Magnus HedemarkGitHubusername <username>factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>* feat(product-analytics-and-measurement): add product analytics and measurement skill Add a new skill that turns intended product outcomes into observable, governed evidence: metric trees with leading/lagging indicators and countermetrics, event/tracking plans with identity/session/data-quality/ ownership considerations, instrumentation QA across client/server/pipeline/ end-to-end layers, dashboard contracts, privacy-aware measurement, and decision cadence for outcome reviews. Includes: - SKILL.md with Loading Guide, When to Use/Not to Use, related-skill routing - README.md with all 5 required human-facing sections - references/discovery-brief.md mapping ownership boundaries vs existing skills - references/metric-tree.md with measurability gates and contextual examples - templates/tracking-plan.md (event taxonomy, identity resolution, privacy) - templates/instrumentation-qa-checklist.md (4-layer QA) - templates/outcome-review.md (decision cadence template) - evals/evals.json with 6 cases covering new feature, internal product, public service, conflicting metrics, unmeasurable North Star rejection, and privacy-boundary measurement - Updated root README catalog, skill-triggers.md, and regenerated catalogs Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * feat(product-analytics-and-measurement): add product analytics and measurement skill Add a new skill that turns intended product outcomes into observable, governed evidence: metric trees with leading/lagging indicators and countermetrics, event/tracking plans with identity/session/data-quality/ ownership considerations, instrumentation QA across client/server/pipeline/ end-to-end layers, dashboard contracts, privacy-aware measurement, and decision cadence for outcome reviews. Includes: - SKILL.md with Loading Guide, When to Use/Not to Use, related-skill routing - README.md with all 5 required human-facing sections - references/discovery-brief.md mapping ownership boundaries vs existing skills - references/metric-tree.md with measurability gates and contextual examples - templates/tracking-plan.md (event taxonomy, identity resolution, privacy) - templates/instrumentation-qa-checklist.md (4-layer QA) - templates/outcome-review.md (decision cadence template) - evals/evals.json with 6 cases covering new feature, internal product, public service, conflicting metrics, unmeasurable North Star rejection, and privacy-boundary measurement - Updated root README catalog, skill-triggers.md, and regenerated catalogs Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> --------- Co-authored-by: username <username> Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
4.7 KiB
Product Analytics and Measurement
Turn intended product outcomes into observable, governed evidence.
Why Install This Skill
Most product teams measure what is easy, not what matters. They inherit dashboards built by previous teams, track events nobody reviews, and discover too late that a "North Star" metric was never actually measurable. When metrics conflict across teams — marketing counts an activation differently than product — there is no contract to resolve the disagreement.
After installing this skill, your agent can define a complete measurement system for any product: decompose a North Star into a metric tree with leading and lagging indicators, produce an event tracking plan with identity and data-quality rules, audit instrumentation before trusting the numbers, and establish a dashboard contract and decision cadence so measurement drives action instead of accumulating dashboards. The skill works across SaaS, internal tools, public services, and consumer products — not just one business model.
What You Get
| Directory | What it provides |
|---|---|
SKILL.md |
Core methodology: metric trees, tracking plans, instrumentation QA, privacy-aware measurement, dashboard contracts, and decision cadence. Includes the Loading Guide, When to Use / When Not to Use, and related-skill routing. |
references/discovery-brief.md |
Bounded discovery brief mapping existing metrics content across product-strategy, go-to-market, financial-modeling, data-engineering, and data-scientist, with ownership boundaries. |
references/metric-tree.md |
How to build, validate, and maintain a metric tree: leading/lagging indicators, countermetrics, ownership rules, measurability gates, and examples across product types. |
templates/tracking-plan.md |
Fillable tracking plan template: event taxonomy, property schema, identity resolution, session definition, data quality rules, and ownership fields. |
templates/instrumentation-qa-checklist.md |
Checklist for verifying instrumentation quality across client, server, pipeline, and end-to-end layers before trusting a metric on a dashboard. |
templates/outcome-review.md |
Template for a recurring product outcome review: metric tree health, countermetric checks, decision log, and metric deprecation/retirement decisions. |
evals/evals.json |
Output-quality evaluation cases covering new features, internal products, public services, conflicting metrics, and unmeasurable-North-Star rejection. |
Quick Start
Load the skill when your task involves defining product metrics, designing tracking, or auditing instrumentation. The agent will follow the working method in SKILL.md:
- Define the outcome and decompose into a metric tree (
references/metric-tree.md). - Verify measurability — reject unmeasurable metrics rather than fabricate proxies.
- Produce a tracking plan contract (
templates/tracking-plan.md). - QA the instrumentation before trusting the dashboard (
templates/instrumentation-qa-checklist.md). - Establish a decision cadence and outcome review rhythm (
templates/outcome-review.md).
No API keys, external services, or software dependencies are required. The skill is purely methodological.
Triggers
Load this skill when the user asks about:
- Defining product metrics, North Star, success metrics, or OKR measurement
- Building a metric tree, leading/lagging indicators, or countermetrics
- Designing an event taxonomy, tracking plan, or analytics specification
- Auditing instrumentation quality or analytics data trustworthiness
- Setting up product funnels, cohort definitions, retention measurement, or adoption tracking
- Creating dashboard contracts with metric definitions, sources, and ownership
- Resolving conflicting metric definitions between teams
- Establishing a product measurement cadence or outcome review process
- Privacy-aware measurement design (consent boundaries, minimization, aggregation thresholds)
Do not load for statistical inference or experiment design (use data-scientist), data pipeline implementation (use data-engineering), data architecture (use data-architect), observability or infrastructure monitoring (use site-reliability-engineering), or general BI dashboard building.
Requirements
- No software dependencies, API keys, or external services required.
- Compatible with any Agent Skills harness that supports markdown skill loading.
- Purely methodological — all outputs are documents (metric trees, tracking plans, checklists, review templates).