evolution-foundation 
Weekly Community Report
Weekly routine that analyzes Discord and WhatsApp activity from the last 7 days and generates a complete HTML report.
Always respond in English.
Workflow
Step 1 — Collect the week's data
Use the /discord-get-messages skill to fetch messages from the last 7 days in the main channels.
Guild ID: YOUR_GUILD_ID
Channels to monitor:
- All community text channels (chat-pt, chat-en, chat-es, help, feedback, suggestions, showcase, news)
- New members channel (
🆕・new-members)
For each channel, fetch paginated messages (100 per request) to cover 7 days.
Step 1b — Collect WhatsApp data (7 days)
Use the /int-whatsapp skill to fetch messages and stats from the last 7 days:
python3 {project-root}/.claude/skills/int-whatsapp/scripts/whatsapp_client.py messages_7d
python3 {project-root}/.claude/skills/int-whatsapp/scripts/whatsapp_client.py stats --start $(date -u -v-7d '+%Y-%m-%d') --end $(date -u '+%Y-%m-%d')
python3 {project-root}/.claude/skills/int-whatsapp/scripts/whatsapp_client.py groups --start $(date -u -v-7d '+%Y-%m-%d') --end $(date -u '+%Y-%m-%d')
Include in the report as a separate "WhatsApp" section with: active groups, total messages, unique participants, topics, support questions.
Step 2 — Calculate metrics
- Growth: total members (estimate), new vs departed, net churn
- WAM (Weekly Active Members): unique members who sent a message
- Communicators: % of visitors who chat (goal: 50%)
- Resolution rate: answered questions / total in #help (goal: >80%)
- First response time: median time between question and first response
- Messages per active member: total msgs / WAM (goal: >4)
Step 3 — Analyze sentiment and topics
For each day of the week:
- Sentiment: classify messages as positive/neutral/negative
- Topics: group discussions by theme, count frequency
Consolidate:
- Top 5 topics with sentiment bar
- Sentiment trend throughout the week
Step 4 — Identify highlights
- Top 5 most active members: by message volume + answers given
- New members who contributed: who is new and already participated
- Members at risk of churn: previously active, inactive this week
Step 5 — Extract product insights
Analyze messages and identify:
- Most requested features: spontaneous feature requests
- Reported bugs: technical problems mentioned
- Docs gap: questions whose answers should be in the documentation (indicate frequency)
Step 6 — Comparison
If previous reports exist in workspace/community/reports/weekly/, compare:
- WAM this week vs previous
- New members vs previous
- Resolution rate vs previous
- Response time vs previous
Step 7 — Generate HTML report
Read the template at .claude/templates/html/custom/community-weekly-report.html.
Replace the placeholders {{...}} with the actual data.
Logo available at: workspace/projects/Evolution Foundation/Logos finais/Favicon logo/SVG/Favicon Color 500.svg
Save to:
workspace/community/reports/weekly/[C] YYYY-WXX-community-report.html
Step 8 — Executive summary
Present in the terminal:
## Weekly Report — Week {WXX}
Members: {N} ({+/-}) | WAM: {N} ({X}%)
Resolution: {X}% | 1st response: {X} min
Sentiment: {label}
Top: {topic 1}, {topic 2}, {topic 3}
Insights: {N} features, {N} bugs, {N} docs gaps
Report saved to workspace/community/reports/weekly/
Rules
- Do not reply to messages — only read and analyze
- Real data — metrics based on collected messages, no fabrication
- Docs gap is gold — each question without docs becomes a backlog item
- Comparison is fundamental — always show trend vs previous week
- Product insights — the most valuable section, handle with care