conf-theme-clustering

conf-theme-clustering is an NLP, speech & audio ML skill for AI agents, published by lyndonkl in the claude repository on GitHub. Skillspace rates conf-theme-clustering C (74/100) and it passed the safety scan; the rating is based on static analysis only.

Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.

Grade

C

Quality

74 / 100

Safety

Passed

GitHub stars

133

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