Generating PR Age Charts

Build an interactive CLI tool that visualizes pull request age distribution for a GitHub repository using Copilot's built-in capabilities.

Runnable example: recipe/pr_visualization.py

cd recipe && pip install -r requirements.txt
# Auto-detect from current git repo
python pr_visualization.py

# Specify a repo explicitly
python pr_visualization.py --repo github/copilot-sdk

Example scenario

You want to understand how long PRs have been open in a repository. This tool detects the current Git repo or accepts a repo as input, then lets Copilot fetch PR data via the GitHub MCP Server and generate a chart image.

Prerequisites

pip install copilot-sdk

Usage

# Auto-detect from current git repo
python pr_breakdown.py

# Specify a repo explicitly
python pr_breakdown.py --repo github/copilot-sdk

Full example: pr_breakdown.py

#!/usr/bin/env python3

import subprocess
import sys
import os
from copilot import CopilotClient

# ============================================================================
# Git & GitHub Detection
# ============================================================================

def is_git_repo():
    try:
        subprocess.run(
            ["git", "rev-parse", "--git-dir"],
            check=True,
            capture_output=True
        )
        return True
    except (subprocess.CalledProcessError, FileNotFoundError):
        return False

def get_github_remote():
    try:
        result = subprocess.run(
            ["git", "remote", "get-url", "origin"],
            check=True,
            capture_output=True,
            text=True
        )
        remote_url = result.stdout.strip()

        # Handle SSH: git@github.com:owner/repo.git
        import re
        ssh_match = re.search(r"git@github\.com:(.+/.+?)(?:\.git)?$", remote_url)
        if ssh_match:
            return ssh_match.group(1)

        # Handle HTTPS: https://github.com/owner/repo.git
        https_match = re.search(r"https://github\.com/(.+/.+?)(?:\.git)?$", remote_url)
        if https_match:
            return https_match.group(1)

        return None
    except (subprocess.CalledProcessError, FileNotFoundError):
        return None

def parse_args():
    args = sys.argv[1:]
    if "--repo" in args:
        idx = args.index("--repo")
        if idx + 1 < len(args):
            return {"repo": args[idx + 1]}
    return {}

def prompt_for_repo():
    return input("Enter GitHub repo (owner/repo): ").strip()

# ============================================================================
# Main Application
# ============================================================================

def main():
    print("🔍 PR Age Chart Generator\n")

    # Determine the repository
    args = parse_args()
    repo = None

    if "repo" in args:
        repo = args["repo"]
        print(f"📦 Using specified repo: {repo}")
    elif is_git_repo():
        detected = get_github_remote()
        if detected:
            repo = detected
            print(f"📦 Detected GitHub repo: {repo}")
        else:
            print("⚠️  Git repo found but no GitHub remote detected.")
            repo = prompt_for_repo()
    else:
        print("📁 Not in a git repository.")
        repo = prompt_for_repo()

    if not repo or "/" not in repo:
        print("❌ Invalid repo format. Expected: owner/repo")
        sys.exit(1)

    owner, repo_name = repo.split("/", 1)

    # Create Copilot client - no custom tools needed!
    client = CopilotClient(log_level="error")
    client.start()

    session = client.create_session(
        model="gpt-5",
        system_message={
            "content": f"""
<context>
You are analyzing pull requests for the GitHub repository: {owner}/{repo_name}
The current working directory is: {os.getcwd()}
</context>

<instructions>
- Use the GitHub MCP Server tools to fetch PR data
- Use your file and code execution tools to generate charts
- Save any generated images to the current working directory
- Be concise in your responses
</instructions>
"""
        }
    )

    # Set up event handling
    def handle_event(event):
        if event["type"] == "assistant.message":
            print(f"\n🤖 {event['data']['content']}\n")
        elif event["type"] == "tool.execution_start":
            print(f"  ⚙️  {event['data']['toolName']}")

    session.on(handle_event)

    # Initial prompt - let Copilot figure out the details
    print("\n📊 Starting analysis...\n")

    session.send(prompt=f"""
      Fetch the open pull requests for {owner}/{repo_name} from the last week.
      Calculate the age of each PR in days.
      Then generate a bar chart image showing the distribution of PR ages
      (group them into sensible buckets like <1 day, 1-3 days, etc.).
      Save the chart as "pr-age-chart.png" in the current directory.
      Finally, summarize the PR health - average age, oldest PR, and how many might be considered stale.
    """)

    session.wait_for_idle()

    # Interactive loop
    print("\n💡 Ask follow-up questions or type \"exit\" to quit.\n")
    print("Examples:")
    print("  - \"Expand to the last month\"")
    print("  - \"Show me the 5 oldest PRs\"")
    print("  - \"Generate a pie chart instead\"")
    print("  - \"Group by author instead of age\"")
    print()

    while True:
        user_input = input("You: ").strip()

        if user_input.lower() in ["exit", "quit"]:
            print("👋 Goodbye!")
            break

        if user_input:
            session.send(prompt=user_input)
            session.wait_for_idle()

    client.stop()

if __name__ == "__main__":
    main()

How it works

Repository detection: Checks --repo flag → git remote → prompts user
No custom tools: Relies entirely on Copilot CLI's built-in capabilities:
- GitHub MCP Server - Fetches PR data from GitHub
- File tools - Saves generated chart images
- Code execution - Generates charts using Python/matplotlib or other methods
Interactive session: After initial analysis, user can ask for adjustments

Why this approach?

Aspect	Custom Tools	Built-in Copilot
Code complexity	High	Minimal
Maintenance	You maintain	Copilot maintains
Flexibility	Fixed logic	AI decides best approach
Chart types	What you coded	Any type Copilot can generate
Data grouping	Hardcoded buckets	Intelligent grouping

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