Director-level metrics review across Nx platform: nx.dev engagement, AI traffic rates, npm download trends, docs traffic/page performance, CNW funnel stats, and SPACE engineering productivity. Use when user says "check metrics", "metrics review", "how are our numbers", "dashboard check", "KPI review", "important metrics", or asks about engagement, traffic, downloads, or productivity trends.
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Director-level metrics review across Nx platform: nx.dev engagement, AI traffic rates, npm download trends, docs traffic/page performance, CNW funnel stats, and SPACE engineering productivity. Use when user says "check metrics", "metrics review", "how are our numbers", "dashboard check", "KPI review", "important metrics", or asks about engagement, traffic, downloads, or productivity trends.
If data is not programmatically accessible: Tell the user which specific
reports to pull from their analytics tool and offer to analyze the data once
pasted. Be specific about which dashboard/view/date range.
2. AI Traffic Rate (ai-traffic)
Goal: Quantify how much traffic comes from AI crawlers/tools vs humans.
Data sources:
Server logs or Netlify analytics (bot filtering)
Cloudflare bot analytics (if available)
User-agent analysis from access logs
What to report:
Overall AI bot traffic as % of total (docs + marketing)
AI traffic specifically on getting-started pages (/getting-started/*,
/docs/getting-started/*, /recipes/getting-started/*)
Trend: is AI traffic increasing month over month?
Top AI user agents (GPTBot, GeminiBot, Bytespider, etc.)
Breakdown: AI training crawlers vs AI-assisted search (Perplexity, SearchGPT)
If data not accessible: Suggest checking Cloudflare/Netlify bot analytics
and specify which filters to apply. Offer to analyze exported CSV/JSON.
Goal: Understand docs traffic trajectory and which content drives visits.
Data sources:
Netlify Analytics API or user-provided analytics export
Google Search Console data (if user provides)
What to report:
Total docs traffic last 30 days vs prior 30 days (trend)
Total docs traffic last 12 months — monthly breakdown, is it growing?
Top 20 most visited docs pages
Top 20 fastest growing pages (by % increase)
Top 20 pages with biggest traffic decline
Traffic by docs section/category breakdown
Search terms driving docs traffic (if GSC data available)
5. Docs Page Cleanup Candidates (docs-cleanup)
Goal: Identify pages that should be removed, merged, or rewritten.
Data sources:
Analytics data (low-traffic pages)
Git blame/log (stale content)
Page content analysis
What to report:
Pages with <100 views/month that aren't core reference
Pages not updated in >12 months with low traffic
Duplicate/overlapping content candidates for merging
Pages with high bounce rate + low time-on-page (content not helpful)
Broken or outdated code examples (check for deprecated APIs)
Implementation approach:
Get traffic data (from analytics or user)
Cross-reference with git log to find last-modified dates
Read low-traffic pages to assess if content is still relevant
Produce a ranked list: remove, merge, or rewrite
6. CNW Stats Review (cnw)
Goal: Track CNW funnel against targets: 200+ "yes" to Cloud, 2K+ starts/day.
How to gather: Invoke the cnw-stats-analyzer skill/command logic directly,
or tell the user to run /cnw-stats-analyzer if this skill can't access MongoDB.
What to report:
Daily starts (target: 2,000+/day)
Daily Cloud opt-in count and rate (target: 200+ "yes" per day)
Cloud opt-in trend: is it moving toward the goal?
Completion rate (completes / starts)
Error rate and top errors
Active experiments and their impact on Cloud adoption
Week-over-week trends for starts and Cloud opt-in
Thresholds (flag as warning if below):
Metric
Target
Warning
Daily starts
2,000
<1,800
Daily Cloud "yes"
200
<150
Completion rate
>70%
<60%
Error rate
<5%
>8%
7. SPACE Metrics — Engineering Productivity (space)
Goal: Holistic engineering productivity health check using the SPACE framework.
SPACE dimensions to assess:
Satisfaction & well-being
Performance
Activity
Communication & collaboration
Efficiency & flow
Data sources:
Linear: cycle times, throughput, blocked issues
GitHub: PR review times, merge frequency, CI pass rates
Team capacity audit (from /audit-capacity)
Qualitative signals from 1:1 notes and team syncs
What to report:
Dimension
Metrics
Source
Satisfaction
Recent sentiment from 1:1s, any flagged concerns
.ai/para/areas/personnel/
Performance
Issues completed vs planned per cycle, bug escape rate
Linear
Activity
PRs merged/week, commits/week, releases shipped
GitHub
Communication
PR review turnaround time, blocked issue count
GitHub + Linear
Efficiency
Cycle time (issue created to done), CI build times
Linear + GitHub Actions
Implementation:
Pull Linear cycle data for active teams (Nx CLI, DPE, Nx Cloud, etc.)
Pull GitHub PR stats: gh api for merge times, review counts
Check .ai/para/areas/personnel/ for recent sentiment signals
Check .ai/para/areas/syncs/ for recurring blockers mentioned in syncs
Produce a per-team summary with trend arrows
8. CLI Scorecard (scorecard)
Goal: Quick snapshot of Nx CLI health metrics (delegates to existing command).
Tell the user to run /nx-scorecard or inline the scorecard logic:
Open/closed issues on GitHub
Open high-priority Linear issues
npm downloads + YoY
Open PRs
Output Format
Produce a single markdown report structured as:
# Nx Platform Metrics Review — {DATE}## Executive Summary- 3-5 bullet points: what's good, what needs attention, what changed
- Use trend indicators: up-arrow, down-arrow, flat, or NEW for new signals
## 1. Landing Page Engagement
{data or "DATA NEEDED: paste from [specific dashboard]"}
## 2. AI Traffic
{data or instructions}
## 3. npm Downloads
{always available via API}
## 4. Docs Traffic
{data or instructions}
## 5. Docs Cleanup Candidates
{analysis}
## 6. CNW Funnel
{data from MongoDB or instructions}
## 7. SPACE Productivity
{analysis}
## 8. CLI Scorecard
{data from GitHub + Linear + npm}
## Action Items- Prioritized list of things that need attention based on the data
- Each item tagged: [urgent], [this-week], [this-month], [monitor]
Running the Review
Always start with npm downloads and CLI scorecard — these are fully
automated and give immediate value.
Check what data is accessible before attempting analytics sections.
Don't waste time on APIs that need credentials.
For sections needing manual data, produce specific instructions:
"Go to [dashboard URL], select [date range], export [format], paste here."
Flag missing data clearly — don't skip sections silently.
Compare against prior review if one exists at
.ai/para/areas/metrics-review/ — call out deltas.
State & History
Save each review to .ai/para/areas/metrics-review/:
