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Identifies and ranks Key Opinion Leaders (KOLs) based on engagement metrics, active rate, and sentiment rather than just views.
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Based on SOC occupation classification
This skill should be used when the user wants to "create a new Python ADK sample", "scaffold a new Python sample recipe", "generate a new Python sample in contrib", "add a new Python sample to the adk-samples repository", or "create a Python adk sample". It utilizes an automated script to copy template files and resolve basic placeholders.
Scan an ADK recipe directory and generate a manifest.yaml for it based on the schema at .github/schemas/manifest-schema.json. Use when the user wants to create or generate a manifest.yaml for a recipe under core/ or contrib/.
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Performs a strict evaluation of a video asset using Google's official 'ABCD' framework (Attract, Brand, Connect, Direct) based on transcript and metadata.
Deconstructs high-performing or viral videos to extract actionable creative insights from metadata and transcript.
Provides a high-signal briefing on events in a specific location and timeframe, backed by primary video sources and transcripts.
| name | kol-discovery |
| description | Identifies and ranks Key Opinion Leaders (KOLs) based on engagement metrics, active rate, and sentiment rather than just views. |
Objective: Find and rank Key Opinion Leaders (KOLs) based on strict performance metrics rather than just view counts.
Execution Steps:
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.get_video_details and get_channel_details to fetch the underlying statistics for the top candidates.engagement_rate and active_rate using the calculate_engagement_metrics tool.analyze_sentiment_heuristic.match_score to rank them objectively.Next Actions: Once the list is presented, actively ask the user if they want to: