Kol Discovery

Skill: KOL Discovery Workflow

Objective: Find and rank Key Opinion Leaders (KOLs) based on strict performance metrics rather than just view counts.

Execution Steps:

1. Search: Use search_youtube to find videos about the topic. If the user specifies a time frame (e.g., "last month"), use get_date_range first to get the published_after date string.
2. Data Gathering: Use get_video_details and get_channel_details to fetch the underlying statistics for the top candidates.
3. Evaluation:
   • For each candidate, calculate engagement_rate and active_rate using the calculate_engagement_metrics tool.
   • If needed, fetch comments and run analyze_sentiment_heuristic.
   • Calculate the match_score to rank them objectively.
4. Reporting: Present the top KOLs in a clear table. Explain why they were chosen (e.g., "High Engagement of 12%, despite lower subscriber count"). Drop clickbait videos that have high views but terrible engagement metrics, and explicitly tell the user you filtered them out to save their time.

Next Actions: Once the list is presented, actively ask the user if they want to:

• See a visual chart of the engagement metrics.
• Generate and publish a final HTML report.
• Do a deep-dive transcript reading on any specific video from the list.

Source: google/adk-samples → python/agents/youtube-analyst/youtube_analyst/skills/kol-discovery/SKILL.md