Openclaw Executive Assistant Webinar

OpenClaw Executive Assistant Webinar

Skill by ara.so — Hermes Skills collection.

Overview

This project provides starter files and a structured workshop for building a local-first AI executive assistant using OpenClaw. It demonstrates three core workflows:

1. Data intake review – Turn unknown files into trustworthy intake reports
2. Operational memory – Transform work residue into daily logs and weekly summaries
3. Offline communications triage – Convert exported emails into actionable lists

All workflows are local-only, produce reviewable markdown artifacts, and use copy/paste prompts with no live integrations.

Repository Structure

.
├── webinar-runbook.html              # Main workshop walkthrough
└── code-along/
    ├── INDEX.md
    ├── 01-data-intake-review/
    │   ├── incoming/                  # Files to inspect
    │   ├── prompts/intake-review.md   # Report generation instructions
    │   ├── outputs/                   # Generated reports
    │   └── expected/report-outline.md
    ├── 02-operational-memory/
    │   ├── inbox/                     # Work notes and residue
    │   ├── prompts/daily-log.md       # Daily log prompt
    │   ├── prompts/weekly-hype.md     # Weekly summary prompt
    │   ├── outputs/                   # Generated logs
    │   └── schedule/                  # Cron examples
    ├── 03-offline-communications-triage/
    │   ├── eml/                       # Exported email files
    │   ├── prompts/email-triage.md    # Triage instructions
    │   ├── outputs/                   # Triage reports
    │   └── expected/report-outline.md
    └── mission-control/               # Optional dashboard

Getting Started

Installation

git clone https://github.com/dandenney/webinars-build-your-own-executive-assistant-with-openclaw.git
cd webinars-build-your-own-executive-assistant-with-openclaw

Workshop Flow

1. Open webinar-runbook.html in a browser
2. Keep the code-along/ folder visible in your editor
3. Work through exercises sequentially
4. Copy prompts from prompts/ directories
5. Review generated artifacts in outputs/ directories

Exercise 1: Data Intake Review

Goal: Transform unknown incoming files into a structured intake report.

File Structure

01-data-intake-review/
├── incoming/           # Place files to review here
├── prompts/
│   └── intake-review.md
├── outputs/
│   └── intake-review.md  # Generated report
└── expected/
    └── report-outline.md

Usage Pattern

1. Place files to review in incoming/
2. Read the prompt from prompts/intake-review.md
3. Provide the prompt and file context to your AI assistant
4. Generate outputs/intake-review.md

Expected Output Format

The intake review should produce a markdown report containing:

• File inventory – List of all files with types and sizes
• Content summary – Brief description of each file's purpose
• Risk assessment – Security/privacy concerns
• Recommended actions – Next steps for each file
• Priority ranking – Ordered by urgency/importance

Exercise 2: Operational Memory

Goal: Create daily logs and weekly summaries from work residue.

File Structure

02-operational-memory/
├── inbox/              # Work notes, snippets, residue
├── prompts/
│   ├── daily-log.md
│   └── weekly-hype.md
├── outputs/
│   ├── daily-log.md
│   └── weekly-hype.md
└── schedule/
    ├── cron-examples.md
    └── heartbeat-note.md

Daily Log Pattern

1. Collect work residue in inbox/
2. Use prompts/daily-log.md to generate a daily log
3. Output to outputs/daily-log.md

Daily log structure:

• Date header
• Completed tasks
• In-progress work
• Blockers/questions
• Tomorrow's focus

Weekly Summary Pattern

1. Accumulate daily logs over the week
2. Use prompts/weekly-hype.md to generate a weekly summary
3. Output to outputs/weekly-hype.md

Weekly summary structure:

• Week range header
• Key accomplishments
• Metrics/progress
• Challenges addressed
• Next week priorities

Automation with Cron

Reference schedule/cron-examples.md for automation patterns:

# Daily log generation (5 PM weekdays)
0 17 * * 1-5 /path/to/generate-daily-log.sh

# Weekly summary (Friday 5 PM)
0 17 * * 5 /path/to/generate-weekly-summary.sh

Exercise 3: Offline Communications Triage

Goal: Convert exported email files into an actionable triage report.

File Structure

03-offline-communications-triage/
├── eml/                    # Exported .eml files
├── prompts/
│   └── email-triage.md
├── outputs/
│   └── email-triage.md     # Generated triage
└── expected/
    └── report-outline.md

Usage Pattern

1. Export emails as .eml files into eml/
2. Use prompts/email-triage.md with your AI assistant
3. Generate outputs/email-triage.md

Expected Triage Format

The email triage report should contain:

• Urgent actions – Emails requiring immediate response
• This week – Items to address within 5 business days
• Backlog – Lower-priority or FYI items
• Archive candidates – No action needed
• Summary counts – Total emails by category

Each email entry should include:

• Sender
• Subject
• Date received
• Recommended action
• Priority level

Key Principles

Local-First Architecture

All data stays on your machine:

• No cloud uploads
• No API calls to external services
• Reviewable markdown outputs
• Version-controllable artifacts

Copy/Paste Workflow

1. Navigate to exercise directory
2. Copy prompt from prompts/*.md
3. Paste into AI assistant (Claude, ChatGPT, etc.)
4. Provide file context as needed
5. Review and save output to outputs/

Markdown Artifacts

All outputs are markdown for:

• Easy version control with Git
• Plain-text searchability
• Cross-platform compatibility
• Human readability

Common Patterns

Adding Custom Prompts

Create new prompt files following the structure:

# [Task Name]

## Context
[What you're working with]

## Goal
[What you want to produce]

## Instructions
[Step-by-step guidance]

## Output Format
[Expected structure]

Chaining Workflows

Combine exercises for compound workflows:

# 1. Review incoming files
# outputs/intake-review.md

# 2. Log the review work
# outputs/daily-log.md (includes intake work)

# 3. Triage any emails found
# outputs/email-triage.md

Customizing Output Formats

Edit prompt files to adjust output structure:

• Change heading levels
• Add custom sections
• Modify priority categories
• Include additional metadata

Troubleshooting

Missing Expected Output

Issue: AI generates different format than expected

Solution: Reference expected/*.md files to see the target structure, then refine your prompt with specific format requirements.

File Context Too Large

Issue: Too many files to process at once

Solution:

• Break into batches
• Process high-priority files first
• Create summary reports for large sets

Inconsistent Daily Logs

Issue: Daily logs vary in format day-to-day

Solution:

• Keep prompt files consistent
• Use the same AI model
• Reference previous logs as examples
• Create a template in the prompt

Cron Jobs Not Running

Issue: Automated generation fails

Solution:

• Check cron syntax with crontab -l
• Verify script paths are absolute
• Ensure scripts have execute permissions: chmod +x script.sh
• Check logs in /var/log/cron or system journal

Best Practices

1. Review all AI output – Never blindly accept generated reports
2. Version control artifacts – Commit outputs to track changes over time
3. Iterate on prompts – Refine instructions based on output quality
4. Keep raw inputs – Preserve original files alongside processed outputs
5. Regular cleanup – Archive old outputs to maintain focus

Integration Ideas

While this workshop is local-only, you can extend it with:

• File watching scripts to auto-trigger processing
• Static site generation from markdown outputs
• Notification systems when new reports are ready
• Dashboard aggregation in mission-control/
• Integration with note-taking tools (Obsidian, Logseq)

Related Resources

• DataCamp webinar: https://www.datacamp.com/webinars/build-your-own-executive-assistant-with-openclaw
• OpenClaw documentation (check project homepage)
• Markdown syntax reference: https://www.markdownguide.org/