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 reatlat/fullstory-claude-plugin --skill experiment-analyzergit clone --depth 1 https://github.com/reatlat/fullstory-claude-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/reatlat/fullstory-claude-plugin/experiment-analyzer)<a href="https://agentmods.dev/skills/reatlat/fullstory-claude-plugin/experiment-analyzer"><img src="https://agentmods.dev/badge/skills/reatlat/fullstory-claude-plugin/experiment-analyzer/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/reatlat/fullstory-claude-plugin/experiment-analyzer"><img src="https://agentmods.dev/badge/skills/reatlat/fullstory-claude-plugin/experiment-analyzer.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 | $0.00052 | $0.01218 |
| Opus 5 | $0.00026 | $0.00609 |
| Sonnet 5 | $0.00010 | $0.00244 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
experiment-analyzer 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 10d 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Analyzer
Analyze A/B tests and experiments — measure impact, check statistical validity, break down results by segment, and tell you which variant won (and by how much).
When to Use
- "Did the new checkout design improve conversion?"
- "Analyze the 'blue button vs green button' experiment"
- "Is the pricing page experiment statistically significant?"
- "Break down the experiment results by device type"
- "Which variant performed better for enterprise users?"
- "Should we ship the new onboarding flow?"
Mental Model
An experiment compares two or more variants against a control. The goal is to determine:
- Direction: Which variant performed better?
- Magnitude: By how much? (+5%? +20%?)
- Confidence: Is the difference real or noise? (statistical significance)
- Segments: Does it work for everyone, or only certain users?
- Side effects: Did the variant improve conversion but increase errors?
Workflow
Step 1: Define the experiment
Ask the user:
- What's being tested? (e.g., checkout flow redesign)
- What's the success metric? (e.g., checkout completion rate)
- When did the experiment start? (so you can scope the time range)
- How are users split? (50/50 random? By user property? By feature flag?)
If users are split by a user property (e.g., experiment_group = "control" or "variant"), use segments. If split by a feature flag that maps to a custom event, filter by that event.
Step 2: Build metrics per variant
For user-property splits (experiment_group property):
fullstory:build_segment("users in experiment group 'control'") → seg_control
fullstory:build_segment("users in experiment group 'variant'") → seg_variant
fullstory:build_metric(query="checkout completion rate", output_type="single_number")
fullstory:update_metric(metric_id, segment_id=seg_control)
fullstory:compute_metric(metric_id) → control_result
fullstory:update_metric(metric_id, segment_id=seg_variant)
fullstory:compute_metric(metric_id) → variant_result
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.
- 10d ago First seen · 118 lines · 52 tokens per session scan A 546ccd8f0fe3
experiment-analyzer is a skill published in the GitHub repository reatlat/fullstory-claude-plugin (62 stars, last pushed 28d ago), licensed MIT. It adds 52 tokens to every session and 1,218 once invoked, about $0.0003 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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