MLOps Industrialization
A framework to transform experimental ML prototypes into robust, production-ready Python packages using src layout, hybrid architecture, and strict configuration management.
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180 skills found
A framework to transform experimental ML prototypes into robust, production-ready Python packages using src layout, hybrid architecture, and strict configuration management.
Execute the implementation planning workflow, generate technical design artifacts, and structure research tasks for Spec Kit projects.
Manage CI/CD workflows, Docker containerization, and infrastructure configurations for the multi-chain crypto wallet system.
Completes development branches by verifying tests, managing merge or PR workflows, and cleaning up worktrees to ensure a consistent repository state.
Automates the release workflow for Worktrunk, including version bumping, CHANGELOG generation, contributor crediting, and publishing to crates.io and GitHub.
Orchestrates Change Request Document workflows for brownfield projects, managing codebase context, impact analysis, and CRD document generation.
Shared memory and collaboration layer for AI coding agents to track actions, manage sessions, detect conflicts, and preserve project context across tools.
Manages the OpenClaw release lifecycle: prepares branches, updates versioning across multiple platforms, generates changelogs, and orchestrates npm and binary artifact publishing.
Standardized review patterns, validation checklists, and quality benchmarks for PolicyEngine codebases.
Production-ready reinforcement learning using Stable Baselines3. Train agents, design custom environments, implement training callbacks, and optimize workflows with a scikit-learn-style API.
Maintains a detailed, step-by-step implementation diary for coding sessions with docmgr integration to track changes, rationale, commands, and failures.
Efficiently manage git worktrees with automated file synchronization, background task execution, and CLI-based workspace orchestration.