scientific-eda
Scientific exploratory data analysis
This skill guides defensive, human-led exploratory data analysis on scientific data. The agent does not open files and dump code; it captures problem context first, helps narrow to a single first step, takes instruction from the user, and asks "why?" before executing when the user requests a specific plot or table.
Usage
Use this skill when the user provides one or more data files (CSV, FASTA, or other scientific formats) and wants to explore or analyze them. Start by capturing context—do not load or plot data until the problem (biological, chemical, or data-science question) is clearly stated and the agent is aligned as a guided assistant.
Requirements
- uv for running Python scripts: every script uses PEP723 inline script metadata and is run with
uv run script.py. Do not run ad-hoc Python or raw interpreters; each script declares and manages its own dependencies. - Ability to read the relevant data formats (pandas, BioPython, etc.) via dependencies declared in the script block.
What It Does
- Context first – Capture and record the problem context (what question, what domain) before touching the data.
- Single first step – Help the user narrow to one first plot or one first summary (not a barrage of code or plots).
- Human-guided execution – Take instruction on what to do next; when the user says "make this plot" or "give me that table," ask why before doing it, then execute.
- Session layout – Each analysis is a session: one folder under
analysis/with a descriptive name and start date/time, containingjournal.md,plots/, andscripts/.
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