機器學習

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform

Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.

Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

Android APK unpacking and resource extraction tool for reverse engineering. Use when you need to decode APK files, extract resources, examine AndroidManifest.xml, analyze smali code, or repackage modified APKs.

Build BSL semantic models with dimensions, measures, joins, and YAML config. Use for creating/modifying data models.

AI-powered enterprise Claude Code hooks orchestrator with intelligent automation, predictive maintenance, ML-based optimization, and Context7-enhanced workflow patterns. Use when designing smart hook systems, implementing AI-driven automation, optimizing hook performance with machine learning, or building enterprise-grade workflow orchestration with automated compliance and monitoring.

Auto-detects project language and framework from package.json, pyproject.toml, Cargo.toml, go.mod, and other configuration files with comprehensive pattern matching based on 17,253+ production code examples.

AI-powered enterprise Claude Code hooks orchestrator with intelligent automation, predictive maintenance, ML-based optimization, and Context7-enhanced workflow patterns. Use when designing smart hook systems, implementing AI-driven automation, optimizing hook performance with machine learning, or building enterprise-grade workflow orchestration with automated compliance and monitoring.

AI-powered enterprise MCP (Model Context Protocol) server orchestrator with intelligent plugin management, predictive optimization, ML-based performance analysis, and Context7-enhanced integration patterns. Use when creating smart MCP systems, implementing AI-driven plugin discovery, optimizing MCP performance with machine learning, or building enterprise-grade server architecture with automated compliance and governance.

Enterprise Skill for advanced development

Enterprise Skill for advanced development

Complete SPEC document authoring guide with YAML metadata structure (7 required + 9 optional fields), EARS requirement syntax (5 patterns including Unwanted Behaviors), version lifecycle management, TAG integration, pre-submission validation checklist, and real-world SPEC examples.

AI-powered enterprise web application testing orchestrator with Context7 integration, intelligent test generation, visual regression testing, cross-browser coordination, and automated QA workflows for modern web applications

AI-powered enterprise skill creation orchestrator with intelligent discovery, predictive optimization, ML-based content generation, and Context7-enhanced development patterns. Use when creating smart skill systems, implementing AI-driven skill generation, optimizing skill performance with machine learning, or building enterprise-grade skill factories with automated compliance and governance.

Enterprise GitHub issue labeling orchestrator with semantic label taxonomy, AI-powered auto-labeling, label hierarchy system, workflow automation, issue triage acceleration, and stakeholder communication; activates for issue classification, label management, workflow automation, priority assignment, and team communication

Auto-detects project language and framework from package.json, pyproject.toml, Cargo.toml, go.mod, and other configuration files with comprehensive pattern matching based on 17,253+ production code examples.