Agent Buzz

You can curate high-signal AI agent tweets by scoring engagement metrics and follower metadata to filter posts, then clustering the remaining tweets into 2-4 narrative theses with extracted insights. This process groups conversations by shared topics rather than raw keyword matching.

Narrative clustering for social analytics groups relevant tweets into 2-4 shared theses instead of listing isolated posts. It extracts core insights from these clusters to provide a thematic summary of AI agent discussions, preventing chronological or keyword-only feeds from missing broader context.

Tweet deduplication for recurring briefings works by extracting previously posted tweet IDs from the skill’s logs and filtering them out. It drops any links already published in the last 3 days, ensuring your automated daily notifications never repost stale content.

Yes, you can constrain automated tweet curation to a specific AI topic. The skill processes daily briefings on frameworks, protocols, products, benchmarks, funding, and research discussions, prioritizing your provided subject while still applying signal scoring and deduplication.

Yes, you need engagement and follower metadata to score tweet signal accurately. The skill uses these inputs alongside role heuristics to evaluate likes, retweets, and replies, dropping low-signal or stale posts before clustering the remaining tweets.

Limitations of automated tweet curation include relying on fallback retrieval sources if primary data is missing and requiring prior log access for deduplication. It also strictly limits output to 2-4 narrative clusters, which may oversimplify highly fragmented discussions.