tweet-rl-tracker

## What I Do Set up a Notion database to track tweet performance and enable a feedback loop for improving tweet quality over time. This is "poor man's reinforcement learning" - manually logging outcomes to discover what works. **NEW: Screenshot Capture** - Automatically capture screenshots from tweet links (including video frames at 10 seconds) using Chrome DevTools MCP. ## The RL Loop ``` 1. WRITE -> Draft tweet with a hypothesis (hook type, topic, etc.) 2. POST -> Publish to Twitter/X 3. LOG -> Record in Notion after 24-48hrs with metrics 4. SCORE -> Mark "Worked?" based on engagement rate 5. REVIEW -> Weekly: compare winners vs losers, extract patterns 6. REPEAT -> Apply learnings to step 1 ``` ## Database Schema ### Core Fields (Outcomes) | Property | Type | Purpose | | ----------- | -------- | ------------------------------------------ | | Tweet | title | The tweet text | | Likes | number | Primary engagement signal | | Impressions | number | Reach/views | | Score | formula | `Likes / Impressions * 100` (engagement %) | | Worked? | checkbox | Binary gut-check - was this a win? | ### Input Features (What You Controlled) | Property | Type | Options | | -------- | ------ | ----------------------------------------------------------------- | | Hook | select | Question, Bold Claim, Story, List, How-To, Contrarian, Data/Stats | | Topic | select | (customize to your niche) | | Posted | date | When you posted | ### Optional Extensions | Property | Type | Purpose | | -------- | --------- | ---------------------- | | Replies | number | Conversation signal | | Retweets | number | Amplification signal | | Link | url | Link to original tweet | | Notes | rich_text | Why did it work/fail? | ## Setup Instructions ### Step 1: Create the Page and Database Use the Notion MCP tools to create: ``` notion_notion-create-pages with: - title: "Tweet Lab" (or your preferred name) - content: Brief intro about the system Then inside that page, create a database with: notion_notion-create-database with the schema above ``` ### Step 2: Example Notion Tool Calls **Create the container page:** ```json { "pages": [ { "properties": { "title": "Tweet Lab" }, "content": "## Tweet Performance Tracker\n\nA simple system to track what works and improve over time.\n\n### The Loop\n1. Post tweet\n2. Log metrics after 24-48hrs\n3. Mark if it \"Worked\"\n4. Weekly: review patterns\n\n<database>Tweets</database>" } ] } ``` **Create the database (after getting page_id):** ```json { "parent": { "page_id": "<page-id-from-above>" }, "title": [{ "type": "text", "text": { "content": "Tweets" } }], "properties": { "Tweet": { "type": "title", "title": {} }, "Likes": { "type": "number", "number": { "format": "number" } }, "Impressions": { "type": "number", "number": { "format": "number" } }, "Score": { "type": "formula", "formula": { "expression": "if(prop(\"Impressions\") > 0, prop(\"Likes\") / prop(\"Impressions\") * 100, 0)" } }, "Worked?": { "type": "checkbox", "checkbox": {} }, "Hook": { "type": "select", "select": { "options": [ { "name": "Question", "color": "blue" }, { "name": "Bold Claim", "color": "red" }, { "name": "Story", "color": "green" }, { "name": "List", "color": "yellow" }, { "name": "How-To", "color": "purple" }, { "name": "Contrarian", "color": "orange" }, { "name": "Data/Stats", "color": "pink" } ] } }, "Topic": { "type": "select", "select": { "options": [] } }, "Posted": { "type": "date", "date": {} } } } ``` ## Using the System ### Daily: Log Tweets After 24-48 hours, add a row: - Copy tweet text - Pull Likes and Impressions from Twitter analytics - Select the Hook type you used - Select or create a Topic tag ### Weekly: Review Session 1. **Sort by Score** (descending) - what performed best? 2. **Filter by "Worked?" = true** - what patterns emerge? 3. **Group by Hook** - which hook types win? 4. **Group by Topic** - which topics resonate? ### Monthly: Form Hypotheses Based on patterns, create hypotheses to test: - "Questions about [topic] get 2x engagement" - "Bold claims underperform for my audience" - "Threads outperform single tweets" Then deliberately test these in the next month. ## Key Insights ### What Makes This Work 1. **Track inputs, not just outputs** - Without knowing what you tried (hook, topic), you can't learn what caused success 2. **Binary "Worked?" is powerful** - Forces a clear decision, easier to analyze than continuous scores 3. **Review regularly** - Data without reflection is useless 4. **Test hypotheses deliberately** - Don't just observe, experiment ### Common Pitfalls - Logging inconsistently (do it same time each day/week) - Not tracking input features (hook, topic) - Never reviewing the data - Optimizing for wrong metric (likes vs. replies vs. followers) ## Customization ### Different Goals = Different Metrics | Goal | Primary Metric | Formula | | ---------- | ------------------------------- | ------------------ | | Virality | Retweets / Impressions | Amplification rate | | Engagement | (Likes + Replies) / Impressions | Interaction rate | | Growth | Profile clicks / Impressions | Curiosity rate | | Community | Replies / Impressions | Conversation rate | ### Add Your Topics Update the Topic select options based on your niche. Examples: - Tech founder: Product, Fundraising, Hiring, Lessons, Industry - Creator: Process, Tools, Mindset, Results, Behind-the-scenes ## Troubleshooting ### "I forget to log tweets" - Set a daily/weekly calendar reminder - Add a "Logged?" checkbox to your posting workflow - Batch log once per week instead of daily ### "I don't know what hook I used" - Decide BEFORE posting, not after - Add hook type to your tweet drafting process - When in doubt, pick the closest match ### "My Score is always low" - Engagement rates are typically 1-5% - this is normal - Compare relative performance, not absolute numbers - Consider using a different base metric for "Worked?" --- ## Screenshot Capture with Chrome MCP ### Prerequisites - Chrome DevTools MCP configured in your MCP client - Logged into Twitter/X in the Chrome profile ### Database Field The Tweets database includes a `Screenshot` field (type: files) to store captured images. **Data Source ID**: `a6913492-bfdc-4f6a-b539-ea98b57a2738` ### Workflow: Capture Tweet Screenshot Given a tweet URL like `https://x.com/username/status/1234567890`: #### Step 1: Navigate to Tweet ```javascript // Open new page with the tweet new_page({ url: 'https://x.com/username/status/1234567890' }); // Wait for tweet to load wait_for({ text: 'Reply' }); // or wait for specific tweet content ``` #### Step 2: Take Screenshot of Tweet ```javascript // Take full page screenshot take_screenshot({ fullPage: false }); // Or screenshot specific element (the tweet article) take_snapshot(); // Get element uids first take_screenshot({ uid: 'tweet-article-uid' }); ``` #### Step 3: For Video Tweets - Capture Frame at 10 Seconds ```javascript // 1. Find and click the video to start playing take_snapshot(); click({ uid: 'video-player-uid' }); // 2. Wait 10 seconds for video to play // (Use evaluate_script to seek if possible) evaluate_script({ function: `() => { const video = document.querySelector('video'); if (video) { video.currentTime = 10; video.pause(); return { success: true, duration: video.duration }; } return { success: false }; }`, }); // 3. Take screenshot of the video frame take_screenshot({ fullPage: false }); ``` #### Step 4: Extract Tweet Metadata ```javascript evaluate_script({ function: `() => { // Extract tweet text const tweetText = document.querySelector('[data-testid="tweetText"]')?.textContent || ''; // Extract author const author = document.querySelector('[data-testid="User-Name"]')?.textContent || ''; // Extract metrics (likes, retweets, etc.) const metrics = {}; document.querySelectorAll('[data-testid="like"], [data-testid="retweet"]').forEach(el => { const label = el.getAttribute('aria-label') || ''; if (label.includes('like')) metrics.likes = parseInt(label) || 0; if (label.includes('repost')) metrics.retweets = parseInt(label) || 0; }); // Check if video exists const hasVideo = !!document.querySelector('video'); const hasImage = !!document.querySelector('[data-testid="tweetPhoto"]'); return { text: tweetText, author, metrics, mediaType: hasVideo ? 'Video' : hasImage ? 'Image' : 'None' }; }`, }); ``` ### Complete Example: Add Tweet with Screenshot ``` User: Add this tweet to the tracker: https://x.com/unvalley_/status/2005899617775542298 Agent workflow: 1. new_page({ url: 'https://x.com/unvalley_/status/2005899617775542298' }) 2. wait_for({ text: 'Reply' }) 3. take_snapshot() - to see page structure 4. evaluate_script() - extract tweet text, author, media type 5. If video: seek to 10s and pause 6. take_screenshot() - capture the visual 7. Save screenshot to local file (screenshot is returned as base64) 8. Create Notion page with extracted data + screenshot file ``` ### Saving Screenshot to Notion **Important**: Notion's files property requires external URLs. The workflow is: 1. Take screenshot with Chrome MCP (returns base64) 2. Save to a publicly accessible location (e.g., upload to S3, Cloudinary, or use a temp file host) 3. Add the URL to the Notion page's Screenshot field ```javascript // After taking screenshot, you'll get base64 data // Save it locally first: const fs = require('fs'); const screenshotPath = `/tmp/tweet-${Date.now()}.png`; fs.writeFileSync(screenshotPath, Buffer.from(base64Data, 'base64')); // Then upload to your preferred hosting and get URL // Finally, create Notion page with the screenshot URL ``` ### Quick Reference: Chrome MCP Tools for Tweet Capture | Tool | Purpose |