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Use to design a pipeline that vectorises support conversations for semantic search or retrieval, with the chunking, PII and deletion decisions made before anything is embedded. Trigger for "make our support conversations searchable", "build a vector index over tickets", "semantic search across transcripts", RAG over support data, chunking transcripts, or an embedding index that returns unhelpful matches.
cx-conversation-embedding-pipeline is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. # Vectorising support conversations Semantic search over support history is genuinely useful: finding the precedent for an unusual case, grounding an AI agent,. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding cx-conversation-embedding-pipeline to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
codex skill install rulebase-co/rulebase-skillsReview the upstream source before running this command.
Scan coverage: Install metadata and the indexed document body.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
# Vectorising support conversations Semantic search over support history is genuinely useful: finding the precedent for an unusual case, grounding an AI agent, or locating every co