Open source alternatives to preference-optimization
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 preference-optimization
preference-optimization
Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO
preference-optimization is an agent skill from the wshobson/agents collection for aligning a fine-tuned language model with preference data, using methods from the DPO family: DPO, ORPO, KTO and SimPO. Preference data comes either as pairs, where a reviewer picks which of two responses is better, or as unpaired thumbs-up and thumbs-down feedback. The skill applies when that kind of feedback exists and the model should learn from it rather than from demonstrations or a verifiable reward. Within the wshobson fine-tuning sequence, the routing step is assumed to have already sent the work here, and the other skills cover demonstration-based training and reward-based reinforcement learning. The skill covers the choice of method within the DPO family, the evidence on how much that choice actually changes results, the production training pattern, and how to build the preference pairs in the first place. Preference data is only as good as the judgments behind it, so the skill's guidance on building pairs matters as much as the training configuration. The skill is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 2.4K 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/preference-optimization