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    RAG Failure Diagnostics & Architect

    1

    This skill transforms your AI agent into a Retrieval Architect capable of diagnosing why RAG systems fail.

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    RAG Failure Diagnostics & Architect

    RAG Failure Diagnostics & Architect

    Example session with this skill installed

    Our internal assistant (Pinecone, 512-token chunks, top-k 5, GPT-4o) answers "Which customers are still on the v1 pricing plan?" confidently with three names. The real list has eleven, and one of the three moved to v2 last quarter. Should we raise top-k or change the chunking?

    • Read your context and instructions
    • Compiled the rag failure diagnostics

    Mode: DIAGNOSE. This is a structural miss, not a tuning problem. Raising top-k or re-chunking will not make the list complete or current.

    Five-question triage

    #QuestionAnswer for this query
    1Multi-hop?No
    2Sequence or time?Yes: "still on" needs the current plan, not any mention
    3Causation?No
    4Aggregate or negative?Yes: "which customers" is an exhaustive list
    5In the corpus, retrievable?Probably yes, spread across many docs

    Failure pattern: rows "How many / which don't" (aggregation over top-k) and "Misses latest" (no temporal index). Top-k 5 can never return eleven customers, and similarity ranks an old "moved to v1" note as highly as the newer "moved to v2" note.

    Fix

    • Answer plan membership from a structured source: a customer_plan table (customer_id, plan, effective_from, effective_to) fed by billing or CRM.
    • Add a router: list, count and "current state" questions go to SQL; "explain the v1 terms" questions stay on vector RAG.
    • Have the model phrase the SQL result, not reconstruct it from chunks.

    Honest effort

    A router plus one typed table is moderate work. Tuning is the pragmatic choice only for single-passage lookups, which this query is not.

    Next steps

    • List your ten most common questions and tag each with its triage row.
    • Confirm billing or CRM has plan history with dates.
    • Keep this query as a regression test with the known answer of eleven.

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

    What you get

    Identify if a RAG failure is a tuning issue or a structural architecture missDesign a hybrid router to dispatch queries between Graph, SQL, and Vector storesCreate a schema to capture causal links and decision provenance for agent memoryPrescribe specific fixes for temporal queries where vector search failsEvaluate if your use case requires GraphRAG vs. standard Vector RAG

    About this skill

    What it does

    This skill transforms your AI agent into a Retrieval Architect capable of diagnosing why RAG systems fail. Instead of blindly tuning chunk sizes or embedding models, it analyzes whether a query's failure is structural (requiring a Knowledge Graph, temporal index, or structured query) or merely a tuning issue. It provides three specialized modes: DIAGNOSE to classify failing queries, ARCHITECT to design hybrid retrieval pipelines, and SCHEMA to build institutional memory layers that capture causal relationships vector search ignores.

    Why use this skill

    RAG systems often fail "quietly"—returning plausible but incorrect answers because vector search cannot handle multi-hop reasoning, temporal changes, or aggregations. This skill prevents the common mistake of over-indexing on semantic similarity. It helps developers move beyond basic vector stores to build sophisticated, production-ready context engines that understand "why" and "when," not just "what."

    What it supports

    • RAG & Vector DBs: Hybrid search, reranking, and chunking strategies.
    • Knowledge Graphs: Designing GraphRAG architectures for multi-hop and causal reasoning.
    • Hybrid Routers: Building logic to dispatch queries between SQL, Vector, and Graph stores.
    • Metadata & Temporal Ops: Constructing schemas for event-sourced or time-aware retrieval.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

    Skills folder by agent

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