Borrowing it
Nothing to install: this file belongs to u9401066/rootcause-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/u9401066/rootcause-mcp/master/.claude/skills/pdf-asset-extractor/SKILL.mdgit clone --depth 1 https://github.com/u9401066/rootcause-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/u9401066/rootcause-mcp/pdf-asset-extractor)<a href="https://agentmods.dev/skills/u9401066/rootcause-mcp/pdf-asset-extractor"><img src="https://agentmods.dev/badge/skills/u9401066/rootcause-mcp/pdf-asset-extractor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/u9401066/rootcause-mcp/pdf-asset-extractor"><img src="https://agentmods.dev/badge/skills/u9401066/rootcause-mcp/pdf-asset-extractor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00084 | $0.03261 |
| Opus 5 | $0.00042 | $0.01631 |
| Sonnet 5 | $0.00017 | $0.00652 |
| Haiku 4.5 | $0.00008 | $0.00326 |
Grade A, and why
pdf-asset-extractor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to pdf-asset-extractor — 129 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 380 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Asset Extractor (MCP Tools)
描述
將 PDF 文件分解為可查詢的資產（圖片、表格、章節），並建立跨文獻知識圖譜。
核心能力：
- 📄 PDF 匯入 → 雙引擎（PyMuPDF 快速 / Marker 高精度）自動提取圖片、表格、文字
- 🖼️ 圖片提取 → 以 base64 返回，支援 Vision AI 分析
- 📊 表格提取 → 轉換為 Markdown 格式
- 🧭 章節導航 → 動態層級 Section Tree（支援任意深度）
- 🔍 知識圖譜 → 跨文獻關係查詢 (LightRAG)
- 📈 圖譜視覺化 → 導出 Mermaid 圖表
觸發條件
- 「ingest PDF」、「匯入 PDF」、「解析文件」、「分析論文」
- 「看 manifest」、「文件結構」、「有什麼圖表」
- 「取得圖片」、「fetch figure」、「拿表格」、「extract」
- 「知識圖譜」、「cross-document」、「比較文獻」、「RAG」
- 「視覺化圖譜」、「export graph」、「mermaid」
- 「章節」、「section」、「導航」、「樹狀結構」
🔧 雙引擎策略
| 引擎 | 強項 | 弱項 | 觸發方式 |
|---|---|---|---|
| PyMuPDF (預設) | 快速、輕量 (~50MB) | 版面分析精度較低 | ingest_documents() |
| Marker (高精度) | 精確 bbox、section hierarchy | 重模型 (~1GB)、較慢 | ingest_documents(use_marker=True) 或 parse_pdf_structure() |
Marker 產出額外資料
blocks.json— 結構化區塊（含 bbox、polygon、section_hierarchy）- 支援 Section Navigation 動態層級導航
⚠️ 重要警告
🖼️ 圖片 Context 限制
Base64 圖片非常大，一次只處理一張！
- 一張圖片 ≈ 200KB base64 ≈ ~270K tokens
- 對話 context 有限，多張圖片會快速耗盡
- 建議流程：先
inspect_document_manifest→ 選定目標圖 → 一次 fetch 一張
👁️ 視覺能力提醒
如果 AI 有視覺能力（Vision），可直接分析返回的圖片 如果是純文字 AI，應誠實告知無法分析圖片內容
📸 圖片 ID 命名規則
系統以
fig_{page}_{index}命名，非解析圖說文字 需手動對照 manifest 頁碼與實際 Figure 編號
🔧 可用 MCP Tools
文件處理
| Tool | 用途 | 參數 |
|---|---|---|
ingest_documents |
匯入 PDF（ETL 流程） | file_paths: list[str], async_mode: bool, use_marker: bool |
parse_pdf_structure |
Marker 結構化解析（單檔） | file_path: str |
get_job_status |
查詢 ETL 進度 | job_id: str |
list_jobs |
列出所有工作 | active_only: bool |
cancel_job |
取消 ETL 工作 | job_id: str |
資產查詢
| Tool | 用途 | 參數 |
|---|---|---|
list_documents |
列出所有已處理文件 | 無 |
inspect_document_manifest |
查看文件結構（圖/表/章節清單） | doc_id: str |
fetch_document_asset |
取得特定資產 | doc_id, asset_type, asset_id |
Section Navigation 🧭 (需 Marker blocks.json)
| Tool | 用途 | 參數 |
|---|---|---|
list_section_tree |
顯示完整 section hierarchy 樹狀結構 | doc_id: str |
get_section_detail |
取得特定 section 的詳細資訊 | doc_id: str, section_path: str |
get_section_blocks |
提取特定 section 的所有 blocks | doc_id: str, section_path: str |
search_sections |
搜尋 section 名稱 | doc_id: str, query: str |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 380 lines · 84 tokens per session scan A a8fa62970f9b
pdf-asset-extractor is a skill published in the GitHub repository u9401066/rootcause-mcp (0 stars, last pushed 6d ago), licensed Apache-2.0. It adds 84 tokens to every session and 3,261 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to pdf-asset-extractor, differing in 129 lines, and is treated as a copy.
Other skills, from other repositories
markitdown
Convert heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server.
azure-ai
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
doc-parse
A document parser that converts PDFs, PowerPoint files, spreadsheets, and Word files into structured Markdown with metadata and a confidence score.
edgeparse
Extract structured content from any PDF for AI agents, RAG pipelines, and Copilot Skills. Use this skill whenever the user wants to read, analyze, or reason about a PDF document; needs to feed document content to an LLM; mentions PDF extraction, parsing, or conversion; wants tables, headings, or bounding boxes from a…
ingest
Process unstructured documents into search-ready chunks. Use this skill when the user wants to process PDFs or unstructured documents into JSONL chunks using Docling. Activate even if the user says document processing, chunking, Docling, PDF processing, or chunk quality evaluation. For cloud-scale ingestion via OSIS…
opendataloader-pdf
A tool for extracting structured content from PDF files, such as text, tables, formulas, and scanned pages. It can produce Markdown, JSON with page positions, or HTML for use in search and AI document systems.