answer images into individual DOCX feedback reports?▼

Yes, the report generation process embeds original student answer images directly into the per-student DOCX feedback reports alongside level-based performance analysis and individual comments.

What Python environment do I need for automated HKDSE ICT report generation?▼

Automated HKDSE ICT report generation requires Python 3.12+ along with pandas, matplotlib, python-docx, Pillow, numpy, and pydantic libraries to process grading results and assemble DOCX files.

How does data visualization work for class-level performance analysis in DOCX reports?▼

Data visualization for class-level performance analysis uses matplotlib and numpy to generate charts from AI-graded JSON data, which are then embedded into the final DOCX reports to show grade distribution and question performance.

Does the report generation workflow support Traditional Chinese text output?▼

Yes, the report content is presented in Traditional Chinese, while only technical identifiers and filenames use English to facilitate cross-system integration.

What input file structure is required for HKDSE ICT level-based performance analysis?▼

Level-based performance analysis requires AI-graded JSON inputs located in the output/{year}/ directory and rubric configuration files in rubric/{grade_year}/level_division.json to process grading results accurately.

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