| name | pulse-weekly |
| description | Weekly community analysis report — aggregates Discord AND WhatsApp activity, engagement metrics, sentiment trends, top contributors, product insights, and docs gaps. Generates an HTML report using the Evolution brand. Use when user says 'weekly community', 'community analysis', 'weekly community report', or any reference to weekly community analysis. |
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
