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senior-data-scientist

Data science workflow for turning ambiguous questions into measurable metrics, experiments, and models.

Skill by vadimcomanescu

IndexedMIT★ 25
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Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/vadimcomanescu-senior-data-scientist

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Data science workflow for turning ambiguous questions into measurable metrics, experiments, and models. Use when framing hypotheses, selecting metrics, designing A/B tests, building predictive models, doing error analysis, or writing experiment/model reports with clear assumptions and caveats.

Use cases: data, writing, productivity

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found
Runs as
Python package

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/senior-data-scientist

What's inside

Skill name
senior-data-scientist
Description
Data science workflow for turning ambiguous questions into measurable metrics, experiments, and models. Use when framing hypotheses, selecting metrics, designing A/B tests, building predictive models, doing error analysis, or writing experiment/model reports with clear assumptions and caveats.
License
MIT
Instructions
21 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (4)

  • LICENSE.txt
  • SKILL.md
  • experiment-report.md · references/
  • csv_profile.py · scripts/

SKILL.md (first lines)

# Senior Data Scientist

Be rigorous about what you’re measuring and why.

## Quick Start
1) Translate the ask into a decision: “what will we do differently based on the result?”
2) Define metrics: primary metric, guardrails, and segmentation.
3) Choose method: analysis, A/B test, causal approach, or predictive model.
4) Validate: leakage checks, baseline, error analysis, and robustness.
5) Communicate: limitations, assumptions, and next steps.

## Optional tool: quick CSV profiling (no pandas)
```bash
python ~/.codex/skills/senior-data-scientist/scripts/csv_profile.py data.csv --max-rows 50000 --out /tmp/profile.json
```

## References
- Experiment report template: `references/experiment-report.md`

From vadimcomanescu/codex-skills, MIT.

More from vadimcomanescu/codex-skills

Signals

Stars
25
Forks
8
Open issues
2
Last commit (this folder)
Jan 19, 2026 (9 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
Repository
github.com/vadimcomanescu/codex-skills
First seen
Oct 24, 2026
Last checked
Oct 24, 2026
Source
GitHubgithub-graphql

More from vadimcomanescu

Cite this page

senior-data-scientist skill. Agent Mart. https://agentmart.dev/skills/vadimcomanescu-senior-data-scientist (last updated 2026-10-24)