openclaw-executive-assistant-webinar

name	openclaw-executive-assistant-webinar
description	Build local-first AI executive assistant workflows with OpenClaw for data intake, operational memory, and communications triage
triggers	["build an executive assistant with openclaw","set up openclaw local workflows","create data intake review system","generate operational memory logs","triage emails with openclaw","implement openclaw executive assistant","use openclaw for communications management","create markdown artifacts with openclaw"]

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/