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🧠 Retrieval Trace Auditor
Audit real RAG evidence traces for missing expected sources, impossible citations, unsupported claims, stale evidence, unused context, and weak routing.
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You say
{"case_id":"policy-17","query_class":"temporal","route":"vector_only","retrieved_evidence":[{"id":"policy-2024","superseded_by":"policy-2026"}],"expected_evidence_ids":["policy-2026"],"cited_evidence_ids":["citation-ghost"],"claims":[{"claim_id":"claim-1","text":"The 2026 policy replaced the rule.","supporting_evidence_ids":["support-ghost"]}]}
Your agent does
Release gate: FAIL Critical: citation-ghost was cited but never retrieved. Critical: claim-1 declares support-ghost, which was never retrieved. High: policy-2026 was expected but absent; temporal query used vector_only. Boundary: semantic entailment and answer correctness remain unverified.
What you get
About this skill
Retrieval Trace Auditor inspects the evidence path behind a RAG or knowledge-agent answer. Supply structured JSONL cases with stable retrieved evidence IDs, expected evidence when known, citations, answer claims, declared support IDs, query class, and route. The included deterministic local helper detects expected evidence that was not retrieved, citations or claim support that never appeared in context, claims with no declared support, stale selected evidence, unused retrieved context, and temporal or relational questions routed through vector-only retrieval. It returns severity-ranked findings, per-case dispositions, a release gate, and a review memo. It deliberately does not claim to judge semantic entailment or factual correctness.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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