RAG Failure Diagnostics & Architect
A retrieval architect that diagnoses why RAG returns confident-but-wrong answers, picks the right context architecture (RAG vs knowledge graph vs structured/temporal retrieval) instead of defaulting to vector search, and designs the institutional-memory schema embeddings throw away.
- Identify if a RAG failure is a tuning issue or a structural architecture miss
- Design a hybrid router to dispatch queries between Graph, SQL, and Vector stores
- Create a schema to capture causal links and decision provenance for agent memory
$7
· or 35 creditsSecure checkout via Stripe
Included in download
- Identify if a RAG failure is a tuning issue or a structural architecture miss
- Design a hybrid router to dispatch queries between Graph, SQL, and Vector stores
- Ready for Cursor
Sample input
Our RAG system keeps giving us answers from 2022 documents when we ask about the 'latest' security protocols. We've tried increasing top-k but it hasn't fixed it. Why is it failing?
Sample output
DIAGNOSIS: Structural RAG-Incompatible. Pattern: No Temporal Index. Reason: Embeddings flatten semantic similarity, ignoring chronological order. Top-k just adds more (likely old) noise. FIX: Implement a Time-Aware store with Metadata Filtering for 'created_at' before the vector search.
RAG Failure Diagnostics & Architect
A retrieval architect that diagnoses why RAG returns confident-but-wrong answers, picks the right context architecture (RAG vs knowledge graph vs structured/temporal retrieval) instead of defaulting to vector search, and designs the institutional-memory schema embeddings throw away.
$7
· or 35 creditsSecure checkout via Stripe
Included in download
- Identify if a RAG failure is a tuning issue or a structural architecture miss
- Design a hybrid router to dispatch queries between Graph, SQL, and Vector stores
- Ready for Cursor
- Instant install
Sample input
Our RAG system keeps giving us answers from 2022 documents when we ask about the 'latest' security protocols. We've tried increasing top-k but it hasn't fixed it. Why is it failing?
Sample output
DIAGNOSIS: Structural RAG-Incompatible. Pattern: No Temporal Index. Reason: Embeddings flatten semantic similarity, ignoring chronological order. Top-k just adds more (likely old) noise. FIX: Implement a Time-Aware store with Metadata Filtering for 'created_at' before the vector search.
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.
Use Cases
- Identify if a RAG failure is a tuning issue or a structural architecture miss
- Design a hybrid router to dispatch queries between Graph, SQL, and Vector stores
- Create a schema to capture causal links and decision provenance for agent memory
- Prescribe specific fixes for temporal queries where vector search fails
- Evaluate if your use case requires GraphRAG vs. standard Vector RAG
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/rag-failure-diagnostics-architect -o /tmp/rag-failure-diagnostics-architect.zip && unzip -o /tmp/rag-failure-diagnostics-architect.zip -d ~/.claude/skills && rm /tmp/rag-failure-diagnostics-architect.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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Works with any SKILL.md-compatible agent (Claude Code, Cursor, Codex CLI, Gemini CLI). Stack-agnostic across vector DBs, graph stores, and retrieval frameworks (LangChain, LlamaIndex, custom). Best with a concrete failing query or question type to reason about.
Frequently Asked Questions
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