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 hollandkevint/data-product-operator --skill metrics-definitiongit clone --depth 1 https://github.com/hollandkevint/data-product-operatorWrote 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/hollandkevint/data-product-operator/metrics-definition)<a href="https://agentmods.dev/skills/hollandkevint/data-product-operator/metrics-definition"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/metrics-definition/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/hollandkevint/data-product-operator/metrics-definition"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/metrics-definition.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 | $0.00071 | $0.00949 |
| Opus 5 | $0.00036 | $0.00475 |
| Sonnet 5 | $0.00014 | $0.00190 |
| Haiku 4.5 | $0.00007 | $0.00095 |
Grade A, and why
metrics-definition 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Core Problem
"47 dashboards and no answers." The failure mode is building metrics without agreeing on what they mean. "Revenue" means three different things to three different teams. "Active users" has no agreed-upon time window. Fix the definitions before building the dashboards.
Metric Definition Template
Every metric must specify:
- Name: Human-readable, follows naming convention (see below)
- Business definition: One sentence a non-technical person understands
- SQL logic: Exact calculation including numerator, denominator, and filters
- Grain: What level is this calculated at? (daily, weekly, per-user, per-account)
- Time window: What period does this cover? (trailing 7 days, calendar month, since signup)
- Dimensional attributes: What can you slice this by? (region, product line, customer segment)
- Owner: Who maintains this definition?
- Update cadence: How often does this refresh?
- Known edge cases: What situations produce unexpected results?
NEVER define a metric without specifying grain and time window. "Monthly active users" means nothing until you define what "active" means and whether "monthly" is calendar month or trailing 30 days.
Outcome Metric Trees
Connect metrics from business outcomes down to leading indicators:
Business outcome: Reduce hospital readmissions 10%
Product outcome: Clinical decisions made 3x faster
Feature outcome: Risk scores updated in real-time
Leading indicator: Query latency under 1 second
Every metric at a lower level should causally influence the level above. If you can't draw the causal link, the metric doesn't belong in the tree.
Trust Metrics
Alongside performance metrics, track trust:
- Data accuracy rate: Percentage of values matching gold standard (target: 99.9%)
- Query response time: P95 latency for consumer queries (target: <3 seconds)
- Data freshness: Time between source update and availability (target: within SLA)
- Support response time: How fast you resolve data questions (target: <24 hours)
- Incident count: Zero algorithmic bias incidents, zero data breaches
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 · 89 lines · 71 tokens per session scan A 801e3c0de54d
metrics-definition is a skill published in the GitHub repository hollandkevint/data-product-operator (3 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 949 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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