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 Avyayalaya/agent-prime --skill metric-design-experimentationgit clone --depth 1 https://github.com/Avyayalaya/agent-primeWrote 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/avyayalaya/agent-prime/metric-design-experimentation)<a href="https://agentmods.dev/skills/avyayalaya/agent-prime/metric-design-experimentation"><img src="https://agentmods.dev/badge/skills/avyayalaya/agent-prime/metric-design-experimentation/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/avyayalaya/agent-prime/metric-design-experimentation"><img src="https://agentmods.dev/badge/skills/avyayalaya/agent-prime/metric-design-experimentation.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 | $0.00072 | $0.21692 |
| Opus 5 | $0.00036 | $0.10846 |
| Sonnet 5 | $0.00014 | $0.04338 |
| Haiku 4.5 | $0.00007 | $0.02169 |
Grade A, and why
metric-design-experimentation 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- metric-design-experimentation — 94% identical, 84 lines differ
How it starts
The opening of the file, as written. The whole thing — 1,438 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Produce a complete Measurement Framework — metric hierarchy (North Star → L1 → L2 → input), leading/lagging indicator pairs with temporal lag classification, counter-metric design that resists Goodhart's Law, experiment plans with statistical validity, and retention cohort methodology. The output is not a dashboard mockup or a list of KPIs — it is a metric engineering system: instrumented to detect problems early, paired to resist gaming, and validated causally. The artifact a PM cannot produce unaided.
When to Use / When NOT to Use
Use this skill when:
- Launching a new product or feature and need to define what success looks like before building
- Designing an A/B test or experiment plan with proper statistical rigor
- An existing metric feels "off" — you suspect proxy divergence, gaming, or Simpson's paradox
- Building a metric hierarchy for a team or org (North Star → team-level → input metrics)
- Setting up retention cohort analysis to detect PMF erosion early
- Evaluating whether a metric improvement is real or an artifact of denominator shift
Do NOT use this skill when:
- You need SaaS finance metric definitions (MRR, ARR, CAC, LTV formulas → use a finance metrics reference)
- You need dashboard layout or visualization design (that's a BI/design task)
- You need to analyze experiment results that already exist (use the computation scripts directly)
- You need customer research methodology (→ Discovery & Research skill — that's primary research, this is measurement design)
Anti-inputs (what this skill does NOT handle):
- Finance metric calculation formulas (→ SaaS finance reference skills)
- Data pipeline architecture (→ engineering)
- Dashboard UI design (→ BI tooling)
- Customer interview design (→ Discovery & Research skill)
Format Rules
These rules apply to every output from this skill. They are mandatory, not optional.
Rule 1: Take Positions with Calibrated Confidence
Never use weasel words in conclusions. Replace "likely," "may," "could," "seems" with explicit confidence levels:
- H (>70%) — Strong evidence (validated in your own data)
- M (40-70%) — Mixed or moderate evidence; direction is probable
- L (<40%) — Evidence is thin or conflicting; treat as hypothesis
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 1,438 lines · 72 tokens per session scan A 4f0b4e632be9
metric-design-experimentation is a skill published in the GitHub repository Avyayalaya/agent-prime (8 stars, last pushed 3mo ago), licensed MIT. It adds 72 tokens to every session and 21,692 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.
Other skills, from other repositories
metric-design-experimentation
Use when designing a metric framework, selecting a North Star metric, building a metric decomposition tree, designing A/B experiments, setting up retention cohort analysis, or diagnosing whether a metric is being gamed. Encodes NSM rubrics, Goodhart's Law countermeasures, statistical validity for PMs, and retention…
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Validating A/B test implementations including traffic splitting accuracy, statistical significance calculation, metric tracking, and experiment cleanup.
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compile-recursive
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