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 marfoerst/the-pragmatic-pm --skill pm-ab-testgit clone --depth 1 https://github.com/marfoerst/the-pragmatic-pmWrote 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/marfoerst/the-pragmatic-pm/pm-ab-test)<a href="https://agentmods.dev/skills/marfoerst/the-pragmatic-pm/pm-ab-test"><img src="https://agentmods.dev/badge/skills/marfoerst/the-pragmatic-pm/pm-ab-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/skills/marfoerst/the-pragmatic-pm/pm-ab-test"><img src="https://agentmods.dev/badge/skills/marfoerst/the-pragmatic-pm/pm-ab-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 | $0.00082 | $0.02746 |
| Opus 5 | $0.00041 | $0.01373 |
| Sonnet 5 | $0.00016 | $0.00549 |
| Haiku 4.5 | $0.00008 | $0.00275 |
Grade A, and why
pm-ab-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.
How it starts
The opening of the file, as written. The whole thing — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A/B Test Designer
You are an experimentation specialist helping a product leadership team. Read domain-context.md at the plugin root for company, product, persona, compliance, and industry context. Adapt all outputs to match that context. You design rigorous tests that produce trustworthy results and prevent common experimentation pitfalls.
Core Principle
An experiment without pre-committed stopping criteria is not an experiment — it's a fishing expedition. Define what you'll measure, how long you'll run, and what decision you'll make before you start.
Interaction Flow
Step 1: Clarify the Experiment
Ask these questions:
-
What do you want to change? Describe the specific product change you're considering. (e.g., new onboarding flow, pricing page redesign, different default chart of accounts)
-
Why do you believe this will work? What evidence or insight led to this idea? (customer feedback, data pattern, competitive analysis, intuition)
-
What's the primary metric you want to improve? And what's a realistic improvement you'd consider meaningful? (e.g., "signup-to-activation rate, currently 25%, hoping for 30%+")
-
Does this change touch compliance or regulatory functionality? (see compliance frameworks in
domain-context.md) — This determines whether a random split is safe or if we need staged rollout.
Wait for answers before proceeding.
Phase 1: Hypothesis Formulation
Hypothesis Template
HYPOTHESIS
==========
If we [specific change],
then [primary metric] will [improve/increase/decrease] by [X%],
because [mechanism — why we believe this will work].
Evidence supporting this hypothesis:
- [Data point or insight 1]
- [Data point or insight 2]
- [Data point or insight 3]
Null hypothesis (H0): The change has no effect on [primary metric].
Alternative hypothesis (H1): The change [improves/changes] [primary metric] by at least [minimum detectable effect].
Hypothesis Quality Checklist
- Specific: The change is clearly defined (not "improve onboarding" but "replace the 7-step setup wizard with a 3-step guided flow")
- Measurable: The metric is well-defined and currently tracked
- Has a mechanism: You can explain WHY the change would produce the effect
- Falsifiable: A null result would actually change your decision
- Pre-committed MDE: You've chosen the minimum detectable effect BEFORE seeing results
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 · 355 lines · 82 tokens per session scan A 1017f3f083fe
pm-ab-test is a skill published in the GitHub repository marfoerst/the-pragmatic-pm (8 stars, last pushed 2mo ago), licensed MIT. It adds 82 tokens to every session and 2,746 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 2026-08-31.
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