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PDF 教材解析與 RAG 索引建立，保留頁碼和行號 metadata。Triggers: 索引教材, 解析 PDF, 建立索引, index, parse pdf, 上傳教材, 導入教材.
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Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.
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npx skills add https://github.com/u9401066/anesthesia-exam --skill knowledge-indexerThe command stays on one line. Scroll horizontally to inspect it before copying.
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MCP tools for PDF ingestion → extract figures, tables, sections → build knowledge graph. Transforms PDF into queryable assets (images, tables, text) with cross-document RAG. Triggers: PDF, ingest, extract, 圖片, 表格, figure, table, manifest, knowledge graph, 知識圖譜, RAG, 文獻分析.
難度分類器，基於 Ragas 標準評估題目難度。Triggers: 難度分類, 難度評估, difficulty, 評估難度, 調整難度.
重複題目檢查器，檢測語意重複和過度相似的題目。Triggers: 查重, 重複檢查, duplicate, 相似題, 題目查重, 檢查重複.
Showing SKILL.md
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| name | knowledge-indexer |
| description | PDF 教材解析與 RAG 索引建立，保留頁碼和行號 metadata。Triggers: 索引教材, 解析 PDF, 建立索引, index, parse pdf, 上傳教材, 導入教材. |
負責將 PDF 教材解析並建立 RAG 索引，保留頁碼和行號 metadata 以支援精準來源追蹤。
# 使用 asset-aware-mcp 或 PyMuPDF
parsed = pdf_parse(file_path, extract_images=True)
# 輸出:
# - pages[]: 每頁內容
# - images[]: 圖片 + 圖說
# - metadata: 頁碼、行號
chunks = []
for page in parsed.pages:
for paragraph in page.paragraphs:
chunk = {
"text": paragraph.text,
"metadata": {
"document": file_name,
"page": page.number,
"line_start": paragraph.line_start,
"line_end": paragraph.line_end,
"chapter": detect_chapter(paragraph),
"section": detect_section(paragraph)
}
}
chunks.append(chunk)
# 使用 LightRAG 或 OpenAI Embedding
for chunk in chunks:
chunk["embedding"] = embed(chunk["text"])
# 儲存到向量資料庫
index.add(chunks)
# 儲存 metadata 映射
save_metadata_map(document_id, chunks)
{
"chunk_id": "ch_001",
"text": "Propofol 是一種靜脈麻醉藥...",
"embedding": [0.1, 0.2, ...],
"metadata": {
"document": "Miller's Anesthesia 9th",
"page": 42,
"line_start": 15,
"line_end": 23,
"chapter": "第三章 靜脈麻醉藥",
"section": "3.2 Propofol",
"has_image": false,
"image_ref": null
}
}
📚 教材索引完成
├── 文件: Miller's Anesthesia 9th Edition.pdf
├── 頁數: 2,456 頁
├── 章節: 48 章
├── 切分: 12,345 個 chunks
├── 圖片: 892 張（含圖說）
└── 索引大小: 156 MB
✅ Metadata 保留
├── 頁碼追蹤: 100%
├── 行號追蹤: 100%
└── 章節標記: 100%
索引 ID: idx_miller_9th_20260203