claude

nlp-prod

Tools, skills, and CLI extensions for designing and productionizing LLM inference pipelines. Pattern-guided, slim by default, eval-first.

229 on GitHubby AIWG Contributorsv2026.11.5MITupdated todayproductivity
/

Plugin nlp-prod from marketplace aiwg in jmagly/aiwg. Commands read from the repo's manifest — today.

What it installs

Skills (7)

  • cost-optimizer

    Analyze LLM pipeline costs and generate concrete optimization recommendations with savings estimates

  • eval-loop

    Configure and run the isolated eval loop pattern — generate, evaluate, refine until pass threshold met

  • pattern-selector

    Recommends the right LLM pipeline pattern for a use case — simple chain, embedded agent, state machine, RAG, eval loop, or dynamic prompt

  • pipeline-design

    Interactive LLM inference pipeline design — elicits requirements, recommends pattern, scaffolds production-ready artifacts

  • pipeline-status

    Show status overview of all LLM inference pipelines in the current project

  • productionize

    Production readiness review — strip prototype scaffolding, harden code, validate cost model, generate prod/ artifacts

  • prompt-engineer

    Production prompt engineering — write, iterate, and refine prompts with built-in eval loop feedback

Subagents (4)

  • Cost Analyst

    TCO analysis, model selection recommendations, caching strategy, and parallelization opportunities for LLM inference pipelines

  • Eval Reviewer

    Isolated evaluator in the eval loop — scores generator outputs with strict isolation; never sees generator context or chain-of-thought

  • Pipeline Architect

    Designs optimal LLM inference pipeline structure for requirements; selects the right pattern; estimates cost at target volume

  • Prompt Engineer

    Creates and iteratively refines production-quality prompts with built-in eval loop integration

View source on GitHub aiwg.io

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