content-pipeline

name content-pipeline description Full-autopilot trend discovery, deep research, and social publishing pipeline. Uses trend-pulse (20 sources), cf-browser (headless Chrome), and notebooklm (research + artifacts) MCP servers. Generates algorithm-optimized content based on Meta's 7 patent-based ranking algorithms. Use when user mentions trending topics, content creation, social media publishing, trend analysis, research pipeline, viral content, content scoring, or Threads posting. user_invocable true Content Pipeline AI-powered content pipeline: trend discovery -> deep research -> algorithm-optimized writing -> social publishing. MCP Servers Required Install via uvx (one-time, auto-cached): uvx --from 'trend-pulse[mcp]' trend-pulse-server uvx --from cf-browser-mcp cf-browser-mcp uvx --from notebooklm-skill notebooklm-mcp MCP Tools Reference trend-pulse (12 tools) Trend Data: get_trending(sources, geo, count) : Query ALL 20 free sources. sources="" for all. geo: "TW"/"US"/"JP"/"". count: 20. Sources: google_trends, hackernews, mastodon, bluesky, wikipedia, github, pypi, google_news, lobsters, devto, npm, reddit, coingecko, dockerhub, stackoverflow, producthunt, arxiv, lemmy, dcard, ptt search_trends(query, sources, geo) : Cross-source keyword search. list_sources() : List all sources. take_snapshot(sources, geo, count) : Save snapshot for velocity tracking. get_trend_history(keyword, days, source) : Historical data with direction. Content Guide: get_content_brief(topic) : Writing brief with hook examples, patent strategies, CTA. get_scoring_guide() : 5-dimension patent scoring. Score >= 70 required. get_platform_specs(platform) : Platform specs (char limits, algorithm signals, posting times). get_review_checklist() : Quality review checklist (7 checks). get_reel_guide() : Reels script guide (3 styles). Search: search_threads_posts(query) : Search Threads posts by heat score. cf-browser (10 tools) Headless Chrome via Cloudflare Browser Rendering. Use instead of WebFetch for JS-rendered pages. browser_markdown(url) : Clean Markdown. Most used for deep research. browser_content(url) : Full rendered HTML. browser_screenshot(url) : Full page screenshot (PNG). browser_pdf(url) : Generate PDF. browser_scrape(url, selector) : CSS selector extraction. browser_json(url, schema) : AI-driven structured data extraction. browser_links(url) : Extract all hyperlinks. browser_a11y(url) : Accessibility tree (low token cost). browser_crawl(url) : Async multi-page crawl. browser_crawl_status(id) : Check crawl progress. notebooklm (13 tools) Deep research + 9 downloadable artifact types (podcast, slides, report, quiz, flashcards, mindmap, datasheet, study_guide). ⚠️ infographic download is unreliable — use slides instead. Management: create_notebook, list_notebooks, delete_notebook Research: add_source, ask, summarize, list_sources, research(mode="fast"|"thorough") Artifacts: generate_artifact(type, lang?), download_artifact(type, output_path) Pipelines: research_pipeline(sources, questions, output_format), trend_research(geo?, count?, platform?) Video Synthesis (slides + podcast -> MP4): Generate slides (PDF) + audio (podcast M4A) — sequentially Download both pdftoppm -png -r 300 slides.pdf slides_page ffmpeg -framerate 1/$per_slide -pattern_type glob -i 'slides_page-*.png' -i audio.mp3 -c:v libx264 -pix_fmt yuv420p -c:a aac -shortest output.mp4 Visual Content Generation Use NotebookLM to generate all visual content. It produces professional-quality slides and mind maps — far better than any alternative. ⛔ 嚴格禁止 ：不得使用 Pillow/PIL、ImageMagick、Python 程式碼或 HTML+Playwright 生成圖卡。 所有圖卡一律透過 NotebookLM nlm_generate(type="slides") 生成。 ⚠️ Do NOT use infographic (download unreliable). Use slides for all visual content. ⚠️ NLM slides 多頁時 → 必須全部上傳 → 用 carousel_urls 發文（不要只發一張）。 Image Cards & Slides (圖卡 / 簡報) Workflow: Create a NotebookLM notebook with the content as a text source, then generate the visual artifact. 1. create_notebook(title="Post Visual", text_sources=["<post content + key data points>"]) 2. generate_artifact(name_or_id, "slides", lang="zh-TW") → PDF slides (best for cards) 3. download_artifact(name_or_id, "slides", "downloads/card.pdf") Choose artifact type by use case: Need Artifact Type Output Single image card slides (1 slide) PDF → export as image Multi-slide carousel slides (N slides) PDF → split per page Data visualization slides infographic ⚠️ download unreliable — use slides Topic overview mindmap Mind map diagram Detailed report report Formatted document Study material flashcards / study_guide Learning cards Carousel Posts (輪播貼文) For multi-image carousel (Threads supports 2-20 images): Create notebook with content organized as numbered sections (one per slide) generate_artifact(name_or_id, "slides") → multi-page PDF Download and split: pdftoppm -png -r 300 slides.pdf slide Upload EVERY image — call upload_image for EACH slide PNG: upload_image(file_path="downloads/slide-1.png") → url1 upload_image(file_path="downloads/slide-2.png") → url2 upload_image(file_path="downloads/slide-3.png") → url3 ...repeat for ALL slides Publish as carousel — pass ALL URLs to publish_to_threads : publish_to_threads(text="...", carousel_urls=[url1, url2, url3, ...]) Do NOT use image parameter — use carousel_urls when there are 2+ images. ⚠️ CRITICAL : When slides have multiple pages, you MUST use carousel_urls (not image ). Single image parameter only posts the first image. carousel_urls posts ALL images as a swipeable carousel. Video (影片) Combine slides + podcast audio into MP4: generate_artifact(name_or_id, "slides") → PDF generate_artifact(name_or_id, "podcast") → M4A audio (run sequentially, NOT parallel) Download both artifacts pdftoppm -png -r 300 slides.pdf slides_page ffmpeg -framerate 1/$per_slide -pattern_type glob -i 'slides_page-*.png' -i audio.mp3 -c:v libx264 -pix_fmt yuv420p -c:a aac -shortest output.mp4 Tips for Better Visuals Language : Always pass lang="zh-TW" for Traditional Chinese content Rich text sources : The more context you feed into the notebook, the better the visual output One topic per notebook : Don't mix unrelated topics — create separate notebooks Add URLs as sources : add_source(name_or_id, url="...") for reference material — NotebookLM will incorporate key data into visuals Mandatory Rules 0. Workspace Containment All files MUST be saved within the session workspace directory. Use relative paths like downloads/card.pdf . NEVER use ~/Downloads, ~/Desktop, or any absolute path outside the workspace. 1. Read Original Sources NEVER write content based on titles/metadata alone. Single topic: read >= 1 primary source via browser_markdown(url) Controversial: read >= 2 sources (both sides) Data claims: find original data source 2. Timeline Verification Every fact must have a verified timestamp. Discard anything > 48 hours old. Source age Allowed Forbidden Today "today" "just now" - 1-3 days "recently" "the other day" "just" "latest" 4-7 days "last week" "this week" "just" "yesterday" 8-30 days "this month" "last week" "just" >30 days "earlier" "this year" any freshness words 3. Use ALL Sources get_trending with sources="" to query ALL 20 sources. Do NOT filter unless user explicitly asks. Meta Patent-Based Scoring (5 Dimensions) # Dimension Weight Check 1 Hook Power 25% First line: number or contrast, 10-45 chars 2 Engagement Trigger 25% CTA anyone can answer, direct "you" address 3 Conversation Durability 20% Has contrast/both sides, creates discussion 4 Velocity Potential 15% Timely, 50-300 chars, urgency language 5 Format Score 15% Mobile-scannable, line breaks, no text walls Quality Gates (ALL must pass before publishing): Overall Score >= 70 Conversation Durability >= 55 Hook: 10-45 chars with number or contrast CTA: clear question or poll Timeline: all time words verified Source: every claim traceable No AI filler words Pipeline Workflow Discover : get_trending(sources="", geo, count=20) Read Source : browser_markdown(url) — MANDATORY Verify Timeline : Check dates, discard stale Research : browser_markdown + WebSearch, 2-3 sources min Brief : get_content_brief(topic) Create : Write -> patent check (5 dimensions) Score : get_scoring_guide — self-score (>= 70) Review : get_review_checklist — final check NotebookLM (optional): create notebook -> generate artifacts Publish : provide ready-to-post content or use publish tool