experiment

experiment is a skill for Claude Code from ai-analyst-lab/ai-analyst-plugin. It costs 89 tokens per session (2,096 once invoked), scanned A, original, MIT.

A workflow for planning, analyzing, explaining, and monitoring experiments such as A/B tests, where different user groups receive different versions of a product. It also covers sample-size planning and the decision to ship or not ship a change.

In plain words
What is it for?
Use it to design an experiment, estimate the needed sample size, analyze results, assess statistical significance, interpret findings, and monitor a launch decision.
Why use it?
It organizes the full experiment process so decisions are based on measured differences between treatment and control groups rather than guesswork.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the ai-analyst-plus plugin — 44 skills, 1 command, 13 agents shipped together

Install

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.

agentmods
npx agentmods add skills/ai-analyst-lab/ai-analyst-plugin/experiment
Any agent
npx skills add ai-analyst-lab/ai-analyst-plugin --skill experiment
Clone the repo
git clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-plugin

Made for: Claude Code.

Or install ai-analyst-plus, the plugin that ships this one along with the rest of its 44 skills, 1 command, 13 agents.

Wrote 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.

agentmods badge for experiment

README.md
[![agentmods](https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/experiment.svg)](https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/experiment)
Your own site
<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/experiment"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/experiment.svg" alt="Measured on agentmods" height="20"></a>
Per session 89 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,096 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00089 $0.02096
Opus 5 $0.00044 $0.01048
Sonnet 5 $0.00018 $0.00419
Haiku 4.5 $0.00009 $0.00210

Measured 6d ago against content hash 3ac1dd1d22ff, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

experiment 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.

The scan reads SKILL.md. This mod also ships 9 executable files (scripts/experiment_stats/__init__.py, scripts/experiment_stats/ab_tests.py, scripts/experiment_stats/bayesian.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

ai-analyst-plus/skills/experiment/SKILL.md · 185 lines

How it starts

The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.

If the skill install path cannot be resolved (some sandboxed environments): read the script file(s) from this skill, write a copy into a scripts/ folder inside the working folder, and run from there. The scripts are self-contained.

Skill: /experiment — OpenXP Experimentation Platform

Purpose

Multi-mode skill for the full experiment lifecycle — from design through analysis to ship/no-ship decision. Orchestrates the experiment plugin agents and calls the coded statistical library bundled in this skill at scripts/experiment_stats/ instead of improvising Python.

Using the bundled library: add this skill's scripts/ directory to sys.path, then import, e.g.

import sys
sys.path.insert(0, "<path to this skill>/scripts")  # the scripts/ dir next to this SKILL.md
from experiment_stats import srm_check

Requires pandas, numpy, and scipy; power.py and corrections.py also need statsmodels (install it in the sandbox if missing).

When to Use

Invoke as /experiment [mode] or trigger on experiment-related intents:

  • "I want to run an experiment"
  • "Analyze this A/B test"
  • "Did this experiment work?"
  • "What's the power for this test?"

Modes

/experiment design

Purpose: Create a pre-registered experiment config. Agent: the experiment-designer plugin agent Flow:

  1. Run Experiment Brief skill to capture hypothesis, north star, guardrails
  2. Invoke Experiment Designer agent
  3. Output: experiments/{slug}/experiment.yaml (from templates/experiment.yaml) Checkpoint: Config review (Type B — skippable with --just-do-it)

/experiment power

Purpose: Power analysis + duration estimation. Flow:

  1. Read experiments/{slug}/experiment.yaml for metric type, baseline, MDE
  2. Call the bundled power module (scripts/experiment_stats/power.py):
    • Proportion metric → power_proportion(baseline_rate, mde)
    • Continuous metric → power_mean(baseline_mean, baseline_std, mde)
  3. Call duration_estimate(total_sample, daily_traffic, allocation)
  4. Update experiment.yaml with computed values (sample_size, duration, viable)
  5. If NOT_VIABLE → suggest /causal select as alternative Checkpoint: Power viability (Type C — NOT_VIABLE fires mandatory checkpoint)

Read the full file on GitHub · 185 lines

Changes

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.

  1. 6d ago First seen · 185 lines · 89 tokens per session scan A 3ac1dd1d22ff

Subscribe to this mod's changes

experiment is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 10d ago), licensed MIT. It adds 89 tokens to every session and 2,096 once invoked, about $0.0004 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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