Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rampstackco/claude-skills --skill data-warehouse-experimentationgit clone --depth 1 https://github.com/rampstackco/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rampstackco/claude-skills/data-warehouse-experimentation)<a href="https://agentmods.dev/skills/rampstackco/claude-skills/data-warehouse-experimentation"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/data-warehouse-experimentation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rampstackco/claude-skills/data-warehouse-experimentation"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/data-warehouse-experimentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00157 | $0.06083 |
| Opus 5.5 | $0.00063 | $0.02433 |
| Sonnet 5.5 | $0.00031 | $0.01217 |
| Haiku 4.5 | $0.00016 | $0.00608 |
Grade A, and why
data-warehouse-experimentation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 396 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Warehouse Experimentation
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.
Most companies that run experiments at scale use a dedicated platform. Statsig, Optimizely, LaunchDarkly with experimentation, PostHog, Amplitude Experiment. The platforms are good. They handle assignment, instrumentation, and analysis in one product, and the SQL-savvy data team does not have to reinvent the variance reduction wheel.
There is a different operational model that mature data teams increasingly choose: warehouse-native experimentation. Assignment happens in code or via feature flags. Exposure events fire to the warehouse like any other event. Metrics are defined as dbt models. Statistical analysis runs as SQL or in a Python notebook against warehouse data. The "experiment platform" is just your existing data stack.
This skill covers when warehouse-native is the right call, the architecture, and the specific techniques that make it work: assignment patterns, exposure logging discipline, metric definitions in dbt, t-tests and CUPED in SQL, sequential testing, and the pitfalls that take down homegrown setups.
When to use this skill: deciding between platform vs warehouse-native, building a warehouse-native experiment infrastructure, auditing an existing one, or running a specific experiment when the platform of record cannot handle a custom metric or segmentation.
What this skill is for
This skill spans the operational execution model for warehouse-native experimentation. It does not replace the methodology and interpretation skills; it composes with them.
experiment-designcovers methodology: hypotheses, sample size, randomization unit, primary metric. Tool-agnostic. Read it first to design the experiment correctly regardless of where it runs.experimentation-analyticscovers interpretation: confidence intervals, p-values, effect size, decision frameworks. Tool-agnostic. Read it when results land.experimentation-platform-orchestratorcovers the platform-vs-warehouse decision in detail. Read it to decide whether to use a platform or this skill.feature-flaggingcovers assignment infrastructure when not running through a platform. Read it for the flag-management discipline that this skill assumes.- This skill (
data-warehouse-experimentation) covers the operational execution: SQL-based assignment, exposure logging, metric definitions in dbt, statistical analysis in SQL or Python, variance reduction, sequential testing.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- README.md 371 B
- references/assignment-and-exposure-patterns.md 7.9 KB
- references/common-pitfalls.md 9.8 KB
- references/metric-definitions-in-dbt.md 7.9 KB
- references/power-analysis-calculations.md 8.0 KB
- references/sequential-testing-patterns.md 7.6 KB
- references/statistical-analysis-templates.md 7.4 KB
- references/variance-reduction-techniques.md 8.9 KB
- references/warehouse-vs-platform-decision.md 7.8 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 396 lines · 157 tokens per session scan A e3aa63517c34
data-warehouse-experimentation is a skill published in the GitHub repository rampstackco/claude-skills (992 stars, last pushed 5d ago), licensed MIT. It adds 157 tokens to every session and 6,083 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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