wechat-to-notion

# wechat-to-notion Save a WeChat article to Notion in three steps: fetch → analyze → save. ## Configuration Check (Do This First) Check if the Notion API key is configured: ```bash echo ${NOTION_API_KEY:0:8}... ``` **If missing**, tell the user: > You haven't configured a Notion API key yet: > 1. Go to https://notion.so/my-integrations → **+ New integration** → copy the key (starts with `ntn_`) > 2. Open your Notion database → **...** → **Connect to** → select your integration > 3. Set the key in your OpenClaw config — **do not paste it into chat**: > ``` > openclaw config set skills.entries.wechat-to-notion.NOTION_API_KEY "ntn_xxx" > ``` > OpenClaw will inject it as `NOTION_API_KEY` automatically. ⚠️ Never ask the user to send the API key as a chat message — it will be exposed in conversation logs. ## Setup (One-time) Ask if the user has an existing Notion database. If yes, use it directly. If no, ask for a parent page URL and create one: ```bash curl -s -X POST https://api.notion.com/v1/databases \ -H "Authorization: Bearer $NOTION_API_KEY" \ -H "Notion-Version: 2025-09-03" \ -H "Content-Type: application/json" \ -d '{ "parent": {"type": "page_id", "page_id": "<parent_page_id>"}, "title": [{"type": "text", "text": {"content": "WeChat Articles"}}], "properties": { "Title": {"title": {}}, "URL": {"url": {}}, "Read Time": {"date": {}}, "Rating": {"select": {"options": [ {"name": "⭐", "color": "gray"}, {"name": "⭐⭐", "color": "gray"}, {"name": "⭐⭐⭐", "color": "yellow"}, {"name": "⭐⭐⭐⭐", "color": "orange"}, {"name": "⭐⭐⭐⭐⭐", "color": "red"} ]}}, "Tags": {"multi_select": {}}, "Notes": {"rich_text": {}} } }' ``` > Match field names to the user's language (e.g. Chinese users get Chinese field names). ## Workflow ### Step 1: Fetch article ```bash python3 {skillDir}/scripts/fetch_wechat.py <wechat_url> > /tmp/wx_article.json ``` ### Step 2: Analyze (inline — reason directly, no subprocess) Use the `read` tool to load `/tmp/wx_article.json`. Read the `title` and text content from `blocks`, then produce two outputs by reasoning directly: **Keywords (3–5):** - Only extract core concepts: the specific technologies, products, or domain terms that define what this article is actually about - Omit generic/broad terms (e.g. "AI", "efficiency", "productivity", "tools", "development") - Comma-separated, preserve original casing **Rating (1–5 stars):** Based on readability and value, give a star rating: - ⭐ (1): waste of time — clickbait, no substance, or unreadable - ⭐⭐ (2): below average — padded, shallow, or poorly organized - ⭐⭐⭐ (3): decent — has useful content but nothing exceptional - ⭐⭐⭐⭐ (4): good — well-written, actionable, worth bookmarking - ⭐⭐⭐⭐⭐ (5): excellent — insightful, well-structured, a must-read in its domain 3 stars and above automatically get a "Featured" tag. **Comment (1 sentence, written in the user's language):** Evaluate the article's **readability** and **value**, not summarize its content. Focus on: - Is it well-structured and easy to follow, or rambling and padded? - Does it deliver actionable insight, or is it surface-level fluff? - Who would actually benefit from reading this? - Example: "Well-structured, flows from theory to hands-on smoothly — ideal for devs wanting to get started with MCP (refreshingly no filler)" - Example: "Clickbait title, buries the lead under three screens of preamble — the core point could fit in a single tweet" - Keep it under 35 words. Be direct — praise or criticize with specifics, no hedging. ### Step 3: Save to Notion ```bash python3 {skillDir}/scripts/save_to_notion.py \ /tmp/wx_article.json \ <notion_db_url> \ <wechat_url> \ <read_time_iso8601+08:00> \ "<kw1>,<kw2>,<kw3>" \ "<comment>" \ <rating> ``` - `read_time`: current time in the user's local timezone as ISO 8601 with offset, e.g. `2026-03-12T14:00:00+08:00` - `keywords`: comma-separated string - `comment`: the single-sentence comment from Step 2 - `rating`: integer 1–5 (star rating); 3+ automatically adds "Featured" to tags The script auto-detects field names from the database schema by type (`title`, `url`, `date`, `select`, `multi_select`), writes all content blocks in batches of 100, and posts the comment to the Notion Comments panel. ## Notes - Cover image: extracted from `og:image` meta tag, inserted as the first block - Rich text (bold/italic), code blocks, and lists are preserved by `fetch_wechat.py` - Read time defaults to current system time with local UTC offset if omitted - The comment appears in the Notion Comments panel, not in the page body - Field names are language-agnostic — the script maps by type, not by name