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docs: add auto-reload and monthly limits documentation to Zen guide
This commit is contained in:
Jay V
@@ -156,6 +156,26 @@ The free models:
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---
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### Auto-reload
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If your balance goes below $5, Zen will automatically reload $20 (plus $1.23
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processing fee).
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You can change the auto-reload amount. You can also disable auto-reload entirely.
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---
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### Monthly limits
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You can also set a monthly usage limit for the entire workspace and for each
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member of your team.
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For example, let's say you set a monthly usage limit to $20, Zen will not use
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more than $20 in a month. But if you have auto-reload enabled, Zen might end up
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charging you more than $20 if your balance goes below $5.
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---
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## Privacy
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All our models are hosted in the US. Our providers follow a zero-retention policy and do not use your data for model training, with the following exceptions:
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249
scripts/analyze-first-time-contributors.sh
Executable file
249
scripts/analyze-first-time-contributors.sh
Executable file
@@ -0,0 +1,249 @@
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#!/bin/bash
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# First-Time Contributor Analyzer
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# Analyzes PRs from first-time contributors over the last 4 weeks
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# Usage: ./scripts/analyze-first-time-contributors.sh
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set -euo pipefail
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REPO="sst/opencode"
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GITHUB_API="https://api.github.com/repos"
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FOUR_WEEKS_AGO=$(date -u -v-28d '+%Y-%m-%dT00:00:00Z' 2>/dev/null || date -u -d '4 weeks ago' '+%Y-%m-%dT00:00:00Z')
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echo "Analyzing first-time contributors from last 4 weeks..."
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echo "Start date: $FOUR_WEEKS_AGO"
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echo ""
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# Create temp files
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TEMP_PRS=$(mktemp)
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TEMP_CONTRIBUTORS=$(mktemp)
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trap "rm -f $TEMP_PRS $TEMP_CONTRIBUTORS" EXIT
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# Fetch all PRs from the last 4 weeks
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echo "Fetching PRs..."
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ALL_PRS="[]"
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for page in {1..10}; do
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echo " Page $page..."
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PAGE_DATA=$(curl -s "${GITHUB_API}/${REPO}/pulls?state=all&sort=created&direction=desc&per_page=100&page=${page}")
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COUNT=$(echo "$PAGE_DATA" | jq 'length')
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if [ "$COUNT" -eq 0 ]; then
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break
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fi
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FILTERED=$(echo "$PAGE_DATA" | jq "[.[] | select(.created_at >= \"${FOUR_WEEKS_AGO}\")]")
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ALL_PRS=$(echo "$ALL_PRS" "$FILTERED" | jq -s '.[0] + .[1]')
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OLDEST=$(echo "$PAGE_DATA" | jq -r '.[-1].created_at')
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if [[ "$OLDEST" < "$FOUR_WEEKS_AGO" ]]; then
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break
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fi
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done
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echo "$ALL_PRS" > "$TEMP_PRS"
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PR_COUNT=$(jq 'length' "$TEMP_PRS")
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echo " Found $PR_COUNT PRs"
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echo ""
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echo "Checking contributor status for each PR..."
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# Get contributors list (people with previous PRs)
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# For each PR, check if the author has "first-time contributor" label or
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# if this is their first PR to the repo
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# Extract PR data with author info
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jq -r '.[] | "\(.number)|\(.user.login)|\(.created_at)|\(.author_association)"' "$TEMP_PRS" > "$TEMP_CONTRIBUTORS"
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echo ""
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# Analyze with Python
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PYTHON_SCRIPT=$(mktemp)
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trap "rm -f $PYTHON_SCRIPT $TEMP_PRS $TEMP_CONTRIBUTORS" EXIT
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cat > "$PYTHON_SCRIPT" << 'EOF'
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import json
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import sys
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from datetime import datetime
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from collections import defaultdict
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# Read PR data
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pr_data = []
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with open(sys.argv[1], 'r') as f:
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for line in f:
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if line.strip():
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parts = line.strip().split('|')
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pr_data.append({
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'number': parts[0],
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'author': parts[1],
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'created_at': parts[2],
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'author_association': parts[3]
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})
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print(f"Analyzing {len(pr_data)} PRs...\n")
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# Categorize by week
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def get_week_label(date_str):
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date = datetime.fromisoformat(date_str.replace('Z', '+00:00')).replace(tzinfo=None)
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if date >= datetime(2025, 12, 22):
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return "Week 51: Dec 22-26"
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elif date >= datetime(2025, 12, 15):
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return "Week 50: Dec 15-21"
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elif date >= datetime(2025, 12, 8):
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return "Week 49: Dec 8-14"
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elif date >= datetime(2025, 12, 1):
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return "Week 48: Dec 1-7"
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else:
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return "Earlier"
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# First-time contributors have author_association of "FIRST_TIME_CONTRIBUTOR" or "NONE"
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# or sometimes "CONTRIBUTOR" for their first few PRs
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by_week = defaultdict(lambda: {
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'total': 0,
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'first_time': 0,
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'returning': 0,
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'first_time_authors': set()
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})
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all_authors = defaultdict(int)
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for pr in pr_data:
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week = get_week_label(pr['created_at'])
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author = pr['author']
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assoc = pr['author_association']
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by_week[week]['total'] += 1
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all_authors[author] += 1
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# GitHub marks first-time contributors explicitly
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# FIRST_TIME_CONTRIBUTOR = first PR to this repo
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# NONE = no association (could be first time)
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# For more accuracy, we check if author appears only once in our dataset
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if assoc == 'FIRST_TIME_CONTRIBUTOR' or (assoc == 'NONE' and all_authors[author] == 1):
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by_week[week]['first_time'] += 1
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by_week[week]['first_time_authors'].add(author)
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else:
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by_week[week]['returning'] += 1
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# Print results
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print("="*90)
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print("FIRST-TIME CONTRIBUTOR ANALYSIS - LAST 4 WEEKS")
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print("="*90 + "\n")
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weeks = ["Week 48: Dec 1-7", "Week 49: Dec 8-14", "Week 50: Dec 15-21", "Week 51: Dec 22-26"]
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print("PRs by Contributor Type:\n")
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for week in weeks:
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if week in by_week:
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data = by_week[week]
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total = data['total']
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first_time = data['first_time']
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returning = data['returning']
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first_time_pct = (first_time / total * 100) if total > 0 else 0
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print(f"{week}: {total} PRs")
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print(f" ✨ First-time contributors: {first_time} ({first_time_pct:.1f}%)")
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print(f" ↩️ Returning contributors: {returning} ({100-first_time_pct:.1f}%)")
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print()
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# Overall summary
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total_prs = sum(data['total'] for data in by_week.values())
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total_first_time = sum(data['first_time'] for data in by_week.values())
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total_returning = sum(data['returning'] for data in by_week.values())
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overall_first_time_pct = (total_first_time / total_prs * 100) if total_prs > 0 else 0
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print("="*90)
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print("OVERALL SUMMARY")
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print("="*90 + "\n")
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print(f"Total PRs (4 weeks): {total_prs}")
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print(f"From first-time contributors: {total_first_time} ({overall_first_time_pct:.1f}%)")
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print(f"From returning contributors: {total_returning} ({100-overall_first_time_pct:.1f}%)")
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# Count unique first-time contributors
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all_first_time_authors = set()
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for data in by_week.values():
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all_first_time_authors.update(data['first_time_authors'])
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print(f"\nUnique first-time contributors: {len(all_first_time_authors)}")
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# Week by week trend
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print("\n" + "="*90)
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print("TREND ANALYSIS")
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print("="*90 + "\n")
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print("First-Time Contributor Rate by Week:\n")
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for week in weeks:
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if week in by_week:
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data = by_week[week]
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rate = (data['first_time'] / data['total'] * 100) if data['total'] > 0 else 0
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bar = "█" * int(rate / 2)
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print(f" {week}: {rate:5.1f}% {bar}")
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print("\n" + "="*90)
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print("KEY INSIGHTS")
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print("="*90 + "\n")
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insights = []
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if total_first_time > 0:
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insights.append(
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f"1. New Contributors: {total_first_time} PRs from first-timers shows healthy\n" +
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f" community growth and welcoming environment for new contributors."
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)
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if overall_first_time_pct > 20:
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insights.append(
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f"2. High New Contributor Rate: {overall_first_time_pct:.1f}% from first-timers is\n" +
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f" excellent. Indicates strong onboarding and accessible contribution process."
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)
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elif overall_first_time_pct > 10:
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insights.append(
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f"2. Moderate New Contributor Rate: {overall_first_time_pct:.1f}% from first-timers\n" +
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f" is healthy. Good balance of new and returning contributors."
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)
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else:
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insights.append(
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f"2. Low New Contributor Rate: {overall_first_time_pct:.1f}% from first-timers.\n" +
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f" Most PRs from established contributors (mature project pattern)."
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)
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# Check for trend
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week_rates = []
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for week in weeks:
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if week in by_week:
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data = by_week[week]
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rate = (data['first_time'] / data['total'] * 100) if data['total'] > 0 else 0
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week_rates.append(rate)
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if len(week_rates) >= 3:
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if week_rates[-1] > week_rates[0]:
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insights.append(
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f"3. Growing Trend: First-time contributor rate increasing\n" +
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f" ({week_rates[0]:.1f}% → {week_rates[-1]:.1f}%). Project attracting more new contributors."
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)
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elif week_rates[-1] < week_rates[0]:
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insights.append(
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f"3. Declining Trend: First-time contributor rate decreasing\n" +
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f" ({week_rates[0]:.1f}% → {week_rates[-1]:.1f}%). May indicate shifting to core contributors."
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)
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else:
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insights.append(
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f"3. Stable Trend: First-time contributor rate relatively stable\n" +
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f" across weeks. Consistent new contributor engagement."
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)
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insights.append(
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f"4. Unique Contributors: {len(all_first_time_authors)} unique new people made their\n" +
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f" first contribution. Shows breadth of community involvement."
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)
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for insight in insights:
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print(f"{insight}\n")
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print("="*90 + "\n")
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EOF
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python3 "$PYTHON_SCRIPT" "$TEMP_CONTRIBUTORS"
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219
scripts/analyze-recent-weeks.sh
Executable file
219
scripts/analyze-recent-weeks.sh
Executable file
@@ -0,0 +1,219 @@
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#!/bin/bash
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# GitHub Issues Analyzer for Recent Weeks
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# Analyzes Dec 15-21 (Week 50) and Dec 22-26 (Week 51)
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# Usage: ./scripts/analyze-recent-weeks.sh
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set -euo pipefail
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REPO="sst/opencode"
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GITHUB_API="https://api.github.com/repos"
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# Start from Dec 15
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START_DATE="2025-12-15T00:00:00Z"
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echo "Analyzing GitHub issues from Dec 15 onwards..."
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echo "Start date: $START_DATE"
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echo ""
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# Create temp file
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TEMP_FILE=$(mktemp)
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trap "rm -f $TEMP_FILE" EXIT
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echo "[]" > "$TEMP_FILE"
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# Fetch all issues from Dec 15 onwards (paginate through results)
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for page in {1..5}; do
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echo " Fetching page $page..."
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PAGE_DATA=$(curl -s "${GITHUB_API}/${REPO}/issues?state=all&sort=created&direction=desc&per_page=100&page=${page}")
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# Check if we got any results
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COUNT=$(echo "$PAGE_DATA" | jq 'length')
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if [ "$COUNT" -eq 0 ]; then
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echo " No more results on page $page"
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break
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fi
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# Filter issues from Dec 15 onwards
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FILTERED=$(echo "$PAGE_DATA" | jq "[.[] | select(.created_at >= \"${START_DATE}\")]")
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FILTERED_COUNT=$(echo "$FILTERED" | jq 'length')
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echo " Found $FILTERED_COUNT issues from Dec 15 onwards on page $page"
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# Append to temp file
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CURRENT=$(cat "$TEMP_FILE")
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MERGED=$(echo "$CURRENT" "$FILTERED" | jq -s '.[0] + .[1]')
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echo "$MERGED" > "$TEMP_FILE"
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# If we've started getting old data, we can stop
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OLDEST=$(echo "$PAGE_DATA" | jq -r '.[-1].created_at')
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if [[ "$OLDEST" < "$START_DATE" ]]; then
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echo " Reached data older than Dec 15, stopping"
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break
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fi
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done
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echo ""
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# Create Python analysis script
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PYTHON_SCRIPT=$(mktemp)
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trap "rm -f $PYTHON_SCRIPT $TEMP_FILE" EXIT
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cat > "$PYTHON_SCRIPT" << 'EOF'
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import json
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import sys
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from datetime import datetime
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from collections import defaultdict
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# Read the issues data from file
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with open(sys.argv[1], 'r') as f:
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data = json.load(f)
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if not data:
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print("No issues found from Dec 15 onwards")
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sys.exit(0)
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print(f"Analyzing {len(data)} issues...\n")
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# Categorize and group by week
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issues_by_week = defaultdict(lambda: defaultdict(int))
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week_totals = defaultdict(int)
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week_order = []
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# Response tracking
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response_by_week = defaultdict(lambda: {
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'total': 0,
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||||
'with_response': 0,
|
||||
'no_response': 0
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||||
})
|
||||
|
||||
def get_week_label(date_str):
|
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"""Convert date to week label"""
|
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date = datetime.fromisoformat(date_str.replace('Z', '+00:00')).replace(tzinfo=None)
|
||||
|
||||
# Manual week grouping for clarity
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||||
if date >= datetime(2025, 12, 22):
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return "Week 51: Dec 22-26"
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||||
elif date >= datetime(2025, 12, 15):
|
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return "Week 50: Dec 15-21"
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||||
else:
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||||
return "Earlier"
|
||||
|
||||
def categorize_issue(item):
|
||||
"""Categorize an issue"""
|
||||
if item.get('pull_request'):
|
||||
return "PR"
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||||
|
||||
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"
|
||||
Reference in New Issue
Block a user