Open source alternatives to llm-evaluation
24 open source AI Skills apps to use instead, best rated first.
Top picks
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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
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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
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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
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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
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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
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by shareAI-lab - A really interesting analysis of how coding agents like Claude
About llm-evaluation
llm-evaluation
Implement comprehensive evaluation strategies for LLM applications
llm-evaluation is an agent skill from the wshobson/agents collection for designing evaluation strategies for LLM applications. It combines automated metrics, human feedback and benchmarking to measure how well an application performs. Automated metrics are fast, repeatable and scalable, because they compute scores from outputs without a person reading each one. Human evaluation covers the qualities that metrics miss, and A/B testing shows how two versions of a prompt or model behave with real users. The skill begins with the core evaluation types and then covers how to build a test set, choose metrics that fit the task, and set baselines so later changes can be compared against where the system started. It applies when measuring LLM performance systematically, comparing models or prompts, detecting regressions before deployment, validating that a prompt change actually helped, building confidence in production systems, establishing baselines and tracking progress over time, and debugging unexpected model behavior. The guidance is meant to make model quality a measured quantity rather than a matter of impressions. The team still chooses the metrics and the test cases, because the right measures depend on what the application is for. The skill is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 12.4K 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/llm-evaluation