Open source alternatives to statistical-power
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 statistical-power
statistical-power
Calculates sample sizes and statistical power for study planning
statistical-power is an agent skill for planning studies by calculating sample sizes and statistical power. It answers how many independent experimental units are needed to detect an effect of scientific importance, or what effects a feasible sample could detect. The skill says to choose the inferential goal first, because precision, equivalence, non-inferiority and sequential monitoring each need their own calculations that the bundled superiority-test helpers do not cover. For any given test, four quantities are linked: sample size, effect size, significance level and power. Fixing three of them lets you solve for the fourth, when a solution exists, for a fixed design, analysis and nuisance parameters. The skill covers analytical calculations, which are exact under standard assumptions and approximate otherwise, and simulation or Monte Carlo methods, which need a credible data-generating process and a calibrated planned analysis. It also helps you choose and convert effect sizes, which is often the hardest part. Use it when someone asks how many subjects, samples or replicates are needed, or wants an a priori power analysis.
- Price
- Free
- Made by
- k-dense-ai
- Category
- AI Skills
- Website
- github.com/k-dense-ai/scientific-agent-skills/statistical-power