# News Digest Video Agent > Create professional multi-source news digest videos. Given a topic, autonomously finds YouTube news clips, tweets, and articles, then assembles a narrated video with voiceovers, background music, and broadcast-style text overlays using VideoDB. - Skill: `video-db/news-digest-video-agent` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add video-db/news-digest-video-agent` - Raw SKILL.md: https://api.skillmd.com/api/skills/video-db/news-digest-video-agent/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: video-db (https://skillmd.com/u/video-db) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/video-db/news-digest-video-agent --- # News Digest Video Agent Skill Use this skill when the user wants to: - Create a news digest video on any topic - Turn a breaking news story into a multi-perspective video summary - Combine YouTube clips, tweets, and articles into a professional broadcast-style video ## Setup (run once) Before using this skill, ensure these dependencies are installed: ### 1. VideoDB skill & SDK ```bash # Install VideoDB skill (provides video upload, indexing, search, TTS, timeline editing) npx skills add video-db/skills # Install Python SDK pip install videodb python-dotenv ``` Set your API key (get one free at https://console.videodb.io): ```bash export VIDEO_DB_API_KEY=your_key_here ``` ### 2. Browser use skill ```bash # Install browser-use skill (provides screenshot, scroll recording, web page capture) mkdir -p ~/.claude/skills/browser-use curl -o ~/.claude/skills/browser-use/SKILL.md \ https://raw.githubusercontent.com/browser-use/browser-use/main/skills/browser-use/SKILL.md ``` Follow the browser-use SKILL.md instructions for any additional setup (e.g., playwright install). ### 3. Verify setup ```python from dotenv import load_dotenv load_dotenv(".env") import videodb conn = videodb.connect() print("VideoDB connected:", conn) ``` ## Required tools / capabilities **Strictly required:** - **VideoDB skill** — video upload, indexing, search, TTS, timeline editing - **Browser use skill** — ALL internet research, searching, screenshots, scroll recordings - **File read/write** — manage registry, scripts, and output files - **Bash / Python** — run the build script ## CRITICAL: No Hallucinated Data **Every URL, every fact, every piece of content MUST come from actual browser navigation.** - You MUST use browser-use to visit real websites and find real URLs - You MUST NOT generate YouTube URLs from memory — go to youtube.com and search - You MUST NOT create fake tweet images — go to Twitter/X and screenshot real tweets - You MUST NOT write research from LLM knowledge alone — browse real articles first - If browser-use is not working, STOP and tell the user. Do NOT proceed with made-up data. **Browser-use is your internet.** It handles both discovery (searching Google, YouTube, Twitter) AND capture (screenshots, scroll recordings). There is no other search tool. ## Video Structure (fixed, deterministic) Every news digest follows the same structure: ``` Title card (4s) → Intro voiceover → News clip 1 (hook + clip) → News clip 2 (hook + clip) → News clip 3 (hook + clip) → Tweet transition → Tweet 1 → Tweet 2 → Tweet 3 → Article transition → Article 1 → Article 2 → Outro (5s) Total: ~3-4 minutes (varies based on generated voiceover lengths) ``` **Note:** Only the title card (4s) and outro (5s) have fixed durations. Everything else is dynamically sized based on the actual voiceover durations returned by VideoDB TTS. The `build_video.py` template reads exact durations from `registry.json` — nothing is hardcoded. ## Inputs The only required input is: - **topic** — e.g., "iran war", "claude source code leak", "us tariffs china" Optional: - Custom background image (default: shipped `assets/news-bg.jpg`) - Custom background music (default: shipped `assets/news-music.mp3`) ## Workflow ### Step 1: Research the topic (USE BROWSER-USE) Use browser-use to actually browse the internet and gather real information: 1. Open Google → search ` news coverage 2024/2025/2026` 2. Visit 2-3 top results from trusted news sites 3. Read the actual article content 4. Extract key facts: what happened, who's involved, timeline, impact **Do NOT write research from LLM memory.** Every fact in `research.md` must trace back to a real URL you visited. Save to `outputs//data/research.md` — include source URLs for every claim. ### Step 2: Find 3 YouTube news videos (USE BROWSER-USE) Use browser-use to navigate to YouTube and find real videos: 1. Open `youtube.com` → search ` BBC News` 2. Click on a real result → copy the URL from the browser 3. Repeat for 2 more channels **Preferred YouTube channels to search:** - Al Jazeera English - BBC News - CNN - Reuters - CNBC - DW News - Sky News - PBS NewsHour **Search strategy:** Go to youtube.com and search for each channel + topic. Do NOT construct URLs from memory. **Criteria:** - 3 different news sources for diverse perspectives - Recent and relevant to the topic - 2-15 minutes long, English language - Each should present a distinct "take" or angle - **Every URL must come from actually visiting youtube.com** — not from LLM knowledge Save URLs to `outputs//data/videos.json` ### Step 3: Process YouTube videos with VideoDB For each of the 3 videos: 1. Upload to VideoDB: `coll.upload(url=youtube_url)` 2. Index spoken words: `video.index_spoken_words(force=True)` 3. Semantic search for the best ~25 second clip: `video.search(query)` 4. Select a clip that is self-contained, ends at a natural sentence, and shows the channel's perspective Update `registry.json` with video IDs, clip start times, and durations. ### Step 4: Capture 3 tweet screenshots (USE BROWSER-USE) Use browser-use to visit X.com profiles of key figures and find real tweets: 1. Visit public profiles directly: `x.com/` (works without login in headless mode) 2. Find 3 tweets from notable people with different perspectives 3. Screenshot each tweet via the embed endpoint: `platform.twitter.com/embed/Tweet.html?id=` **Note:** X.com **search** (`x.com/search?q=...`) requires login and won't work. Stick to visiting profiles directly. See `AGENTS.md` Step 4 for the full workflow. **Do NOT create fake/placeholder tweet images.** Every screenshot must be of a real tweet from a real browser session. Save to `outputs//assets/tweets/tweet_1.png`, `tweet_2.png`, `tweet_3.png` Upload each to VideoDB and update `data/registry.json`. ### Step 5: Capture 2 article assets (USE BROWSER-USE) Use browser-use to find and capture real articles: 1. Open Google → search ` site:apnews.com` (or other preferred sites) 2. Visit the actual article in the browser 3. Screenshot the key section (chart, data, key quote) 4. Record a scroll video (~8-13 seconds) by scrolling through the article **Preferred article sources (search these first):** - AP News (apnews.com) - Council on Foreign Relations (cfr.org) - Reuters (reuters.com) - BBC News (bbc.com/news) - The Guardian (theguardian.com) - NPR (npr.org) **Do NOT create fake scroll videos with ffmpeg from static images.** The scroll must be a real browser recording of a real article. **Criteria:** - Article 1: economic/domestic impact angle - Article 2: historical context / geopolitical analysis angle Save to `outputs//assets/articles/` Update `data/registry.json`. ### Step 6: Write scripts & generate voiceovers Write **11 voiceover scripts** based on research and collected sources: | Script | Guideline | Content | |--------|-----------|---------| | intro | 2-3 sentences | Quick hook — what happened, why it matters | | hook_1 | 1-2 sentences | Introduce news source 1, what they report | | hook_2 | 1-2 sentences | Introduce news source 2, what they report | | hook_3 | 1-2 sentences | Introduce news source 3, what they report | | tweet_transition | 1-2 sentences | Bridge to social media reactions | | tweet_1 | 2-3 sentences | Narrate tweet 1 content and context | | tweet_2 | 2-3 sentences | Narrate tweet 2 content and context | | tweet_3 | 2-3 sentences | Narrate tweet 3 content and context | | article_transition | 1 sentence | Bridge to deep analysis section | | article_1 | 3-4 sentences | Summarize article 1 key findings | | article_2 | 3-4 sentences | Summarize article 2 key findings | **Tone:** Professional broadcast news narrator. Concise, factual, no speculation. **Duration note:** Do NOT target specific second counts. Write natural scripts following the sentence guidelines above. Generate via TTS, then record the **exact duration** returned by `audio.length` into `registry.json`. The build template handles all timing dynamically. Generate each using VideoDB TTS: `coll.generate_voice(text=script, voice_name="Default")` Save scripts to `data/scripts.json`. Update `data/registry.json` with audio IDs and **exact durations** (from `audio.length`). ### Step 7: Build the video Run the build template: ```bash python templates/build_video.py outputs//data/registry.json ``` This assembles the 5-track timeline and generates the final stream URL. Output saved to `outputs//output/output.json`. ## Per-run folder structure All runs go inside `outputs/` within the skill directory. **Never** create run folders at the skill root. **Never** place loose files at the topic folder root — everything goes in a subfolder. ``` agentic-video-creator/news-digest/ ├── SKILL.md ├── AGENTS.md ├── README.md ├── assets/ # Shipped defaults (bg, music) ├── templates/ └── outputs/ # ALL runs go here └── / # e.g., "epstein-files" │ ├── assets/ # Physical files (viewable media) │ ├── tweets/ # Tweet screenshot PNGs │ │ ├── tweet_1.png │ │ ├── tweet_2.png │ │ └── tweet_3.png │ └── articles/ # Article scroll videos + screenshots │ ├── article_1_scroll.mp4 │ ├── article_1_screenshot.png │ ├── article_2_scroll.mp4 │ └── article_2_screenshot.png │ ├── data/ # Digital metadata (IDs, scripts, research) │ ├── research.md # Topic research brief │ ├── scripts.json # All 11 voiceover scripts │ ├── registry.json # All VideoDB asset IDs + durations │ └── videos.json # YouTube URLs + clip metadata │ ├── _build/ # Intermediate/working files (agent scratch) │ └── ... # Upload results, processing logs, etc. │ └── output/ # Final deliverables └── output.json # Stream URL, player URL, duration ``` **Rules:** - `assets/` = physical media files you can open and preview - `data/` = metadata, IDs, scripts — everything VideoDB and the build template need - `_build/` = intermediate files created during processing (prefixed with `_` to signal "ignore") - `output/` = the final video URL and metadata - **Zero files at the topic root** — everything in a subfolder ## Shipped assets Default background and music are in `assets/`: - `assets/news-bg.jpg` — Professional dark news background - `assets/news-music.mp3` — News-style background music (125s, looped at 15% volume) Users can replace these with their own files. Upload to VideoDB and update registry. ## Deliverables A successful run produces: - `output/output.json` with playable stream URL - All physical media preserved in `assets/` - Full `data/registry.json` for reproducibility ## Reference See `outputs/` folder for complete agent-generated examples with full registry structure and asset organization. **Example output:** [Iran War 2026 Video](https://player.videodb.io/watch?v=https://play.videodb.io/v1/43570285-1d6e-4548-86e6-294201d2418f.m3u8) ## Reference docs - `README.md` — Overview, setup, and usage guide - `AGENTS.md` — Detailed agent workflow, error handling, quality criteria - `templates/build_video.py` — Timeline builder (reads registry.json)