claude

polymath-ai

AI craft: prompt engineering, RAG, eval design, agent design, LLM guardrails, cost/latency tuning.

0 on GitHubby Mohammad Bafkarv0.12.0MITupdated 15 days agoai
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Plugin polymath-ai from marketplace polymath in MohammadBafkar/polymath. Commands read from the repo's manifest — 12 days ago.

What it installs

Skills (6)

  • agent-design

    Design an LLM agent — architecture (ReAct, plan-execute, reflexion), tool/function-calling contract, memory, multi-agent orchestration, and the stop/budget conditions.

  • eval-plan

    Design an evaluation for an LLM feature — task type, dataset shape, automated vs human rubric, regression set, decision threshold.

  • llm-cost-latency

    Cut LLM cost and latency — model routing/cascade, prompt caching, batching, streaming, context trimming, output-token control, and a per-request cost/latency budget.

  • llm-guardrails

    Design guardrails for an LLM feature — input validation, prompt-injection defenses, output filtering/schema validation, allow-deny policy, and human-in-the-loop gates.

  • prompt-engineer

    Author or improve an LLM prompt — clarify task, constrain output shape, place worked examples, bound calibration; vendor-agnostic.

  • rag-design

    Design a retrieval-augmented generation pipeline — chunking, embeddings, retriever, reranker, prompt assembly — with the right cuts for the use case.

View source on GitHub

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