This content originally appeared on DEV Community and was authored by Mohit Agnihotri
This is a submission for the AI Agents Challenge powered by n8n and Bright Data
What I Built
Viral-O-Meter for YouTube — an n8n + Bright Data agent that checks a keyword's viral potential in real time.
It pulls top YouTube results for a keyword via Bright Data’s Verified Node, normalizes metrics (views/likes/length), computes robust stats (median & p75 views, like rate, Shorts share, median lengths), and asks an AI Agent to deliver a GO/NO_GO verdict, recommended format (Shorts vs Long-form), ideal length band, content angles, and hooks.
Who it helps: creators & marketers who want a quick, data-driven green‑light before spending time on a video.
Demo
- Video walkthrough: https://youtu.be/vJgj8UrPAmw
- Live agent: https://dev-challenge-brightdata.app.n8n.cloud/form/trend-finder
n8n Workflow
Gist (workflow JSON): https://gist.github.com/mohitagnihotri/062a1cee13e30bcdeccba3aaa8895b61
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Key nodes:
- Bright Data (Verified Node): Data Collector (trigger collection & fetch snapshot), Web Unlocker when needed.
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Code (Normalize): converts raw fields to numeric (views/likes), derives
views_per_day, parses HH:MM:SS, classifies Shorts vs Long-form. - Code (Stats): computes medians/quantiles and seeds a suggested length band (±20% around top‑quartile median).
- Agent (AI): consumes the enriched JSON and returns structured recommendations.
- Report: send proper analysis report to Gmail.
Note: I included two drop-in Code nodes: (1) normalizer, (2) stats pre-compute. Paste them into the workflow, or pull from the Gist.
Technical Implementation
High-level flow
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Ingest with Bright Data
- Use Data Collector to collect YouTube search/results pages by keyword.
- Use Deliver Snapshot / Get Snapshot Content to fetch structured results.
- If regional blocks occur, enable Web Unlocker automatically in the Verified Node.
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Normalize (Code Node #1)
- Parse numeric strings like “58,613”, “1.2M/1.2K” → integers.
- Parse
video_length(seconds or HH:MM:SS) →length_seconds&length_hms. - Derive
views_per_day, computelike_rate_pct, detectpost_type.
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Stats (Code Node #2)
- Compute median / p75 for views, median like rate, Shorts share %.
- Compute median length (all) and median length among the top quartile by views.
- Create a recommended_seed with an anchor length (top‑quartile median) and a ±20% band; include a format_hint based on top performers.
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AI Agent
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System Prompt: “You are a YouTube content viability analyst …” (uses
benchmarks&recommended_seedif present; otherwise computes). - User Prompt: “Analyze the following dataset … { JSON.stringify($json, null, 2) }”
- Output: deterministic JSON with verdict, confidence, recommended format/length, angles, hooks, and checklist.
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System Prompt: “You are a YouTube content viability analyst …” (uses
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Outputs
- Post reports to Gmail.
Design choices
- No external assumptions: Agent reasons only over the provided dataset.
- Robustness: use medians and quantiles to resist outliers.
- Actionability: force the Agent to output clear ranges (seconds + mm:ss), angles, hooks, and checklist.
Bright Data Verified Node
- Endpoints used: Data Collector (trigger & deliver snapshots), Web Unlocker when geo or bot blocks are detected.
- Why Bright Data? Verified, production-ready node in n8n; handles real-time collection and anti-bot hurdles without brittle DIY scraping.
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Schema highlights: title, url, views, likes, video_length, date_posted, channel, post_type, plus derived fields (
views_per_day,like_rate_pct,length_seconds). - Cost control: run snapshots on-demand and cap concurrent collections; cache daily to reduce duplicate fetches.
Journey
What worked
- Verified Node made collection straightforward; Deliver Snapshot gave predictable JSON.
- Separating normalize and stats steps simplified prompt design and let the Agent stay lightweight.
Challenges
- Mixed units & locales: views “K/M” + comma separators → solved via a single parser.
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Shorts vs Long-form detection: use explicit
post_typewhen present; fallback to<60s. -
Recency bias: compute
views_per_dayto compare older vs newer videos fairly.
What I learned
- Scrapping without tools like BrightData is not possible as IP gets blocked, get 403 error etc.
- Framing the Agent’s output as strict JSON drives consistent downstream automation.
- Anchoring recommended length to the top‑quartile median yields practical targets that mirror what performs best.
This content originally appeared on DEV Community and was authored by Mohit Agnihotri
Mohit Agnihotri | Sciencx (2025-08-31T14:31:25+00:00) Viral-O-Meter for YouTube (Built with n8n + Bright Data). Retrieved from https://www.scien.cx/2025/08/31/viral-o-meter-for-youtube-built-with-n8n-bright-data/
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