Agent skill · software engineering · jeremylongshore
podium-rag-context-bridge
Bridge a live Podium call transcript or webchat turn to an LLM by fetching relevant
Why this skill is useful
Adds a complex RAG pipeline with executable scripts for real-time context retrieval and reranking that fundamentally enhances LLM capabilities.
What it needs
Requires bash installed locally. Requires podium account access. About 10k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Podium RAG Context Bridge Overview Take a live transcript chunk (or webchat turn) and emit a structured RAG context bundle the calling LLM can drop into its prompt. This is not a chatbot. This is the substrate that sits between Podium's transcription stream and whatever LLM your agent loop is using — fetching the right historical context, fast enough to matter, in a shape the model can actually consume. The substrate combines two retrieval surfaces: a vector store of past Podium conversations (embedded at ingest by podium-conversation-history-export) and a live Podium API lookup for the contact's fresh state (phone, opt-out, location, last-seen). The two surfaces answer different questions — vectors answer "what did this person ever say about this topic," the live API answers "is the phone number we're about to dial actually still their phone number." A naive RAG pipeline collapses them and ships drift to the model. The six production failures this skill prevents: 1. Relevance scoring picks wrong historical context — naive cosine similarity over top-K=5 chunks surfaces the five most similar embeddings; on real Podium corpora most of those are off-topic boilerplate ("thanks for reaching out", "have a great day"). The model sees noise, generates a generic reply, and the operator loses the customer. Fix: cross-encoder reranking + per-contact filtering BEFORE the model sees anything. 2. Vector store stale vs live Podium data drift — the contact updated their phone yesterday; today the vector store still embeds the old number. The model answers "I'll call you back at (555) 0100" using a number that hasn't worked in 16 hours. Vector recall and live state are different SLAs and must be merged with live state winning on any field that can mutate. 3. Transcript chunk boundaries lose context — the transcriber emits chunks every 800ms. A boundary that cuts "my order number is" / "ABC-12345" in half means neither chunk retrieves the order. Both retrieve nothing relevant. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/podium-rag-context-bridge