skills/notion-to-markdown/SKILL.md
Directly convert Notion pages to Obsidian-friendly Markdown using MCP fetch and local processing. Use this skill when you need to migrate specific Notion pages identified by URL or ID into the local Obsidian vault, ensuring LaTeX normalization, image localization, and property-to-frontmatter mapping.
npx skillsauth add jaimeparker/stable-jarvis notion-to-markdownInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill provides a high-level workflow for fetching live Notion pages and converting them into clean, localized Markdown files suitable for an Obsidian vault.
Unlike bulk migration from exports, this skill leverages the Notion MCP (notion-fetch) to retrieve page content directly and then uses the stable_jarvis.notion_to_obsidian toolkit to perform surgical processing.
Use notion-search to find the exact page ID or URL.
# Example search
notion-search query="Leveraging Skills from Unlabeled Prior Data"
Retrieve the raw Notion content and save it to a temporary JSON file.
# Fetch content via MCP
notion-fetch id="[PAGE_ID]" > temp_fetch.json
Run the migration script to convert the JSON content to localized Markdown.
python skills/notion-to-markdown/scripts/migrate.py temp_fetch.json [OUTPUT_DIR]
Inspect the output Markdown. Notion content can sometimes be "jammed" or have deep indentation that script-based normalization might miss. Use the replace tool to:
$$ blocks are correctly populated with recovered LaTeX.<span> tags).$ \dots $ and $ \dots $ to standard $...$ and $$...$$ syntax.assets/ folder and updating Markdown links.==text==).jarvis environment (conda activate jarvis).notion-fetch MCP Tool: Required to retrieve page data.stable_jarvis package: Must be installed in editable mode (pip install -e .) within the jarvis environment.httpx: Used for reliable image downloads.migrate.py script is run immediately after notion-fetch.notion-fetch tool.development
# Mao Semantic Search Search Mao Zedong Selected Works by conceptual meaning using vector embeddings. Builds a local embedding index over all 230 articles across 5 volumes, then performs cosine similarity search at query time. ## When to Use - User asks a thematic/conceptual question about Mao's works ("What did Mao say about guerrilla warfare?") - Keyword search over the .md files is insufficient - User wants to find passages related to a concept without knowing exact terminology - As a pre-
development
Use when a researcher is choosing, framing, refining, or stress-testing a research question, hypothesis, thesis topic, project idea, grant direction, paper angle, or stalled research direction.
research
精读文献。快速泛读请用paper-quick-read。
data-ai
泛读:快速概览Zotero库中的文献,单轮LLM生成摘要级Markdown报告,并上传为Zotero Note。深度精读请用paper-deep-reader。