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Extracts the true audience mood and key feedback by analyzing comment sentiment and keyword frequency.
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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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Provides a high-signal briefing on events in a specific location and timeframe, backed by primary video sources and transcripts.
| name | sentiment-analysis |
| description | Extracts the true audience mood and key feedback by analyzing comment sentiment and keyword frequency. |
Objective: Understand the true audience reaction to a video, product, or creator by analyzing comment sentiment.
Execution Steps:
get_video_comments to pull the top 20-50 comments for the target video.analyze_sentiment_heuristic to gauge the overall mood (Positive/Neutral/Negative).Next Actions: Ask the user if they would like to visualize this sentiment data or publish it as a report.