ml-pipeline-workflow
Build and orchestrate end-to-end MLOps pipelines covering data preparation, training, validation, and automated deployment.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
158 skills found
Build and orchestrate end-to-end MLOps pipelines covering data preparation, training, validation, and automated deployment.
Audit and synchronize the supported LLM model list in assets.py against the authoritative litellm registry.
Detects timing side channels in cryptographic code to prevent secret data leakage. Essential for auditing sensitive implementations.
Maintain and update the MassGen model registry, including backend capabilities, model metadata, pricing structures, and context window configurations for new and existing AI models.
Standards for organizing, structuring, and maintaining project documentation to ensure consistency across user guides, development docs, and AI-assisted workflows.
Fetch and analyze current trending programming models from OpenRouter. Ideal for selecting models for reviews, optimizing AI costs, and staying updated on AI coding trends with real-time pricing and context window data.
Persistent, Git-friendly memory for Claude. Automatically store and retrieve project decisions, bug fixes, and coding patterns in a local .mv2 file.
Generate AGENTS.md and AI configuration files (Cursor, Claude, Gemini, Copilot) for your project to streamline your vibe-coding workflow and maintain context across sessions.
A Git-backed memory store for agent skills. Download, version, edit, and share custom agent behaviors and procedural knowledge using a CLI.
Automates the creation of Betty Framework skills by scaffolding directory structures, generating YAML manifests, and handling registry registration.
Git-aware logical undo at track, phase, or task level with confirmation gates.
Keep your technical specifications, test suites, and source code perfectly synchronized during AI-assisted development.