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 agentmods add skills/rampstackco/claude-skills/experimentation-analyticsnpx skills add rampstackco/claude-skills --skill experimentation-analyticsgit 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/experimentation-analytics)<a href="https://agentmods.dev/skills/rampstackco/claude-skills/experimentation-analytics"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/experimentation-analytics.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00179 | $0.07295 |
| Opus 5 | $0.00089 | $0.03648 |
| Sonnet 5 | $0.00036 | $0.01459 |
| Haiku 4.5 | $0.00018 | $0.00730 |
Grade A, and why
experimentation-analytics 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 6d 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 — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experimentation Analytics
A data-team-mentor's playbook for interpreting experiment results without fooling yourself.
The result panel is the moment-of-truth for an experiment. The numbers on it determine whether you ship, kill, or iterate. They also expose every shortcut taken in the design phase: an underpowered test produces wide confidence intervals; a peeked test produces a too-narrow p-value; a ratio metric without delta-method correction produces overconfident lift estimates. Most ship-the-wrong-thing decisions trace back to misreading the result panel.
This skill is the discipline that prevents misreading. It assumes the experiment was designed well (see the experiment-design skill). It assumes the platform's results panel is technically correct (most modern platforms are; some older ones are not). It assumes you can read a number off a screen. The hard part is knowing what each number actually means and what it does not, and that is what is here.
When to use this skill: any time you are reading an experiment result panel and about to make a ship, kill, or iterate decision.
What this skill is for
This skill covers result interpretation, the statistical concepts that make the numbers trustworthy, and the dashboard reconciliation work that prevents executive-level confusion when the experiment number does not match the BI number. The audience is product managers and data analysts who read experiment results together and need a shared vocabulary that does not paper over the dangerous parts of statistics.
Companion skills cover the adjacent territory. The experiment-design skill covers pre-experiment thinking: hypothesis, sample size, MDE, segments, what NOT to test. Read it before designing the test; read this skill when reading the result. The feature-flagging skill covers the operational mechanics of flag management, environment promotion, and stale-flag cleanup. Together the three skills span the experimentation lifecycle from intent through interpretation. For platform-specific MCP commands, consult the chosen platform's docs; Statsig, PostHog, Optimizely, GrowthBook, Eppo, Amplitude, and Kameleoon all expose rich analytics surfaces that this skill informs how to read.
What ships with it
7 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.
- references/analytics-platform-comparison.md 9.9 KB
- references/common-interpretation-failures.md 14 KB
- references/confidence-interval-cheatsheet.md 7.0 KB
- references/dashboard-vs-experiment-reconciliation.md 7.2 KB
- references/p-value-interpretation-guide.md 7.9 KB
- references/result-presentation-templates.md 9.0 KB
- references/statistical-method-reference.md 12 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.
- 6d ago First seen · 334 lines · 179 tokens per session scan A 6b84055d9897
experimentation-analytics is a skill published in the GitHub repository rampstackco/claude-skills (817 stars, last pushed 8d ago), licensed MIT. It adds 179 tokens to every session and 7,295 once invoked, about $0.0009 per session on Opus 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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