Open source alternatives to nature-statistics
24 open source AI Skills apps to use instead, best rated first.
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github-ops
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opensource-pipeline
Getopensource-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
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andrej-karpathy-skills
Getandrej-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
Getmarkitdown-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
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od-contribute
Getod-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
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github-deep-research
Getgithub-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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About nature-statistics

nature-statistics
Audit or improve manuscript statistical reporting, including experimental units
nature-statistics is an agent skill that audits and improves the statistical reporting in a manuscript, so that it is transparent, reproducible and appropriately bounded. It covers experimental units, replication, uncertainty, tests and the statistics shown in figures. It is a reporting and review skill, not a replacement for a statistician who reanalyses raw data. It only computes from data when you supply the data and explicitly ask for computation. Its default stance puts design transparency ahead of decorative statistical language. It asks three questions: what was measured, which unit was analysed, and what inference was claimed. It treats the independent experimental unit as the default sample size, and it does not silently count cells, fields of view, repeated readings, spectra, model runs or technical replicates as independent samples. It prefers effect sizes, uncertainty intervals, sample sizes and exact test definitions to significance-only wording. Where information is missing, it marks the gap for the authors rather than inventing sample sizes, tests, software versions, exclusions, randomisation or blinding.
- Price
- Free
- Made by
- yuan1z0825
- Category
- AI Skills
- Website
- github.com/yuan1z0825/nature-skills/nature-statistics










