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 assimovt/productskills --skill metrics-frameworkgit clone --depth 1 https://github.com/assimovt/productskillsWrote 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/assimovt/productskills/metrics-framework)<a href="https://agentmods.dev/skills/assimovt/productskills/metrics-framework"><img src="https://agentmods.dev/badge/skills/assimovt/productskills/metrics-framework/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/assimovt/productskills/metrics-framework"><img src="https://agentmods.dev/badge/skills/assimovt/productskills/metrics-framework.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 | $0.00054 | $0.00705 |
| Opus 5 | $0.00027 | $0.00352 |
| Sonnet 5 | $0.00011 | $0.00141 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
metrics-framework 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.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Define metrics that drive decisions, not dashboards that collect dust. Every metric must answer: "If this number changes, what will we do differently?" If nothing changes, it's a vanity metric.
North Star Metric
One metric that captures the core value your product delivers to customers. Not revenue (that's an output). Not signups (that's vanity). The North Star reflects the moment customers get value.
Examples:
- Slack: Messages sent in channels with 3+ members
- Spotify: Time spent listening
- Airbnb: Nights booked
Test: If this metric goes up, does the company sustainably grow? If yes, it's your North Star.
Input/Output Tree
Build a tree connecting your North Star to actionable inputs:
North Star: Weekly active teams with 3+ collaborators
|
+-- Acquisition: New signups/week
| +-- Website visitors
| +-- Signup conversion rate
|
+-- Activation: % who invite a teammate in 7 days
| +-- Onboarding completion rate
| +-- Time to first shared project
|
+-- Engagement: Documents edited per active team/week
| +-- Feature adoption (comments, mentions, sharing)
| +-- Return frequency
|
+-- Retention: % active at day 30
+-- Weekly return rate
+-- Feature breadth used
Each leaf is a metric a team can directly influence. The tree shows HOW inputs drive the North Star.
Counter-Metrics
EVERY metric gets a counter-metric. This prevents gaming and ensures you're not optimizing one thing at the expense of another.
| Metric | Counter-Metric |
|---|---|
| Signup conversion rate | Activation rate (don't lower the bar to get signups) |
| Time to first value | 30-day retention (don't rush users past learning) |
| Feature adoption | Task completion rate (don't push features that confuse) |
| Revenue per user | Churn rate (don't squeeze users into leaving) |
If the metric improves but the counter-metric degrades, you've optimized the wrong thing.
Guidelines
- CRITICAL: ALWAYS pair every metric with a counter-metric. No exceptions.
- NEVER use vanity metrics: total signups, page views, total users, app downloads. These only go up and tell you nothing.
- NEVER track more than 5-7 metrics actively. If your dashboard has 20 charts, nobody looks at any of them.
- ALWAYS define metrics with exact formulas. "Activation rate" means nothing. "Percentage of new signups who invite at least one teammate within 7 days of account creation" is a metric.
- ALWAYS set a baseline before setting a target. You can't improve what you haven't measured.
- NEVER set targets without understanding the current number and why it's where it is.
- ALWAYS measure outcomes (user behavior) over outputs (features shipped).
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 · 75 lines · 54 tokens per session scan A b003b7306caf
metrics-framework is a skill published in the GitHub repository assimovt/productskills (68 stars, last pushed 6mo ago), licensed MIT. It adds 54 tokens to every session and 705 once invoked, about $0.0003 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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