Open source alternatives to ml-pipeline-workflow
24 open source AI Skills apps to use instead, best rated first.
Top picks
- G
github-ops
Launchgithub-ops is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It manages a GitHub repository with a focus on community health, CI reliability and the experience of contributors. It covers triaging issues by classify
Open source
- O
opensource-pipeline
Launchopensource-pipeline is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It prepares a private project for safe public release through a three-stage pipeline. The fork stage strips secrets from a copy. The sanitise st
Open source
- A
andrej-karpathy-skills
Launchandrej-karpathy-skills is a single CLAUDE.md file from the multica-ai project that is meant to improve how Claude Code behaves on coding tasks. It is derived from Andrej Karpathy's public observations about LLM coding pitfalls. The file dis
Open source
- M
microsoft/markitdown
Launchmarkitdown-mcp is the MCP server package for MarkItDown, Microsoft's tool for converting many file formats to Markdown for language models. It is a lightweight server that runs over stdio, Streamable HTTP or SSE, and exposes one tool, conve
Open source
- O
od-contribute
Launchod-contribute is an agent skill in the OpenDesign project that provides a guided, one-click contribution flow to the OpenDesign repository, written so that people without a coding background can send a real pull request. It is locked to the
Open source
- G
github-deep-research
Launchgithub-deep-research is an agent skill from the DeerFlow project that researches a single GitHub repository in several rounds and writes a report in Markdown. It is for requests that need more than a README summary, such as a comprehensive
Open source
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by shareAI-lab - A really interesting analysis of how coding agents like Claude
About ml-pipeline-workflow
ml-pipeline-workflow
Build end-to-end MLOps pipelines from data preparation through model training
ml-pipeline-workflow is an agent skill from the wshobson/agents collection for building end-to-end MLOps pipelines that cover the full machine learning lifecycle. The pipeline runs from data ingestion and preparation through training, validation and deployment to monitoring, and each step feeds the next in an automated, repeatable way. The skill gives guidance for designing the workflow orchestration of an ML system, including DAG-based orchestration in which each step is a node that runs after its dependencies finish. It covers implementing automation from data to model to deployment, setting up reproducible training workflows so that a model can be rebuilt from the same inputs, and integrating ML components into a production system. It applies when creating an ML pipeline from scratch, designing workflow orchestration for ML systems, automating data-to-model-to-deployment steps, setting up reproducible training, and connecting models to production services. The skill describes what a good pipeline provides, which helps a team decide what to build first. Pipelines depend on the team's own data stores, compute and deployment targets, so the agent adapts the patterns to those and the team reviews the result. It is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 10.9K installs and passing results from three security audits at the time of the scan.
- Price
- Free
- Made by
- wshobson
- Category
- AI Skills
- Website
- github.com/wshobson/agents/ml-pipeline-workflow