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

1. Growth: total members (estimate), new vs departed, net churn
2. WAM (Weekly Active Members): unique members who sent a message
3. Communicators: % of visitors who chat (goal: 50%)
4. Resolution rate: answered questions / total in #help (goal: >80%)
5. First response time: median time between question and first response
6. Messages per active member: total msgs / WAM (goal: >4)

Step 3 — Analyze sentiment and topics

For each day of the week:

1. Sentiment: classify messages as positive/neutral/negative
2. Topics: group discussions by theme, count frequency

Consolidate:

• Top 5 topics with sentiment bar
• Sentiment trend throughout the week

Step 4 — Identify highlights

1. Top 5 most active members: by message volume + answers given
2. New members who contributed: who is new and already participated
3. Members at risk of churn: previously active, inactive this week

Step 5 — Extract product insights

Analyze messages and identify:

1. Most requested features: spontaneous feature requests
2. Reported bugs: technical problems mentioned
3. 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