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 lenar-amirov/product-pipeline-public --skill experiment-designgit clone --depth 1 https://github.com/lenar-amirov/product-pipeline-publicWrote 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/lenar-amirov/product-pipeline-public/experiment-design)<a href="https://agentmods.dev/skills/lenar-amirov/product-pipeline-public/experiment-design"><img src="https://agentmods.dev/badge/skills/lenar-amirov/product-pipeline-public/experiment-design/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/lenar-amirov/product-pipeline-public/experiment-design"><img src="https://agentmods.dev/badge/skills/lenar-amirov/product-pipeline-public/experiment-design.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 | $0.00093 | $0.00693 |
| Opus 5 | $0.00046 | $0.00347 |
| Sonnet 5 | $0.00019 | $0.00139 |
| Haiku 4.5 | $0.00009 | $0.00069 |
Grade A, and why
experiment-design 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 12d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Design — /design-ab-test engine
Input: the top solution from solution-scoring and its riskiest
assumption (from the assumption map). The experiment tests THAT, not
"the feature in general".
0. Is an AB test even the right instrument?
Cheaper first: a concept test (user-testing skill) kills desirability risks in days; a fake-door sizes demand; an ops pilot tests feasibility. AB test is for causal measurement of the metric effect at scale — expensive and slow; don't default to it. State the choice and why.
1. AB test parameters (with the analyst)
- Hypothesis in the falsifiable form: "If [change] for [segment], then [primary metric] moves from [baseline] by ≥[MDE], because [mechanism]." Baseline comes from the registry/CONTEXT.md, never from memory.
- Primary metric — exactly one; the same definition the dashboard uses (metric-definition mismatches are how gates die — see /challenge).
- MDE — the smallest business-meaningful effect (the shipping floor), NOT the smallest statistically detectable one and not the effect you hope for. A result can be significant and still worthless — sizing the test to detect 0.2% when only 2%+ changes the roadmap wastes weeks.
- Sample size & duration — from baseline, MDE, power 0.8, α 0.05; respect weekly seasonality (full weeks only). If the segment is too small for the MDE — say the test is infeasible and go back to instrument choice.
- Guardrails — metrics that must NOT degrade (engagement, latency, support load) with explicit thresholds.
- Decision criteria are pre-registered: ship / iterate / kill thresholds written into this doc BEFORE launch — post-hoc goalpost moving is how p-hacking enters product work. Division of power: the primary metric decides ship/no-ship; a guardrail breach pauses the test and triggers investigation — it neither silently vetoes nor gets explained away after the fact.
2. Tracking readiness
Verify the events the test needs actually exist (tech-spec §tracking). A test on unmeasured behavior is a dependency, not a test — create it in status.json with the analyst as owner.
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.
- 12d ago First seen · 55 lines · 93 tokens per session scan A a351eedb8277
experiment-design is a skill published in the GitHub repository lenar-amirov/product-pipeline-public (12 stars, last pushed 22d ago), licensed MIT. It adds 93 tokens to every session and 693 once invoked, about $0.0005 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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