Free alternatives to exploratory-data-analysis
50 free AI Skills apps to use instead, best rated first.
Top picks
- W
webapp-testing
LaunchNative Python Playwright scripts for testing local web applications with server lifecycle management. Includes with_server.py helper to automatically start and manage single or multiple servers before running automation scripts. Supports re
- C
content-research-writer
LaunchAssists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on
- O
openhue
Launchopenhue is an agent skill from the OpenClaw project, listed on skills.sh. It lets an agent control Philips Hue lights and scenes through a Hue Bridge with the OpenHue CLI: turn lights on and off, dim rooms, set scenes such as movie mode, an
- B
batch-grill-me
Launchbatch-grill-me is an agent skill from Matt Pocock's skills collection, listed on skills.sh. It interviews you relentlessly about a plan until you share an understanding. It maps decisions as a tree and asks every question it can answer now
- I
implement
Launchimplement is an agent skill from Matt Pocock's skills collection, listed on skills.sh. It implements work from a spec or tickets: it fetches the ticket, works test-first at agreed seams, runs type checks and tests as it goes, reviews the re
- F
frontend-design
LaunchDistinctive, production-grade frontend interfaces that reject generic AI aesthetics through intentional design choices. Guides aesthetic direction selection (brutalist, maximalist, retro-futuristic, luxury, organic, etc.) before implementat
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brainstorming
Transform rough ideas into fully-formed designs through structured questioning
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Guides completion of development work by presenting clear options
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code
About exploratory-data-analysis
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.
- Price
- Free
- Made by
- k-dense-ai
- Category
- AI Skills
- Website
- github.com/k-dense-ai/scientific-agent-skills/exploratory-data-analysis