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exploratory-data-analysis

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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