Open source alternatives to evaluation-methodology
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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Learn Claude Code
by shareAI-lab - A really interesting analysis of how coding agents like Claude
About evaluation-methodology
evaluation-methodology
PluginEval quality methodology, covering dimensions, rubrics
evaluation-methodology is an agent skill from the wshobson/agents collection that explains how PluginEval measures the quality of a skill or plugin. PluginEval scores a skill or plugin from 0 to 100 by combining up to three layers of evaluation. The static layer is a lint: it is fast and deterministic and is useful for checking the structure of a skill. The LLM judge and Monte Carlo layers are more involved, and the skill is clear that both are experimental and not validated against human labels, so their numbers should be treated as rough signals rather than measurements. The skill is for understanding how plugin quality is measured, reading a score and its dimensions, and deciding how to act on a result. It points to the rubric anchors used by the judge, and to a reference on fixing each anti-pattern and improving each dimension of the score. For the trace-based evaluation program, it refers to the evals documentation at the root of the repository. It is useful for skill authors who want to know what a score is made of before they change their skill to raise it. It is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 7.0K 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/evaluation-methodology