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 uthumany/uthy-legacy-os --skill experiment-analysisgit clone --depth 1 https://github.com/uthumany/uthy-legacy-osWrote 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/uthumany/uthy-legacy-os/experiment-analysis)<a href="https://agentmods.dev/skills/uthumany/uthy-legacy-os/experiment-analysis"><img src="https://agentmods.dev/badge/skills/uthumany/uthy-legacy-os/experiment-analysis/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/uthumany/uthy-legacy-os/experiment-analysis"><img src="https://agentmods.dev/badge/skills/uthumany/uthy-legacy-os/experiment-analysis.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00029 | $0.00758 |
| Opus 5.5 | $0.00012 | $0.00303 |
| Sonnet 5.5 | $0.00006 | $0.00152 |
| Haiku 4.5 | $0.00003 | $0.00076 |
Grade A, and why
experiment-analysis 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 2mo 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Analysis
Overview
Running the experiment is only half the work — analyzing the results correctly is where the value lives. This skill helps you interpret experiment data, avoid common statistical pitfalls, and make confident go/kill decisions.
When to Use
- An A/B test or experiment has concluded
- You need to decide whether to ship, iterate, or kill
- You want to understand not just the outcome but WHY it happened
- Don't use for: pre-experiment planning (use experiment-design skill), exploratory data analysis (different approach)
Instructions
1. Check Experiment Validity
Before analyzing results, verify:
- Sample size: Did you reach the required sample size?
- Duration: Did the experiment run long enough? (minimum 1 full business cycle, usually 1-2 weeks)
- Novelty effect: Did the effect change over time? (compare first few days vs. last few days)
- Sample ratio mismatch: Are the control/treatment group sizes as expected?
- Data quality: Any tracking issues, bugs, or outliers?
2. Read the Primary Metric
- Statistical significance: p-value < 0.05 (or your pre-defined threshold)
- Effect size: How big is the impact? (absolute and relative)
- Confidence interval: What's the range of plausible effects?
- Practical significance: Even if statistically significant, is this effect worth building?
3. Analyze Guardrail Metrics
What metrics should NOT have degraded?
- Check ALL guardrail metrics for statistically significant negative changes
- A negative guardrail doesn't automatically kill the experiment, but it requires discussion
- Trade-off decision: Is the primary lift worth the guardrail degradation?
4. Segment Analysis
- Break down results by key segments (new vs. existing users, platform, plan type)
- A negative overall result might hide a strong positive in a specific segment
- Be cautious of over-segmenting (too many slices = false positives)
5. Qualitative Signals
- What did user feedback say during the experiment?
- Any support tickets related to the change?
- Any unexpected user behavior observed?
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.
- 2mo ago First seen · 82 lines · 29 tokens per session scan A 6e14f087b463
experiment-analysis is a skill published in the GitHub repository uthumany/uthy-legacy-os (5 stars, last pushed 5mo ago), licensed MIT. It adds 29 tokens to every session and 758 once invoked, about $0.0001 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 2026-08-31.
experiment-analysis compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| experiment-analysisuthumany/uthy-legacy-os | Skill | 5 | 5mo ago | MIT | A | 29 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |