loading
loading
Wire up a retrieval-augmented generation pipeline from docs to answers.
Skips the where-do-I-start of RAG: ingestion, chunking, and upsert in one config, plus a relevance check so junk chunks never reach the prompt.
Listed for review
No verified public repo for this skill yet, so this page does not give you an install command. Skills with a verified source install in one command — or fully manual: copy the skill folder into .claude/skills/ and your agent picks it up.
Boostor Quality Score
84/100 · B
RAG Pipeline handles document ingestion, chunking strategy selection, embedding generation, and vector store upsert in a single configurable skill. It supports Pinecone, Supabase pgvector, and Qdrant out of the box. A built-in relevance evaluation step scores retrieved chunks before they enter the final prompt.
Audit product analytics for missing events, vague names, and payload gaps tied to real funnel questions.
Ties every event to a decision question and flags PII in payloads — so your analytics answers funnel questions instead of just piling up.
Transparent + deterministic: every point above is computed from this skill's real fields plus a prompt-injection safety scan. No black box, no pay-to-rank.
Trace, diff, and fix broken data transformations in any ETL pipeline.
Debugging an ETL means hunting which transform broke the data. This snapshots each step and walks the diff back to the first failing one for you.
Profile a dataset for completeness, duplicates, outliers, and schema issues before anything uses it.
Catches the business-critical nulls and duplicate keys before a report depends on them — concrete column-level issues, no hand-wavy causal claims.