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.
git clone --depth 1 https://github.com/unixcrh/phuryn-pm-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/commands/unixcrh/phuryn-pm-skills/analyze-test)<a href="https://agentmods.dev/commands/unixcrh/phuryn-pm-skills/analyze-test"><img src="https://agentmods.dev/badge/commands/unixcrh/phuryn-pm-skills/analyze-test/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/commands/unixcrh/phuryn-pm-skills/analyze-test"><img src="https://agentmods.dev/badge/commands/unixcrh/phuryn-pm-skills/analyze-test.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 | $0.00020 | $0.00945 |
| Opus 5 | $0.00010 | $0.00473 |
| Sonnet 5 | $0.00004 | $0.00189 |
| Haiku 4.5 | $0.00002 | $0.00094 |
Grade A, and why
analyze-test 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 11d 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.
This is a copy
100% identical to analyze-test — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analyze-test -- A/B Test Analysis
Evaluate experiment results with statistical rigor and translate findings into a clear product decision: ship, extend, or stop.
Invocation
/analyze-test Control: 4.2% conversion (n=5000), Variant: 4.8% conversion (n=5100)
/analyze-test [upload a CSV of test results]
/analyze-test [screenshot from your experimentation platform]
Workflow
Step 1: Accept Test Data
Accept in any format:
- Summary statistics (conversion rates, sample sizes per variant)
- Raw event data (CSV with user_id, variant, converted, timestamp)
- Screenshot from an experimentation platform (Optimizely, LaunchDarkly, etc.)
- Description of the experiment and results
Step 2: Validate Test Design
Before analyzing results, check:
- Was sample size sufficient? (run a power analysis)
- Was the test run long enough? (capture weekly cycles, minimum 1-2 business cycles)
- Was randomization clean? (check for sample ratio mismatch)
- Were there any external factors during the test period?
Flag issues if found — results from a flawed test can be misleading.
Step 3: Analyze Results
Apply the ab-test-analysis skill:
- Statistical significance: Calculate p-value and confidence interval
- Effect size: Absolute and relative difference between variants
- Practical significance: Is the effect large enough to matter for the business?
- Confidence interval: What's the range of plausible true effects?
- Segment analysis: If data allows, check for differential effects by user segment
Step 4: Generate Analysis
## A/B Test Analysis: [Test Name]
**Date**: [today]
**Test duration**: [X days/weeks]
**Total sample**: [N users]
### Results Summary
| Variant | Sample | Metric | Rate | 95% CI |
|---------|--------|--------|------|--------|
| Control | [n] | [metric] | [X%] | [X% - Y%] |
| Variant | [n] | [metric] | [X%] | [X% - Y%] |
### Statistical Analysis
- **Relative lift**: [+X%] ([CI range])
- **P-value**: [X]
- **Statistically significant**: [Yes/No] at 95% confidence
- **Minimum detectable effect**: [X%] (what the test was powered to detect)
### Sample Size Check
- **Required sample**: [N] per variant (for [X%] MDE at 80% power)
- **Actual sample**: [N] per variant
- **Verdict**: [Sufficiently powered / Underpowered / Overpowered]
### Decision
**Recommendation: [SHIP / EXTEND / STOP]**
[Clear explanation of why, considering both statistical and practical significance]
### Business Impact Estimate
If shipped to 100% of users:
- **Expected impact**: [metric change per month/quarter]
- **Revenue impact**: [if applicable]
- **Confidence**: [How certain we are about this estimate]
### Caveats
- [Any concerns about the test validity]
- [Segments where results differ]
- [Novelty effects or other biases to consider]
### Follow-Up
- [What to test next based on learnings]
- [Monitoring plan if shipping the variant]
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.
- 11d ago First seen · 110 lines · 20 tokens per session scan A 562c6aa10b19
analyze-test is a command published in the GitHub repository unixcrh/phuryn-pm-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 20 tokens to every session and 945 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to analyze-test, differing in 0 lines, and is treated as a copy.
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