Playbook Discovery

Discover repeatable workflows from your historical data that AI agents can automate.

Description

This skill analyzes your business communication data (email, calendar, files, chat) to identify "playbooks" — documented, repeatable workflows with clear triggers, steps, and end states.

Why this matters: Before you can automate, you need to know what to automate. Most small business owners have dozens of repeatable workflows buried in their daily habits — they just haven't documented them. This skill surfaces those patterns.

Triggers

• "discover playbooks"
• "find workflows to automate"
• "analyze my email patterns"
• "what can I automate"
• "playbook discovery"
• "workflow analysis"
• User connects email/calendar and wants to find automation opportunities

Prerequisites

At least one of these data sources connected:

• Email — Gmail (gmail skill) or Microsoft 365 (Graph API)
• Calendar — Google Calendar (google-calendar skill) or Outlook
• Files — Google Drive, OneDrive, Dropbox
• Chat — Slack, Teams, Discord

More data sources = better pattern recognition.

Workflow

Phase 1: Data Collection

Collect data systematically to avoid API limits. Chunk by time period (monthly).

For each data source, extract:

Email (Inbox + Sent)

For each of the last 6 months:

• ALL inbox emails (aim for 100-200+ per month)
• ALL sent emails (aim for 100-200+ per month)
• Extract: subject lines, senders/recipients, dates, thread patterns
• Categorize by type: partner comms, internal, requests, approvals, technical

Calendar

Full 6-month period:

• All events with attendees, duration, recurrence
• Identify recurring meetings and cadence (weekly, bi-weekly, monthly)
• Note meeting types: 1:1s, team syncs, partner meetings, training
• Look for meeting sequences that precede deliverables
• Identify high-frequency attendees

Files

• Look for versioned documents (v1, v2, Draft 1, Final, etc.)
• Identify templates and recurring document types
• Note file modification patterns and naming conventions

Chat/Messaging

• Channel/conversation patterns
• Recurring discussion types
• Request/response flows

Phase 2: Pattern Recognition

Analyze collected data for these pattern types:

1. People Patterns
   • Who do they communicate with most?
   • Who are external partners vs internal team?
   • What's the escalation chain?
2. Topic Patterns
   • What subjects recur?
   • What types of requests come in repeatedly?
   • What themes dominate?
3. Temporal Patterns
   • What happens weekly? Monthly? Quarterly? Annually?
   • Are there seasonal workflows?
   • What's time-sensitive vs flexible?
4. Flow Patterns
   • What triggers action?
   • What sequences of steps repeat?
   • What are the request → response → deliverable chains?

Phase 3: Workflow Extraction

Group related patterns into candidate workflows. For each candidate, define:

Phase 4: Prioritization

Rank candidate workflows by:

• Frequency: How often does this happen? (daily > weekly > monthly)
• Business Impact: How important is this to their role/organization?
• Automation Potential: How repeatable and rule-based is it?
• Time Saved: How much human time does this consume?

Phase 5: Output

Present findings in this structure:

1. Data Summary

Emails analyzed: X,XXX (inbox: X,XXX, sent: X,XXX)
Calendar events: XXX
Files reviewed: XXX
Time period: [start] to [end]

2. Key Patterns Discovered

Major themes across people, topics, time, and flows.

3. Top Playbooks (4-6 recommended)

For each playbook:

## Playbook: [Name]

**Evidence:** What data supports this pattern?

**Trigger Conditions:**
- [Specific trigger 1]
- [Specific trigger 2]

**Step-by-Step Workflow:**
1. [Step 1]
2. [Step 2]
3. [Step 3]
...

**Inputs Required:**
- [Input 1]
- [Input 2]

**Outputs Produced:**
- [Output 1]
- [Output 2]

**Success Criteria:**
- [What does "done" look like?]

**Edge Cases & Escalation:**
- [When to escalate to human]
- [What can go wrong]

**Business Impact:**
[Why this matters — time saved, errors prevented, etc.]

4. Summary Table

Phase 6: Next Steps

After presenting playbooks, offer:

1. Create detailed documentation for each playbook
2. Identify which existing skills could implement each playbook
3. Prioritize which playbook to automate first
4. Design the automation architecture

Tips for Better Results

• More data = better patterns. Connect all available sources.
• 6 months minimum. Shorter periods miss seasonal patterns.
• Include sent mail. Your responses reveal your workflows.
• Don't filter. Let the analysis find the patterns.

Example Output

Playbook: Weekly Partner Status Report

Evidence: 47 emails with subject containing "weekly update" or "status report" sent every Monday between 9-11am to the same 5 recipients over 6 months.

Trigger: Monday 9am OR partner requests update

Steps:

1. Pull metrics from dashboard
2. Summarize key wins/blockers
3. Draft email with standard template
4. Send to partner distribution list

Automation Potential: HIGH — template-based, data-driven, predictable schedule

Related Skills

• gmail — Email data collection
• google-calendar — Calendar data collection
• slack-directory — Communication pattern analysis
• revenue-forecaster — If a discovered playbook drives or depends on revenue, feed the forecaster's output into it
• autoresearch — Once a playbook is running, use autoresearch to iteratively improve its output

The best automation starts with understanding what you already do.