RAG Architecture & Debugging
Design, diagnose, and optimize high-performance RAG systems with an engineering-first framework.
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THE AGENSI STORE
40 skills found
Design, diagnose, and optimize high-performance RAG systems with an engineering-first framework.
Adversarial memory audit to remove PII, stale facts, and injected instructions from agent storage.
Hardens AI prompts and agent workflows against logic errors, tool-misuse, and prompt injection.
Inventory every LLM model and provider your code depends on, the AI bill of materials, and flag the dependency risk. It lists each provider, model, and where it's used, then flags hardcoded model ids, single-provider dependency with no alternative, the same model referenced by different ids, model ids with no config or env indirection, and providers pinned in your manifests. Recognizes OpenAI, Anthropic, Google Gemini, and more from an editable list.
Find the LLM integration code that will not survive a provider being pulled or going down. Flags single-provider lock-in with no alternative, calls with no failover branch, missing timeouts, retries with no limit or backoff, no degraded-mode default, and hardcoded endpoints with no alternate. This is about the model going away, not the model declining.
by SkillForge
Stop fragile agent chains with structured, versioned, and idempotent handoff contracts for multi-agent systems.
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.
by Ifásola
Diagnose RAG bottlenecks with precision metrics (Recall, MRR, nDCG) to identify retrieval or ranking failures.
by Kaymue
Audit AI/LLM spend across OpenAI, Anthropic, AWS Bedrock, Azure. Find waste, project runway, FinOps report. Free scripts.
Turn a one-line job description into a production-ready, guarded, and tested AI agent scaffold.
Create a system of six reusable, field-specific prompt patterns to ensure consistent AI outputs.
Turn Claude into a court. One brain, a dozen free workers. King makes your agent the orchestrator, not the laborer — it plans the task, delegates the heavy lifting to a fleet of free-tier LLMs, cross-checks their work, and hands you a single verified answer better than any one model could give. You keep the judgment; the free models burn the tokens. Now with a guided first-run wizard that gets you set up in minutes.
Model what your LLM app or agent will cost, find where the money goes, and get a plan to cut it. Per-request and monthly projections, ranked cost drivers, an optimization plan with estimated savings, and unit economics against your pricing — with the arithmetic shown.
by John Barros
Architect resilient, human-in-the-loop agent workflows with strict stop criteria, budgets, and validation rubrics.
A persistent, keyword-triggered verbosity toggle to force bare-minimum, caveman-brief AI responses.
Your skill works today. Will it work after the next model update? Build the harness that answers with numbers. Builds a standalone regression test harness with mechanical grading to verify skill behavior after model or code updates.