metrics-framework-dcd277 — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited metrics-framework-dcd277 (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
This skill builds a complete metrics framework tailored to a product or business. It connects the North Star metric to actionable leading indicators, making it clear which metrics to track, which to optimise, and how they relate to each other.
Ask the user for these if not provided:
If no framework preference is given, recommend the best fit based on stage and business model.
If a professional-brain (brain/) exists, use it before asking:
context.md for the metric definitions the org already agreed on (reuse them — don't silently redefine a metric) and knowledge/strategy.md for what the business is optimising for.knowledge/, and any target-setting decision to decisions/, each provenance-tagged so a [hunch] target isn't treated as a committed goal.Explain in 2–3 sentences why you're recommending this framework for their context.
[Metric Name]: [Definition — exactly what is measured and how]
Why this is the right North Star for this business: [2–3 sentences. It should reflect customer value delivered, not just revenue or activity. Explain what behaviour it captures and why maximising it correlates with long-term business health.]
How to measure it: [Formula or data source] Current baseline: [Leave as [ADD BASELINE] for user to fill] Target: [Leave as [ADD TARGET] for user to fill]
Show how supporting metrics roll up to the North Star. Format as a hierarchy:
[North Star Metric]
├── [Driver 1: e.g. Acquisition]
│ ├── [L2 metric: e.g. Organic signups / week]
│ └── [L2 metric: e.g. Paid CAC by channel]
├── [Driver 2: e.g. Activation]
│ ├── [L2 metric: e.g. % users completing onboarding within 7 days]
│ └── [L2 metric: e.g. Time to first value action]
└── [Driver 3: e.g. Retention]
├── [L2 metric: e.g. Day 30 retention rate]
└── [L2 metric: e.g. Feature adoption depth]For each L2 metric, provide:
[2–3 metrics to watch that prevent optimising the North Star in ways that damage the business. E.g. "If we optimise for signups, we need to watch spam account rate. If we optimise for engagement, we need to watch support ticket volume."]
Suggest a 3-tier dashboard structure:
[5 questions the team should ask in their weekly metrics review to turn numbers into insights. e.g. "Is our activation rate improving while retention stays flat? That suggests onboarding quality issue, not a product-market fit problem."]
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.