ci

scripts/analyze-first-time-contributors.sh

@@ -1,249 +0,0 @@
-#!/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="anomalyco/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

@@ -1,219 +0,0 @@
-#!/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="anomalyco/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"