Open source alternatives to pymc
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 pymc
pymc
Builds and checks Bayesian models
pymc is an agent skill for building and checking Bayesian models with PyMC, including hierarchical models, NUTS sampling with MCMC, variational inference, predictions with mutable data, and posterior predictive checks. It targets PyMC 6.3.2, PyTensor 3.3.2 and ArviZ 1.3.0. These are local Python interfaces. No service endpoint, credentials or remote inference is needed. The native tests use the PyMC NUTS sampler on small synthetic models. Optional nutpie and JAX samplers, sequential Monte Carlo, and convergence at production scale were not validated by those tests. For a scientific run the skill asks you to record the complete resolved environment, the backend, the random seeds, the preprocessing and the data provenance. The review notes an environment detail. On macOS, a C linker error could be avoided by disabling PyTensor C compilation for the process, and the Python library workflows passed under that setting. That does not show that the C backend works on the same host. Use it when you are fitting a Bayesian model and want the sampling diagnostics and the posterior checks handled properly.
- Price
- Free
- Made by
- k-dense-ai
- Category
- AI Skills
- Website
- github.com/k-dense-ai/scientific-agent-skills/pymc