Patterns

Patterns & Best Practices #

Understanding these core patterns will help you design more effective playbooks and avoid common pitfalls.

Fundamental Playbook Patterns #

The Reporter Pattern #

Ideal for regular status updates and summaries:

When to use: Daily/weekly summaries, team updates, performance reports

Key characteristics:

  • Gather metrics and activity data from multiple sources
  • Calculate trends and changes over time
  • Format professional summaries with consistent templates
  • Distribute to stakeholders on a regular schedule
  • Handle edge cases (no data, weekends, holidays)

Example applications:

  • Daily customer call summaries
  • Weekly development activity reports
  • Monthly business performance dashboards
  • Team standup automation

The Monitor Pattern #

Great for ongoing surveillance and alerting:

When to use: Anomaly detection, threshold monitoring, urgent alerts

Key characteristics:

  • Check current status against baselines or thresholds
  • Compare to historical data and normal ranges
  • Identify anomalies, changes, or concerning trends
  • Generate alerts when conditions are met
  • Log activity for trend analysis and learning

Example applications:

  • Performance monitoring dashboards
  • Support ticket escalation alerts
  • Sales pipeline warnings
  • System health monitoring

The Collector Pattern #

Perfect for gathering and synthesizing information:

When to use: Research, competitive intelligence, content curation

Key characteristics:

  • Connect to multiple external data sources
  • Query for relevant information using filters
  • Filter, organize, and deduplicate data
  • Synthesize findings with clear insights
  • Distribute formatted results to stakeholders

Example applications:

  • Daily news summaries
  • Competitive intelligence tracking
  • Industry trend analysis
  • Social media monitoring

The Orchestrator Pattern #

Ideal for complex multi-step workflows:

When to use: Process automation, complex business logic, multi-system coordination

Key characteristics:

  • Coordinate activities across multiple tools and systems
  • Handle dependencies and conditional logic
  • Manage error conditions and retry mechanisms
  • Scale processing based on data volume
  • Maintain state and audit trails

Example applications:

  • Customer onboarding workflows
  • Sales pipeline automation
  • Content publishing workflows
  • Data synchronization processes

Output Template Best Practices #

Use Consistent Structure #

Design templates that are easy to scan and understand:

📊 [Report Type] - [Date/Period]

Key Metrics: • [Metric 1]: [Value] ([Change from previous]) • [Metric 2]: [Value] ([Change from previous])

Highlights: • [Most important insight or achievement] • [Notable change or trend]

Details: [Structured breakdown of information]

Action Items: • [@Owner] [Specific task] - Due: [date]

Include Context and Meaning #

Don’t just report numbers—explain what they mean:

  • Good: “Support tickets: 23”
  • Better: “Support tickets: 23 (↑15% from last week, mostly billing questions)”

Design for Your Audience #

Consider who will read the output and what they need:

  • Executives: High-level metrics, trends, strategic implications
  • Team leads: Individual performance, blockers, action items
  • Individual contributors: Personal updates, task details, context

Handle Edge Cases Gracefully #

Plan for common scenarios:

  • No data available: “No customer calls scheduled today”
  • Weekends/holidays: “Next update will cover Monday-Wednesday activity”
  • Errors: “Unable to fetch GitHub data, using cached information”

Timing and Delivery Best Practices #

Schedule Based on Need #

Consider when people actually need the information:

  • Before meetings: Send standup summaries 30 minutes before daily standup
  • Start of day: Deliver overnight activity summaries by 8 AM
  • End of week: Friday afternoon summaries for weekly planning
  • Real-time: Immediate alerts for urgent issues

Choose the Right Channel #

Match delivery method to content and audience:

  • Team channels: Shared updates, general announcements
  • Direct messages: Personal updates, sensitive information
  • Email: External stakeholders, formal reports
  • Archives: Google Drive, Notion for historical reference

Respect Communication Preferences #

  • Use threading for follow-up information
  • Tag people only when action is required
  • Consider time zones for global teams
  • Allow opt-out mechanisms for non-essential updates

Data Quality and Reliability #

Validate Your Sources #

  • Check data freshness and availability
  • Cross-reference information when possible
  • Handle API failures and rate limits gracefully
  • Provide confidence indicators for uncertain data

Implement Error Handling #

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## Steps

- Try to fetch data from primary source
   - If unavailable, use backup source or cached data
   - Log any issues for investigation

- If no data is available at all:
   - Skip the update rather than sending empty information
   - Optionally notify about the missing data

Plan for Scale #

  • Consider what happens as data volume grows
  • Implement pagination for large result sets
  • Use efficient queries and filtering
  • Monitor execution time and optimize as needed

Iterative Improvement #

Start Simple, Then Enhance #

  1. Version 1: Basic data collection and formatting
  2. Version 2: Add trend analysis and insights
  3. Version 3: Include predictive elements and recommendations
  4. Version 4: Optimize for performance and add advanced features

Gather Feedback #

  • Ask stakeholders what’s most valuable
  • Monitor which information gets acted upon
  • Track whether the automation saves time
  • Adjust format and content based on usage

Measure Success #

  • Time saved: How much manual work is eliminated?
  • Decision speed: Are people making faster decisions?
  • Visibility: Are problems identified sooner?
  • Engagement: Do people read and act on the output?

Common Pitfalls to Avoid #

Information Overload #

  • Problem: Sending too much information, too frequently
  • Solution: Focus on actionable insights, allow customization

Poor Timing #

  • Problem: Updates arrive when people can’t act on them
  • Solution: Schedule based on when decisions are made

Inconsistent Format #

  • Problem: Different structures make information hard to process
  • Solution: Use templates and maintain consistent formatting

Ignoring Edge Cases #

  • Problem: Playbooks break or send confusing messages when data is missing
  • Solution: Plan for common failure scenarios upfront

Over-Automation #

  • Problem: Automating everything without considering human judgment needs
  • Solution: Focus on repetitive, rules-based tasks first

Building Your Playbook Strategy #

Assessment Questions #

Before building a playbook, ask:

  1. What manual task am I doing repeatedly?
  2. Where is the data I need, and how reliable is it?
  3. Who needs this information, and when do they need it?
  4. What format would be most useful for decision-making?
  5. What could go wrong, and how should I handle it?

Start with High-Impact, Low-Complexity #

Prioritize playbooks that:

  • Save significant time on repetitive tasks
  • Use reliable, easily accessible data sources
  • Have clear, measurable success criteria
  • Can be implemented without complex business logic

Plan for Evolution #

Design playbooks that can grow and improve:

  • Use modular steps that can be enhanced independently
  • Collect metrics on usage and effectiveness
  • Plan regular reviews and updates
  • Document learnings for future playbooks

Ready to put these patterns into practice? Start with Quick Start Examples or explore specific use cases in Communication Examples and Advanced Examples.