By athola
Run local ONNX model inference for ML-enhanced plugin capabilities. Automatically provisions an ONNX Runtime daemon via uv, enabling skill quality evaluation and data analysis workflows with Airflow integration.
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub athola/claude-night-market --plugin oracleMeta-skills infrastructure for Claude Code plugin ecosystem - skill authoring, hook development, modular design patterns, and evaluation frameworks
Spec Driven Development toolkit - structured specification, planning, and implementation workflows for systematic feature development
Spatial knowledge organization using memory palace techniques - build, navigate, and maintain virtual memory structures for enhanced recall and information management. Includes PR Review Room for capturing review knowledge.
Documentation review, cleanup, generation, voice extraction, and human-quality writing enforcement with AI slop detection and SICO-based voice profiling
Multi-source research plugin — code archaeology, community discourse, academic literature, and TRIZ cross-domain analysis with domain-adaptive depth
Train and optimize machine learning models with automated workflows
Evaluate and compare ML model performance metrics
ML engineering agents providing expertise in MLOps, model deployment, and inference optimization
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
Use this agent when implementing AI/ML features, integrating language models, building recommendation systems, or adding intelligent automation to applications. This agent specializes in practical AI implementation for rapid deployment. Examples:\n\n<example>\nContext: Adding AI features to an app\nuser: "We need AI-powered content recommendations"\nassistant: "I'll implement a smart recommendation engine. Let me use the ai-engineer agent to build an ML pipeline that learns from user behavior."\n<commentary>\nRecommendation systems require careful ML implementation and continuous learning capabilities.\n</commentary>\n</example>\n\n<example>\nContext: Integrating language models\nuser: "Add an AI chatbot to help users navigate our app"\nassistant: "I'll integrate a conversational AI assistant. Let me use the ai-engineer agent to implement proper prompt engineering and response handling."\n<commentary>\nLLM integration requires expertise in prompt design, token management, and response streaming.\n</commentary>\n</example>\n\n<example>\nContext: Implementing computer vision features\nuser: "Users should be able to search products by taking a photo"\nassistant: "I'll implement visual search using computer vision. Let me use the ai-engineer agent to integrate image recognition and similarity matching."\n<commentary>\nComputer vision features require efficient processing and accurate model selection.\n</commentary>\n</example>
ML research skills: topic, plan, judge, run, sweep, verify, fortify, retro
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