Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.
About this skill (catalog notes)
Data Warehouse Experimentation includes pricing or quota commentary; 8 code blocks for direct copy-paste. At roughly 3,652 words the SKILL.md is on the longer end of the catalog distribution.
Original author
rampstackco
Indexed lastmod
Catalog position
Science & Research · math-stats
Indexed related skills
10
How Data Warehouse Experimentation fits the catalog
Data Warehouse Experimentation sits in the Science & Research category under the math-stats sub-topic in the ClaudSkills catalog. There are 10 related skills indexed alongside it; comparing a few before installing usually reveals which fits your workflow best.
These notes are auto-generated from features detected in the SKILL.md file and from this catalog's structure — they aren't part of the source repository.
From the source SKILL.md
A senior data scientist's playbook for running experiments natively out of BigQuery, Snowflake, or any modern data warehouse, with metric definitions in dbt and statistical analysis in SQL or Python.
What this skill does
Data Warehouse Experimentation is a community-contributed Claude Code skill in the math-stats sub-category. It ships as a SKILL.md file that Claude Code auto-discovers under ~/.claude/skills/data-warehouse-experimentation/ and loads when your prompt matches the skill's trigger.
Who uses this skill
The Data Warehouse Experimentation Claude Code skill is built for researchers, data scientists, academics, and analysts working with complex data and scientific literature. It's part of ClaudSkills (also referred to as Claude Skills or Claude Code Skills) — the open community-curated registry of 116,000+ SKILL.md files for Anthropic's Claude Code agent and the wider Claude ecosystem (Claude API, Claude Agent SDK).
Or just download SKILL.md directly and drop it into ~/.claude/skills/data-warehouse-experimentation/. Claude Code auto-discovers it on next session.
Skills live at ~/.claude/skills/data-warehouse-experimentation/SKILL.md on macOS/Linux, or %USERPROFILE%\.claude\skills\data-warehouse-experimentation\SKILL.md on Windows. See the full install guide for step-by-step instructions.
Telegram
📱 Install from your phone or desktop Telegram
Open @claudskills_bot on Telegram, tap Open Desktop App, and the desktop app installs this skill for you. Or share the bot link with a colleague — they get the same one-tap install. Learn more →
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One-click install via the desktop app
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Pro
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How do I install the Data Warehouse Experimentation Claude Code skill?
Install via the ClaudSkills desktop app (one click) or copy SKILL.md from the source repository to ~/.claude/skills/data-warehouse-experimentation/SKILL.md and restart Claude Code. Both flows are detailed at claudskills.com/install/.
What does the Data Warehouse Experimentation skill do?
Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.
Is this skill free to install?
Yes. ClaudSkills is an open registry — every skill keeps its source repository's license, and manual install via copy is free. ClaudSkills Pro ($9/mo, $79/yr, or $149 one-time) adds one-click install via the desktop app and a multi-signal Quality Score.
When should I use the Data Warehouse Experimentation skill?
Use Data Warehouse Experimentation when your Claude Code task falls under the Science & Research category — specifically in the math stats area. Claude Code auto-discovers installed skills and invokes the right one based on the task description, so you can also ask Claude directly (e.g. "use Data Warehouse Experimentation" or describe the task and let Claude pick). Browse related skills at /category/science/.
What is a Claude Code skill and how does the Data Warehouse Experimentation skill fit in?
A Claude Code skill is a SKILL.md file that lives under ~/.claude/skills/<name>/ and tells the Claude Code CLI agent how to perform a specific task (instructions, prompts, allowed tools). Skills are auto-discovered at session start. Data Warehouse Experimentation is one of 67,000+ skills indexed in the open ClaudSkills catalog, classified under the Science & Research category. Learn more at /learn/what-is-a-claude-skill/.
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Claude™ is a trademark of Anthropic PBC. ClaudSkills (also referred to as Claude Skills or Claude Code Skills Catalog) is an independent community-curated registry of SKILL.md files, not affiliated with, endorsed by, or sponsored by Anthropic.
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