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/ai-analyst-lab/ai-analyst-plugin/experimentnpx skills add ai-analyst-lab/ai-analyst-plugin --skill experimentgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/experiment)<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>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 | $0.00089 | $0.02096 |
| Opus 5 | $0.00044 | $0.01048 |
| Sonnet 5 | $0.00018 | $0.00419 |
| Haiku 4.5 | $0.00009 | $0.00210 |
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.
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 — 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:
- Run Experiment Brief skill to capture hypothesis, north star, guardrails
- Invoke Experiment Designer agent
- Output:
experiments/{slug}/experiment.yaml(fromtemplates/experiment.yaml) Checkpoint: Config review (Type B — skippable with --just-do-it)
/experiment power
Purpose: Power analysis + duration estimation. Flow:
- Read
experiments/{slug}/experiment.yamlfor metric type, baseline, MDE - 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)
- Proportion metric →
- Call
duration_estimate(total_sample, daily_traffic, allocation) - Update
experiment.yamlwith computed values (sample_size, duration, viable) - If NOT_VIABLE → suggest
/causal selectas alternative Checkpoint: Power viability (Type C — NOT_VIABLE fires mandatory checkpoint)
What ships with it
11 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.
- scripts/experiment_stats/__init__.py 2.7 KB runs code
- scripts/experiment_stats/ab_tests.py 11 KB runs code
- scripts/experiment_stats/bayesian.py 10 KB runs code
- scripts/experiment_stats/corrections.py 1.9 KB runs code
- scripts/experiment_stats/effect_size.py 2.4 KB runs code
- scripts/experiment_stats/power.py 12 KB runs code
- scripts/experiment_stats/sequential.py 7.1 KB runs code
- scripts/experiment_stats/srm.py 5.8 KB runs code
- scripts/experiment_stats/variance_reduction.py 5.8 KB runs code
- templates/experiment-report.md 1.8 KB
- templates/experiment.yaml 3.6 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 · 185 lines · 89 tokens per session scan A 3ac1dd1d22ff
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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