designing hybrid context layers
by loreto
Architects the right retrieval strategy for every query — teaching your agent when to use RAG, a knowledge graph, or a temporal index instead of defaulting to vector search for everything.
Skills for retrieval pipelines, evaluations, embeddings, MCP servers, and running language models in production. Measure and operate your AI stack instead of guessing.
43 skills · page 1 of 2
by loreto
Architects the right retrieval strategy for every query — teaching your agent when to use RAG, a knowledge graph, or a temporal index instead of defaulting to vector search for everything.
by loreto
RAG fails quietly. It retrieves documents, returns confident-looking answers, and misses the question entirely — because the question required connecting facts across documents, reasoning about sequence, or tracing causation. This skill gives you a five-question diagnostic checklist that classifies any failing query as either RAG-safe or structurally RAG-incompatible, then maps it to the specific failure pattern and the architectural fix that resolves it.
by Roy Yuen
Design, debug, and optimize production RAG systems with expert architecture, hybrid search, and grounding strategies.
by Roy Yuen
Professional prompt engineering patterns for building robust, secure, and production-ready LLM applications.
by GTDataworks
Five compact operator skills for artifact intake, zip preflight, test planning, session handoff, and MCP tool-candidate inspection.
by Kaymue
Diagnose broken RAG systems. 8 failure categories: chunking, embeddings, retrieval, reranking, hallucination. Recall@k measurement.
by tudor
Build a full-stack AI chatbot trained on your own documents across any industry — legal, healthcare, e-commerce, HR, finance, real estate, insurance, education, cybersecurity, government, and more.
by Arun Dass
Cuts AI coding agent token consumption by 75–94%
Scaffold a secure, spec-compliant MCP server from a description of the tools you want to expose. Sets up the official SDK (TypeScript or Python/FastMCP), defines tools/resources/prompts with strict JSON Schema, wires the right transport (stdio or Streamable HTTP), adds OAuth 2.1 for remote, and hardens against the MCP-specific footguns — prompt injection via tool output, token passthrough, over-broad scopes, command/path/SSRF injection, leaked secrets — before it ships. Returns a runnable skeleton plus a security checklist. Built by someone who's shipped production MCP servers.
by Edric Vale
Drive a running TouchDesigner instance through the twozero MCP bridge: create operators, wire nodes, animate parameters, run render passes, and query state back
Design and write the eval suite for your LLM-powered feature — the metrics that match your failure modes, a golden dataset plan with starter cases, anchored rubrics, LLM-as-judge prompts with the known bias mitigations, and pass/fail gates wired for CI.
Diagnose and fix context-window problems in your agent setup. Inventories everything that loads — memory files, skills, MCP tool definitions, hooks — estimates where the budget goes, maps your symptoms to causes, and prescribes concrete trims, restructures, and session habits, rewriting the files where that's the fix.
Drastically reduce RAG costs and latency while improving retrieval accuracy through advanced memory architecture.
by Ifásola
Diagnose RAG bottlenecks with precision metrics (Recall, MRR, nDCG) to identify retrieval or ranking failures.
Produces a diagnosis of why a RAG system gives confident-but-wrong answers, or picks between vector search, knowledge graph, and structured/temporal retrieval. Use when the user says "why is our RAG hallucinating", "diagnose this failing query", "should we use a knowledge graph", "pick a retrieval architecture", or "design our memory layer". Do not use when the request is building a RAG system from zero.
by GTDataworks
Capture tool-trace receipts, label failures, and emit replay prompts so you can debug an agent run after it finishes.
RAG system design and retrieval debugging (retrieval-augmented generation) - architecture decisions, hybrid search strategy, and a readiness checklist. Use when the user says my RAG app returns irrelevant chunks, the model makes things up, debug our retrieval accuracy, fix RAG hallucinations, design our RAG system, or choose a vector database.
Every RAG tutorial shows the same pipeline; almost none of it survives contact with your actual corpus
by GTDataworks
Compatibility evidence for MCP servers against Codex CLI and Qwen: discovery receipts, report.md, and receipt.json.
Deploy a local, private RAG pipeline using Supabase, n8n, and Ollama in minutes.
Preflight redacted MCP 2026-07-28 OAuth evidence for issuer discovery, registration, PKCE, scopes, token audience, refresh behavior, and role-specific gaps.
by Md Tamim
A systematic diagnostic framework for debugging AI pipelines, prompt drift, RAG failures, and agent orchestrations.
by PromptWagon
Reviews document sets, source quality, chunking logic, metadata, retrieval coverage, citation traceability, answer grounding, source gaps, stale content, duplicate content, and failure patterns for RAG knowledge-base chatbots. Helps AI, product, support, governance, and engineering teams diagnose common and costly RAG quality problems before deployment or after incidents.