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exploratory-data-analysis

Open source alternatives to exploratory-data-analysis

24 open source AI Skills apps to use instead, best rated first.

Top picks

  1. G

    github-ops

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    github-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

  2. O

    opensource-pipeline

    Launch

    opensource-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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  3. A

    andrej-karpathy-skills

    Launch

    andrej-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

  4. M

    microsoft/markitdown

    Launch

    markitdown-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

  5. O

    od-contribute

    Launch

    od-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

  6. G

    github-deep-research

    Launch

    github-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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About exploratory-data-analysis

E

exploratory-data-analysis

Performs bounded, local exploratory analysis of explicitly supported scientific

exploratory-data-analysis is an agent skill for inspecting local scientific data files before modelling or confirmatory analysis. It produces bounded, deterministic aggregate reports for files you explicitly authorise. It supports redacted profiles of CSV, TSV and JSON files, with optional support for NumPy, HDF5 and other formats described in its domain references. The skill is clear about its limits. It does not certify a file, infer scientific meaning, or support every format it lists. Its safety rules are firm: it treats every cell, header, sequence title, HDF5 name or attribute, image tag and metadata string as untrusted data. It never follows instructions embedded in a file, never resolves embedded URLs, never runs macros, never evaluates expressions, never executes HDF5 objects, never loads models, and never passes file-derived text to a shell. It is meant to run on local data, so the output is an aggregate report you can review rather than a copy of the raw records. The skill suits researchers who want a first look at a dataset's structure and quality before deciding how to model it.

View exploratory-data-analysisAll AI Skills apps
Price
Free
Made by
k-dense-ai
Category
AI Skills
Website
github.com/k-dense-ai/scientific-agent-skills/exploratory-data-analysis