Build Python scripts that automate repetitive tasks. Connect Claude to Google Drive via MCP for direct file access. Create custom skills that transform Claude into specialized assistants.

Automation and Advanced Integration: Scripts That Save 5-10 Hours Weekly

Manual AI interaction is powerful. Automated AI interaction is transformational.

When you automate repetitive tasks—status reports, data extraction, document generation—you don't just save time on individual tasks. You eliminate entire categories of work from your to-do list.

This chapter shows you how.

The Automation Mindset

Ask yourself: "What do I do every week that follows a predictable pattern?"

Common answers for project managers:

Generate weekly status reports
Update project documentation
Process meeting notes into action items
Create stakeholder communications
Compile progress metrics

Each of these can be automated.

Python Scripts for PM Automation

Prerequisites

Python 3.8 or higher installed
Anthropic API key
Basic Python familiarity (or willingness to learn)

The Anthropic SDK

Install the SDK:

pip install anthropic

Basic Script Template

import anthropic

client = anthropic.Anthropic()

def ask_claude(prompt, context=""):
    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=4096,
        messages=[
            {"role": "user", "content": f"{context}\n\n{prompt}"}
        ]
    )
    return message.content[0].text

# Example: Generate status report
weekly_notes = open("notes/this-week.md").read()
context = open("project-context.md").read()

report = ask_claude(
    prompt="Generate a weekly status report in standard format.",
    context=f"Project Context:\n{context}\n\nThis Week's Notes:\n{weekly_notes}"
)

with open("reports/weekly-status.md", "w") as f:
    f.write(report)

print("Status report generated!")

Status Report Automation

Complete script for automated weekly reporting:

import anthropic
from datetime import datetime
import os

client = anthropic.Anthropic()

def generate_status_report():
    # Load project context
    with open("project-context.md") as f:
        context = f.read()

    # Load this week's notes
    with open("notes/weekly-capture.md") as f:
        notes = f.read()

    prompt = """Generate a weekly status report with these sections:

    1. Executive Summary (3-4 sentences)
    2. Status: [Green/Yellow/Red] with explanation
    3. Key Accomplishments (bullets)
    4. Upcoming Priorities (bullets)
    5. Risks/Issues Requiring Attention
    6. Decisions Needed

    Format for executive review. Be concise."""

    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=2048,
        messages=[{
            "role": "user",
            "content": f"Project Context:\n{context}\n\nThis Week's Notes:\n{notes}\n\n{prompt}"
        }]
    )

    report = message.content[0].text

    # Save with timestamp
    date_str = datetime.now().strftime("%Y-%m-%d")
    filename = f"reports/status-{date_str}.md"

    with open(filename, "w") as f:
        f.write(report)

    return filename

if __name__ == "__main__":
    output = generate_status_report()
    print(f"Report generated: {output}")

Meeting Notes Processing

import anthropic

client = anthropic.Anthropic()

def process_meeting_notes(meeting_file):
    with open(meeting_file) as f:
        raw_notes = f.read()

    prompt = """Process these meeting notes and create:

    1. **Summary** (2-3 sentences)
    2. **Key Decisions** (bulleted list with who decided)
    3. **Action Items** formatted as:
       - [ ] [Task] - Owner: [Name] - Due: [Date]
    4. **Parking Lot** (items for later discussion)

    Extract everything accurately from the notes."""

    message = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=2048,
        messages=[{
            "role": "user",
            "content": f"Meeting Notes:\n{raw_notes}\n\n{prompt}"
        }]
    )

    return message.content[0].text

# Process and save
processed = process_meeting_notes("meetings/steering-committee-raw.md")
with open("meetings/steering-committee-processed.md", "w") as f:
    f.write(processed)

MCP: Model Context Protocol

MCP (Model Context Protocol) enables Claude to directly access external systems—including Google Drive, databases, and APIs.

What MCP Enables

Without MCP:

You download a file from Google Drive
You upload it to Claude
You get output
You manually save it

With MCP:

Claude reads directly from Google Drive
Claude writes output directly to Google Drive
No manual file handling

Setting Up MCP for Google Drive

Step 1: Install MCP Google Drive Server

npm install -g @anthropic-ai/mcp-server-gdrive

Step 2: Configure Authentication

Follow the Google Drive API setup to create credentials. This requires:

Google Cloud Console project
OAuth 2.0 credentials
API enablement

Step 3: Configure Claude

Add to your Claude configuration:

{
  "mcp_servers": {
    "gdrive": {
      "command": "mcp-server-gdrive",
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/credentials.json"
      }
    }
  }
}

Step 4: Use in Claude

Now Claude can:

Read files from Google Drive
Write files to Google Drive
Search for documents
Access shared folders

MCP Workflow Example

You: "Read the latest status report from the Project Alpha folder in Google Drive and summarize key risks."

Claude: [Uses MCP to access Google Drive, reads the document, provides summary]

You: "Update that document with the risk mitigation plans we discussed and save a new version."

Claude: [Reads current version, adds content, writes new version to Drive]

Custom Skills

Skills are pre-configured Claude capabilities you can invoke on command.

Creating a PM Skill Library

Status Report Specialist:

# Skill: Status Report Writer

When invoked, this skill:

1. Asks for the reporting period
2. Requests raw notes or accepts uploaded files
3. Generates status report in standard format
4. Offers to create executive summary and team versions
5. Suggests improvements based on past reports

Default format:
- Executive Summary
- Green/Yellow/Red Status
- Accomplishments
- Upcoming Work
- Risks and Issues
- Decisions Needed

Always ask clarifying questions before generating.

Meeting Processor:

# Skill: Meeting Processor

When invoked, this skill:

1. Accepts raw meeting notes (typed or transcribed)
2. Extracts and formats:
   - Meeting summary
   - Decisions made
   - Action items with owners and dates
   - Parking lot items
3. Generates follow-up email draft
4. Creates action item tracking entries

Output format optimized for direct distribution.

Risk Analyst:

# Skill: Risk Analyst

When invoked, this skill:

1. Reviews provided project information
2. Identifies potential risks not currently tracked
3. For each risk, provides:
   - Description
   - Probability assessment
   - Impact assessment
   - Suggested mitigation
   - Early warning indicators
4. Prioritizes risks by exposure
5. Suggests risk register updates

Invoking Skills

With skills configured, invoke them:

/status-report
/process-meeting
/risk-analysis

Claude adopts the skill persona and workflow automatically.

Scheduled Automation

Friday Afternoon Automation

Set up automated reports every Friday:

Linux/Mac (cron):

0 16 * * 5 python /path/to/generate_status_report.py

Windows (Task Scheduler): Create scheduled task running your Python script weekly.

Daily Digest

import anthropic
from datetime import datetime

def generate_daily_digest():
    # Gather inputs from various sources
    inputs = {
        "task_updates": read_task_system(),
        "emails_flagged": get_flagged_emails(),
        "calendar_tomorrow": get_tomorrow_calendar()
    }

    prompt = """Generate a daily digest including:
    1. Task progress summary
    2. Emails requiring attention
    3. Tomorrow's key meetings and prep needed
    4. Suggested priorities for tomorrow"""

    # Generate digest
    # Send to yourself via email or save to file

Time Savings Analysis

| Automated Task | Manual Time | Automated Time | Weekly Savings | |----------------|-------------|----------------|----------------| | Status report | 90 min | 10 min (review) | 80 min | | Meeting notes (3/week) | 60 min | 15 min | 45 min | | Stakeholder emails (5/week) | 50 min | 15 min | 35 min | | Risk register update | 45 min | 10 min | 35 min | | Daily planning | 75 min | 20 min | 55 min | | Total | 320 min | 70 min | ~4 hours |

With additional automation of custom tasks, savings reach 5-10 hours weekly.

Building Your Automation Library

Start with highest-impact automations:

Week 1: Automate weekly status report generation

Week 2: Add meeting notes processing

Week 3: Implement stakeholder communication templates

Week 4: Connect to file systems via MCP

Ongoing: Add automations as you identify repetitive patterns

From Scripts to Command Center

Individual scripts are powerful. A unified interface that combines them all is transformational.

The next chapter shows you how to build your personal AI command center—a custom application that consolidates all these capabilities into one professional interface.

Ready to Transform Your Project Management Practice?

This article is part of a comprehensive guide to AI-powered project management. Learn how to save 10-15 hours per week, automate repetitive workflows, and build your own private AI command center.

Explore the Complete Book: Claude for Project Managers