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    🧠 Retrieval Trace Auditor

    1

    Audit real RAG evidence traces for missing expected sources, impossible citations, unsupported claims, stale evidence, unused context, and weak routing.

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    🧠 Retrieval Trace Auditor

    🧠 Retrieval Trace Auditor

    Example session with this skill installed

    {"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"]}]}

    • Read your context and instructions
    • Compiled the retrieval trace auditor

    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.

    Connects securely to your tools. The creator never sees your data.

    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 use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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