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 magnus919/agent-skills --skill product-analytics-and-measurementgit clone --depth 1 https://github.com/magnus919/agent-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/skills/magnus919/agent-skills/product-analytics-and-measurement)<a href="https://agentmods.dev/skills/magnus919/agent-skills/product-analytics-and-measurement"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/product-analytics-and-measurement/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/magnus919/agent-skills/product-analytics-and-measurement"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/product-analytics-and-measurement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00129 | $0.02250 |
| Opus 5 | $0.00064 | $0.01125 |
| Sonnet 5 | $0.00026 | $0.00450 |
| Haiku 4.5 | $0.00013 | $0.00225 |
Grade A, and why
product-analytics-and-measurement 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 9d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Analytics and Measurement
A dedicated operating method for turning intended product outcomes into observable, governed evidence. This skill covers the full measurement lifecycle: from defining what success looks like through metric trees and leading/lagging indicators, to specifying how that success is tracked through event taxonomies and tracking plans, to verifying that the instrumentation actually captures what it claims.
Loading Guide
Load this skill when the task involves any of:
| Trigger | What to load |
|---|---|
| Define product outcomes, North Star, or success metrics | SKILL.md + references/metric-tree.md |
| Design an event taxonomy or tracking plan | SKILL.md + templates/tracking-plan.md |
| Audit or QA instrumentation quality | SKILL.md + templates/instrumentation-qa-checklist.md |
| Build product funnels, path analysis, or cohort definitions | SKILL.md + references/metric-tree.md |
| Set up a dashboard contract or measurement governance | SKILL.md + templates/outcome-review.md |
| Resolve conflicting metric definitions across teams | SKILL.md + references/discovery-brief.md (ownership boundaries) |
| Design a privacy-aware measurement strategy | SKILL.md |
When to Use
- You need to define measurable outcomes, metric trees, and leading/lagging indicators for a product (SaaS, internal tool, public service, or consumer).
- You need a tracking plan that specifies events, properties, identity resolution, session boundaries, data quality rules, and ownership.
- You need to verify that existing instrumentation produces trustworthy data.
- You need a dashboard contract that pins metric definitions, sources, refresh cadences, and ownership.
- You need a product outcome review cadence that ties measurements back to decisions.
When Not to Use
This skill does not replace:
- Data architecture — schema design, storage selection, data modeling at the infrastructure level belong to
../data-architect/SKILL.md. - Data engineering — pipeline implementation, ETL/ELT, dbt models, and data quality monitoring at the operational level belong to
../data-engineering/SKILL.md. - Statistical inference — experiment design, hypothesis testing, causal inference, and model selection belong to
../data-scientist/SKILL.md. - Observability — system health, latency, error budgets, and infrastructure monitoring belong to
../site-reliability-engineering/SKILL.md. - Business intelligence — dashboard building, report generation, and data visualization as an end in itself. This skill governs the measurement contract and metric definitions that feed BI, not the BI layer itself.
What ships with it
7 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.
- 9d ago First seen · 167 lines · 129 tokens per session scan A 7b8b698cf4fe
product-analytics-and-measurement is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 129 tokens to every session and 2,250 once invoked, about $0.0006 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 2026-09-03.
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