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Debug a RAG failure by classifying whether it's source, chunking, embedding, ranking, prompt, or synthesis.
Stops you blaming the model for a retrieval bug — it classifies each miss as source, chunking, embedding, ranking, prompt, or synthesis.
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 Retrieval Debugger takes your queries, retrieval logs, source corpus, and expected answers, then returns a debug report: per query, the expected source, the chunks actually retrieved, why it missed, and a fix plan. It classifies each failure as a missing source, a chunking problem, an embedding issue, a ranking issue, a prompt issue, or an answer-synthesis issue — instead of the usual reflex of blaming the model and rewriting 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.