docs: add auto-reload and monthly limits documentation to Zen guide

packages/web/src/content/docs/zen.mdx

@@ -156,6 +156,26 @@ The free models:
 
 ---
 
+### Auto-reload
+
+If your balance goes below $5, Zen will automatically reload $20 (plus $1.23
+processing fee).
+
+You can change the auto-reload amount. You can also disable auto-reload entirely.
+
+---
+
+### Monthly limits
+
+You can also set a monthly usage limit for the entire workspace and for each
+member of your team.
+
+For example, let's say you set a monthly usage limit to $20, Zen will not use
+more than $20 in a month. But if you have auto-reload enabled, Zen might end up
+charging you more than $20 if your balance goes below $5.
+
+---
+
 ## Privacy
 
 All our models are hosted in the US. Our providers follow a zero-retention policy and do not use your data for model training, with the following exceptions:

scripts/analyze-first-time-contributors.sh

@@ -0,0 +1,249 @@
+#!/bin/bash
+
+# First-Time Contributor Analyzer
+# Analyzes PRs from first-time contributors over the last 4 weeks
+# Usage: ./scripts/analyze-first-time-contributors.sh
+
+set -euo pipefail
+
+REPO="sst/opencode"
+GITHUB_API="https://api.github.com/repos"
+FOUR_WEEKS_AGO=$(date -u -v-28d '+%Y-%m-%dT00:00:00Z' 2>/dev/null || date -u -d '4 weeks ago' '+%Y-%m-%dT00:00:00Z')
+
+echo "Analyzing first-time contributors from last 4 weeks..."
+echo "Start date: $FOUR_WEEKS_AGO"
+echo ""
+
+# Create temp files
+TEMP_PRS=$(mktemp)
+TEMP_CONTRIBUTORS=$(mktemp)
+trap "rm -f $TEMP_PRS $TEMP_CONTRIBUTORS" EXIT
+
+# Fetch all PRs from the last 4 weeks
+echo "Fetching PRs..."
+ALL_PRS="[]"
+for page in {1..10}; do
+  echo "  Page $page..."
+  PAGE_DATA=$(curl -s "${GITHUB_API}/${REPO}/pulls?state=all&sort=created&direction=desc&per_page=100&page=${page}")
+  
+  COUNT=$(echo "$PAGE_DATA" | jq 'length')
+  if [ "$COUNT" -eq 0 ]; then
+    break
+  fi
+  
+  FILTERED=$(echo "$PAGE_DATA" | jq "[.[] | select(.created_at >= \"${FOUR_WEEKS_AGO}\")]")
+  ALL_PRS=$(echo "$ALL_PRS" "$FILTERED" | jq -s '.[0] + .[1]')
+  
+  OLDEST=$(echo "$PAGE_DATA" | jq -r '.[-1].created_at')
+  if [[ "$OLDEST" < "$FOUR_WEEKS_AGO" ]]; then
+    break
+  fi
+done
+
+echo "$ALL_PRS" > "$TEMP_PRS"
+PR_COUNT=$(jq 'length' "$TEMP_PRS")
+echo "  Found $PR_COUNT PRs"
+
+echo ""
+echo "Checking contributor status for each PR..."
+
+# Get contributors list (people with previous PRs)
+# For each PR, check if the author has "first-time contributor" label or 
+# if this is their first PR to the repo
+
+# Extract PR data with author info
+jq -r '.[] | "\(.number)|\(.user.login)|\(.created_at)|\(.author_association)"' "$TEMP_PRS" > "$TEMP_CONTRIBUTORS"
+
+echo ""
+
+# Analyze with Python
+PYTHON_SCRIPT=$(mktemp)
+trap "rm -f $PYTHON_SCRIPT $TEMP_PRS $TEMP_CONTRIBUTORS" EXIT
+
+cat > "$PYTHON_SCRIPT" << 'EOF'
+import json
+import sys
+from datetime import datetime
+from collections import defaultdict
+
+# Read PR data
+pr_data = []
+with open(sys.argv[1], 'r') as f:
+    for line in f:
+        if line.strip():
+            parts = line.strip().split('|')
+            pr_data.append({
+                'number': parts[0],
+                'author': parts[1],
+                'created_at': parts[2],
+                'author_association': parts[3]
+            })
+
+print(f"Analyzing {len(pr_data)} PRs...\n")
+
+# Categorize by week
+def get_week_label(date_str):
+    date = datetime.fromisoformat(date_str.replace('Z', '+00:00')).replace(tzinfo=None)
+    
+    if date >= datetime(2025, 12, 22):
+        return "Week 51: Dec 22-26"
+    elif date >= datetime(2025, 12, 15):
+        return "Week 50: Dec 15-21"
+    elif date >= datetime(2025, 12, 8):
+        return "Week 49: Dec 8-14"
+    elif date >= datetime(2025, 12, 1):
+        return "Week 48: Dec 1-7"
+    else:
+        return "Earlier"
+
+# First-time contributors have author_association of "FIRST_TIME_CONTRIBUTOR" or "NONE"
+# or sometimes "CONTRIBUTOR" for their first few PRs
+
+by_week = defaultdict(lambda: {
+    'total': 0,
+    'first_time': 0,
+    'returning': 0,
+    'first_time_authors': set()
+})
+
+all_authors = defaultdict(int)
+
+for pr in pr_data:
+    week = get_week_label(pr['created_at'])
+    author = pr['author']
+    assoc = pr['author_association']
+    
+    by_week[week]['total'] += 1
+    all_authors[author] += 1
+    
+    # GitHub marks first-time contributors explicitly
+    # FIRST_TIME_CONTRIBUTOR = first PR to this repo
+    # NONE = no association (could be first time)
+    # For more accuracy, we check if author appears only once in our dataset
+    
+    if assoc == 'FIRST_TIME_CONTRIBUTOR' or (assoc == 'NONE' and all_authors[author] == 1):
+        by_week[week]['first_time'] += 1
+        by_week[week]['first_time_authors'].add(author)
+    else:
+        by_week[week]['returning'] += 1
+
+# Print results
+print("="*90)
+print("FIRST-TIME CONTRIBUTOR ANALYSIS - LAST 4 WEEKS")
+print("="*90 + "\n")
+
+weeks = ["Week 48: Dec 1-7", "Week 49: Dec 8-14", "Week 50: Dec 15-21", "Week 51: Dec 22-26"]
+
+print("PRs by Contributor Type:\n")
+for week in weeks:
+    if week in by_week:
+        data = by_week[week]
+        total = data['total']
+        first_time = data['first_time']
+        returning = data['returning']
+        first_time_pct = (first_time / total * 100) if total > 0 else 0
+        
+        print(f"{week}: {total} PRs")
+        print(f"  ✨ First-time contributors: {first_time} ({first_time_pct:.1f}%)")
+        print(f"  ↩️  Returning contributors:  {returning} ({100-first_time_pct:.1f}%)")
+        print()
+
+# Overall summary
+total_prs = sum(data['total'] for data in by_week.values())
+total_first_time = sum(data['first_time'] for data in by_week.values())
+total_returning = sum(data['returning'] for data in by_week.values())
+overall_first_time_pct = (total_first_time / total_prs * 100) if total_prs > 0 else 0
+
+print("="*90)
+print("OVERALL SUMMARY")
+print("="*90 + "\n")
+
+print(f"Total PRs (4 weeks):              {total_prs}")
+print(f"From first-time contributors:     {total_first_time} ({overall_first_time_pct:.1f}%)")
+print(f"From returning contributors:      {total_returning} ({100-overall_first_time_pct:.1f}%)")
+
+# Count unique first-time contributors
+all_first_time_authors = set()
+for data in by_week.values():
+    all_first_time_authors.update(data['first_time_authors'])
+
+print(f"\nUnique first-time contributors:   {len(all_first_time_authors)}")
+
+# Week by week trend
+print("\n" + "="*90)
+print("TREND ANALYSIS")
+print("="*90 + "\n")
+
+print("First-Time Contributor Rate by Week:\n")
+for week in weeks:
+    if week in by_week:
+        data = by_week[week]
+        rate = (data['first_time'] / data['total'] * 100) if data['total'] > 0 else 0
+        bar = "█" * int(rate / 2)
+        print(f"  {week}: {rate:5.1f}% {bar}")
+
+print("\n" + "="*90)
+print("KEY INSIGHTS")
+print("="*90 + "\n")
+
+insights = []
+
+if total_first_time > 0:
+    insights.append(
+        f"1. New Contributors: {total_first_time} PRs from first-timers shows healthy\n" +
+        f"   community growth and welcoming environment for new contributors."
+    )
+
+if overall_first_time_pct > 20:
+    insights.append(
+        f"2. High New Contributor Rate: {overall_first_time_pct:.1f}% from first-timers is\n" +
+        f"   excellent. Indicates strong onboarding and accessible contribution process."
+    )
+elif overall_first_time_pct > 10:
+    insights.append(
+        f"2. Moderate New Contributor Rate: {overall_first_time_pct:.1f}% from first-timers\n" +
+        f"   is healthy. Good balance of new and returning contributors."
+    )
+else:
+    insights.append(
+        f"2. Low New Contributor Rate: {overall_first_time_pct:.1f}% from first-timers.\n" +
+        f"   Most PRs from established contributors (mature project pattern)."
+    )
+
+# Check for trend
+week_rates = []
+for week in weeks:
+    if week in by_week:
+        data = by_week[week]
+        rate = (data['first_time'] / data['total'] * 100) if data['total'] > 0 else 0
+        week_rates.append(rate)
+
+if len(week_rates) >= 3:
+    if week_rates[-1] > week_rates[0]:
+        insights.append(
+            f"3. Growing Trend: First-time contributor rate increasing\n" +
+            f"   ({week_rates[0]:.1f}% → {week_rates[-1]:.1f}%). Project attracting more new contributors."
+        )
+    elif week_rates[-1] < week_rates[0]:
+        insights.append(
+            f"3. Declining Trend: First-time contributor rate decreasing\n" +
+            f"   ({week_rates[0]:.1f}% → {week_rates[-1]:.1f}%). May indicate shifting to core contributors."
+        )
+    else:
+        insights.append(
+            f"3. Stable Trend: First-time contributor rate relatively stable\n" +
+            f"   across weeks. Consistent new contributor engagement."
+        )
+
+insights.append(
+    f"4. Unique Contributors: {len(all_first_time_authors)} unique new people made their\n" +
+    f"   first contribution. Shows breadth of community involvement."
+)
+
+for insight in insights:
+    print(f"{insight}\n")
+
+print("="*90 + "\n")
+EOF
+
+python3 "$PYTHON_SCRIPT" "$TEMP_CONTRIBUTORS"

scripts/analyze-recent-weeks.sh

@@ -0,0 +1,219 @@
+#!/bin/bash
+
+# GitHub Issues Analyzer for Recent Weeks
+# Analyzes Dec 15-21 (Week 50) and Dec 22-26 (Week 51)
+# Usage: ./scripts/analyze-recent-weeks.sh
+
+set -euo pipefail
+
+REPO="sst/opencode"
+GITHUB_API="https://api.github.com/repos"
+
+# Start from Dec 15
+START_DATE="2025-12-15T00:00:00Z"
+
+echo "Analyzing GitHub issues from Dec 15 onwards..."
+echo "Start date: $START_DATE"
+echo ""
+
+# Create temp file
+TEMP_FILE=$(mktemp)
+trap "rm -f $TEMP_FILE" EXIT
+
+echo "[]" > "$TEMP_FILE"
+
+# Fetch all issues from Dec 15 onwards (paginate through results)
+for page in {1..5}; do
+  echo "  Fetching page $page..."
+  PAGE_DATA=$(curl -s "${GITHUB_API}/${REPO}/issues?state=all&sort=created&direction=desc&per_page=100&page=${page}")
+  
+  # Check if we got any results
+  COUNT=$(echo "$PAGE_DATA" | jq 'length')
+  if [ "$COUNT" -eq 0 ]; then
+    echo "  No more results on page $page"
+    break
+  fi
+  
+  # Filter issues from Dec 15 onwards
+  FILTERED=$(echo "$PAGE_DATA" | jq "[.[] | select(.created_at >= \"${START_DATE}\")]")
+  FILTERED_COUNT=$(echo "$FILTERED" | jq 'length')
+  echo "  Found $FILTERED_COUNT issues from Dec 15 onwards on page $page"
+  
+  # Append to temp file
+  CURRENT=$(cat "$TEMP_FILE")
+  MERGED=$(echo "$CURRENT" "$FILTERED" | jq -s '.[0] + .[1]')
+  echo "$MERGED" > "$TEMP_FILE"
+  
+  # If we've started getting old data, we can stop
+  OLDEST=$(echo "$PAGE_DATA" | jq -r '.[-1].created_at')
+  if [[ "$OLDEST" < "$START_DATE" ]]; then
+    echo "  Reached data older than Dec 15, stopping"
+    break
+  fi
+done
+
+echo ""
+
+# Create Python analysis script
+PYTHON_SCRIPT=$(mktemp)
+trap "rm -f $PYTHON_SCRIPT $TEMP_FILE" EXIT
+
+cat > "$PYTHON_SCRIPT" << 'EOF'
+import json
+import sys
+from datetime import datetime
+from collections import defaultdict
+
+# Read the issues data from file
+with open(sys.argv[1], 'r') as f:
+    data = json.load(f)
+
+if not data:
+    print("No issues found from Dec 15 onwards")
+    sys.exit(0)
+
+print(f"Analyzing {len(data)} issues...\n")
+
+# Categorize and group by week
+issues_by_week = defaultdict(lambda: defaultdict(int))
+week_totals = defaultdict(int)
+week_order = []
+
+# Response tracking
+response_by_week = defaultdict(lambda: {
+    'total': 0,
+    'with_response': 0,
+    'no_response': 0
+})
+
+def get_week_label(date_str):
+    """Convert date to week label"""
+    date = datetime.fromisoformat(date_str.replace('Z', '+00:00')).replace(tzinfo=None)
+    
+    # Manual week grouping for clarity
+    if date >= datetime(2025, 12, 22):
+        return "Week 51: Dec 22-26"
+    elif date >= datetime(2025, 12, 15):
+        return "Week 50: Dec 15-21"
+    else:
+        return "Earlier"
+
+def categorize_issue(item):
+    """Categorize an issue"""
+    if item.get('pull_request'):
+        return "PR"
+    
+    labels = [label['name'] for label in item.get('labels', [])]
+    title = item['title'].lower()
+    
+    if 'discussion' in labels:
+        return "Feature Request"
+    elif 'help-wanted' in labels:
+        return "Help Question"
+    elif 'bug' in labels:
+        return "Bug Report"
+    elif any(x in title for x in ['[feature]', 'feature request', '[feat]']):
+        return "Feature Request"
+    elif title.endswith('?') and 'bug' not in title:
+        return "Help Question"
+    else:
+        return "Other"
+
+# Process each issue
+for item in data:
+    week_label = get_week_label(item['created_at'])
+    if week_label not in week_order:
+        week_order.append(week_label)
+    
+    category = categorize_issue(item)
+    
+    # Check if it's an actual issue (not PR)
+    if not item.get('pull_request'):
+        response_by_week[week_label]['total'] += 1
+        if item['comments'] > 0:
+            response_by_week[week_label]['with_response'] += 1
+        else:
+            response_by_week[week_label]['no_response'] += 1
+    
+    issues_by_week[week_label][category] += 1
+    week_totals[week_label] += 1
+
+# Sort weeks (most recent first)
+week_order = sorted([w for w in week_order if w != "Earlier"], reverse=True)
+
+# Print results
+print("="*80)
+print("GITHUB ISSUES BREAKDOWN - RECENT WEEKS")
+print("="*80 + "\n")
+
+for week in week_order:
+    print(f"{week}: {week_totals[week]} total")
+    for category in sorted(issues_by_week[week].keys()):
+        count = issues_by_week[week][category]
+        print(f"  • {category}: {count}")
+    print()
+
+print("---")
+total = sum(week_totals[w] for w in week_order)
+print(f"TOTAL: {total} issues/PRs\n")
+
+print("OVERALL SUMMARY:")
+all_counts = defaultdict(int)
+for week in week_order:
+    for category, count in issues_by_week[week].items():
+        all_counts[category] += count
+
+for category in sorted(all_counts.keys(), key=lambda x: -all_counts[x]):
+    count = all_counts[category]
+    pct = (count / total) * 100
+    print(f"  • {category}: {count} ({pct:.1f}%)")
+
+# Response rates
+print("\n" + "="*80)
+print("ISSUE RESPONSE RATES")
+print("="*80 + "\n")
+
+for week in week_order:
+    data = response_by_week[week]
+    if data['total'] > 0:
+        rate = (data['with_response'] / data['total'] * 100)
+        print(f"{week}:")
+        print(f"  Total issues: {data['total']}")
+        print(f"  With response: {data['with_response']} ({rate:.1f}%)")
+        print(f"  No response: {data['no_response']}")
+        print()
+
+# Week over week comparison
+print("="*80)
+print("WEEK-OVER-WEEK COMPARISON")
+print("="*80 + "\n")
+
+if len(week_order) >= 2:
+    w1 = week_order[0]  # Most recent
+    w2 = week_order[1]  # Previous
+    
+    vol_change = week_totals[w1] - week_totals[w2]
+    vol_pct = (vol_change / week_totals[w2] * 100) if week_totals[w2] > 0 else 0
+    
+    print(f"Volume Change: {week_totals[w2]} → {week_totals[w1]} ({vol_pct:+.1f}%)")
+    print()
+    
+    print("Category Changes:")
+    for category in sorted(all_counts.keys()):
+        old_val = issues_by_week[w2].get(category, 0)
+        new_val = issues_by_week[w1].get(category, 0)
+        change = new_val - old_val
+        direction = "↑" if change > 0 else "↓" if change < 0 else "→"
+        print(f"  {category:18s}: {old_val:3d} → {new_val:3d}  {direction} {abs(change)}")
+    
+    print()
+    if response_by_week[w1]['total'] > 0 and response_by_week[w2]['total'] > 0:
+        r1 = (response_by_week[w1]['with_response'] / response_by_week[w1]['total'] * 100)
+        r2 = (response_by_week[w2]['with_response'] / response_by_week[w2]['total'] * 100)
+        print(f"Response Rate: {r2:.1f}% → {r1:.1f}% ({r1-r2:+.1f}pp)")
+
+print("\n" + "="*80 + "\n")
+EOF
+
+# Run the analysis
+python3 "$PYTHON_SCRIPT" "$TEMP_FILE"