prod-trends

name	prod-trends
description	Weekly trends analysis — compares community, GitHub, and financial metrics week-over-week to detect patterns, risks and opportunities. Use when user says 'trends analysis', 'trends', 'how are the metrics', 'weekly comparison', 'metrics evolution', or as part of the weekly review routine.

Trends Analysis — Weekly Comparison

Routine that compares community, GitHub, and financial metrics week-over-week to detect patterns, risks, and opportunities.

Always respond in English.

Data Sources

1. Community (Discord)

Read previous reports in:

• workspace/community/reports/daily/ — daily pulses (HTML)
• workspace/community/reports/weekly/ — weekly reports (HTML)

Extract from HTML or generate from data:

• Messages per day (volume)
• Active members (WAM)
• Unanswered questions
• Overall sentiment
• Top recurring topics

2. GitHub

Read previous reports in:

• workspace/projects/github-reviews/ — reviews (HTML)

Extract or generate:

• Open PRs (trend: accumulating or being resolved?)
• Open vs closed issues
• Stars/forks (growth)
• Commits per week (team activity)
• Average open PR time

3. Financial

Query data via skills:

• /int-stripe — MRR, cobranças, reembolsos, assinaturas ativas
• /int-omie — accounts receivable/payable (if available)

Metrics:

• MRR (Monthly Recurring Revenue)
• Monthly charges vs previous month
• Refunds
• Active subscriptions (growth/churn)

4. Operational (ADWs)

Read runner metrics:

• ADWs/logs/metrics.json — runs, success rate, avg time per routine

Workflow

Step 1 — Collect current week's data

Fetch the most recent data from each source (last 7 days).

Step 2 — Collect previous week's data

Fetch data from 7-14 days ago for comparison. If it does not exist (first run), mark as "baseline" and skip comparison.

Step 3 — Calculate trends

For each metric, calculate:

• Current vs previous value
• Absolute and percentage variance
• Direction: ↑ (rising), ↓ (falling), = (stable)
• Classification: 🟢 healthy, 🟡 attention, 🔴 risk

Classification criteria:

Metric	🟢 Healthy	🟡 Attention	🔴 Risk
WAM	stable or ↑	drop <10%	drop >10%
Unanswered questions	<5	5-10	>10
Sentiment	positive	neutral	negative
Open PRs	<10	10-20	>20 accumulating
Unanswered issues	<5	5-15	>15
Stars (weekly)	>10	5-10	<5
MRR	stable or ↑	drop <5%	drop >5%
Success rate ADWs	>90%	70-90%	<70%

Step 4 — Detect patterns

Analyze recent weeks (as many as available) and identify:

• Persistent trends — metric rising/falling for 2+ consecutive weeks
• Correlations — e.g., increase in GitHub issues + increase in Discord questions = possible bug
• Anomalies — unusual spike or drop vs average
• Seasonality — recurring patterns (e.g., Monday has more activity)

Step 5 — Generate HTML report

Read the template at .claude/templates/html/custom/trends-report.html. Replace the placeholders {{...}} with the actual data.

Overall health classification:

• All 🟢 or mostly 🟢: healthy — "Healthy"
• Mix of 🟢 and 🟡: mixed — "Attention"
• Any 🔴: risk — "Risk"

REQUIRED: Always generate the HTML first. Read the template, replace the placeholders, and save the complete HTML file. This applies even on the first run (baseline) — even without comparison, fill the scorecard with current values and "—" for previous.

Save HTML to workspace/daily-logs/[C] YYYY-WXX-trends.html.

Then, also save a summarized markdown version to workspace/daily-logs/[C] YYYY-WXX-trends.md:

# Trends Analysis — Week {WXX}

## Executive Summary
{3 bullets: what improved, what worsened, opportunity}

## Scorecard

| Area | Metric | Current | Previous | Var | Trend | Status |
|------|---------|-------|----------|-----|-------|--------|
| Community | WAM | {N} | {N} | {+/-X%} | ↑/↓/= | 🟢/🟡/🔴 |
| Community | Unanswered questions | {N} | {N} | | | |
| Community | Sentiment | {label} | {label} | | | |
| GitHub | Open PRs | {N} | {N} | | | |
| GitHub | Unanswered issues | {N} | {N} | | | |
| GitHub | Stars (week) | {N} | {N} | | | |
| Financial | MRR | R${N} | R${N} | {var%} | | |
| Financial | Active subscriptions | {N} | {N} | | | |
| Operational | Success rate ADWs | {X}% | {X}% | | | |

## Detected Patterns
- {pattern 1 with evidence}
- {pattern 2 with evidence}

## Risks
- {risk with supporting metric}

## Opportunities
- {opportunity based on data}

## Recommendations
1. {concrete action based on data}
2. {concrete action}

Step 6 — Save snapshot

Save a snapshot of current metrics to memory/trends/YYYY-WXX.json to accumulate history:

{
  "week": "YYYY-WXX",
  "date": "YYYY-MM-DD",
  "community": {"wam": N, "messages": N, "unanswered": N, "sentiment": "positive"},
  "github": {"prs_open": N, "issues_open": N, "issues_unanswered": N, "stars_week": N, "commits_week": N},
  "financial": {"mrr": N, "subscriptions": N, "refunds": N},
  "operational": {"adw_runs": N, "adw_success_rate": N, "adw_avg_seconds": N}
}

Create memory/trends/ if it does not exist.

Rules

• First run = baseline — no comparison, just collect and save snapshot
• Real data — do not fabricate metrics, use what is available
• If a source has no data, skip — do not block due to a missing report
• Focus on action — each insight should lead to a concrete recommendation
• Do not alarm without evidence — red only when the metric truly indicates risk