Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Avyayalaya/pm-skills-arsenalnpx agentmods add skills/avyayalaya/pm-skills-arsenal/metric-design-experimentationWrote 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/pm-skills-arsenal/metric-design-experimentation)<a href="https://agentmods.dev/skills/avyayalaya/pm-skills-arsenal/metric-design-experimentation"><img src="https://agentmods.dev/badge/skills/avyayalaya/pm-skills-arsenal/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/pm-skills-arsenal/metric-design-experimentation"><img src="https://agentmods.dev/badge/skills/avyayalaya/pm-skills-arsenal/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 | $0.23674 |
| Opus 5 | $0.00036 | $0.11837 |
| Sonnet 5 | $0.00014 | $0.04735 |
| Haiku 4.5 | $0.00007 | $0.02367 |
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 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.
This is a copy
94% identical to metric-design-experimentation — 84 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 — 1,552 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)
Example
Prompt: We're a B2B project management SaaS that just raised Series B. We have ~8,000 paying customers (60% SMB self-serve, 40% mid-market sales-assisted). Our board wants a North Star Metric. The CEO wants to use MRR. I think MRR is too lagging. Help me design the right metric framework.
What ships with it
2 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.
- 12d ago First seen · 1,552 lines · 72 tokens per session scan A 86a699d82ff0
metric-design-experimentation is a skill published in the GitHub repository Avyayalaya/pm-skills-arsenal (6 stars, last pushed 3mo ago), licensed MIT. It adds 72 tokens to every session and 23,674 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to metric-design-experimentation, differing in 84 lines, and is treated as a copy.
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metric-design-experimentation
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Senior PM agent with 6 knowledge domains, 30+ frameworks, 12 templates, and 32 SaaS metrics with formulas. Pure Markdown, zero scripts.
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dummy-dataset
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.