# Openclaw Executive Assistant Workshop
> Build local-first AI executive assistant workflows with OpenClaw for data intake, operational memory, and communications triage
- Skill: `aradotso/openclaw-executive-assistant-workshop` (Agent Skill)
- Install (CLI): `npx skillmds@latest add aradotso/openclaw-executive-assistant-workshop`
- Raw SKILL.md: https://api.skillmd.com/api/skills/aradotso/openclaw-executive-assistant-workshop/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: aradotso (https://skillmd.com/u/aradotso)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/aradotso/openclaw-executive-assistant-workshop
---
# OpenClaw Executive Assistant Workshop
> Skill by [ara.so](https://ara.so) — Hermes Skills collection.
This skill covers building local-first executive assistant workflows using OpenClaw. The workshop focuses on three core patterns: data intake review, operational memory (daily/weekly logs), and offline communications triage. All workflows stay local, produce markdown artifacts, and require no live integrations.
## What This Project Does
The OpenClaw executive assistant workshop teaches you to:
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** — Process exported emails into actionable lists
All exercises use copy/paste prompts, local folders, and generate reviewable markdown outputs.
## Repository Structure
```text
.
├── webinar-runbook.html # Main walkthrough guide
└── code-along/
├── INDEX.md
├── 01-data-intake-review/
│ ├── incoming/ # Files to inspect
│ ├── prompts/intake-review.md # Prompt instructions
│ ├── outputs/ # Generated reports
│ └── expected/report-outline.md
├── 02-operational-memory/
│ ├── inbox/ # Work notes and residue
│ ├── prompts/daily-log.md
│ ├── prompts/weekly-hype.md
│ ├── outputs/
│ ├── schedule/cron-examples.md
│ └── schedule/heartbeat-note.md
├── 03-offline-communications-triage/
│ ├── eml/ # Exported email files
│ ├── prompts/email-triage.md
│ ├── outputs/
│ └── expected/report-outline.md
└── mission-control/ # Optional dashboard
```
## Installation & Setup
### Clone the Repository
```bash
git clone https://github.com/dandenney/webinars-build-your-own-executive-assistant-with-openclaw.git
cd webinars-build-your-own-executive-assistant-with-openclaw
```
### Prerequisites
- OpenClaw AI assistant (Claude, ChatGPT, or similar)
- Text editor
- Web browser (for viewing `webinar-runbook.html`)
No additional dependencies required — this is a prompt-based workshop.
## Workshop Flow
### Exercise 1: Data Intake Review
Turn unknown files in `incoming/` into a structured intake report.
**Location:** `code-along/01-data-intake-review/`
**Steps:**
1. Review files in `incoming/` folder
2. Copy prompt from `prompts/intake-review.md`
3. Provide prompt and folder contents to your AI assistant
4. Save output to `outputs/intake-review.md`
**Expected Output Structure:**
```markdown
# Data Intake Review
## Summary
Brief overview of files received
## Files Analyzed
- filename1.ext — description and recommendation
- filename2.ext — description and recommendation
## Priority Actions
1. Action item based on file contents
2. Follow-up needed
## Next Steps
Recommendations for processing
```
**Example Prompt Pattern:**
```markdown
Review the following files in my incoming folder and create an intake report:
[List files and relevant contents]
For each file:
- Identify type and purpose
- Extract key information
- Note any actions needed
- Flag urgency or importance
Output a markdown report with summary, file details, and action items.
```
### Exercise 2: Operational Memory
Transform daily work notes into momentum documents.
**Location:** `code-along/02-operational-memory/`
#### Daily Log
**Steps:**
1. Place work residue (notes, snippets, thoughts) in `inbox/`
2. Copy prompt from `prompts/daily-log.md`
3. Generate daily log
4. Save to `outputs/daily-log.md`
**Expected Output:**
```markdown
# Daily Log — YYYY-MM-DD
## Completed Today
- Task or achievement
- Progress made on project X
## In Progress
- Item being worked on
- Blocked on Y
## Insights & Notes
- Learning or observation
- Idea to explore
## Tomorrow's Focus
- Priority 1
- Priority 2
```
#### Weekly Hype Summary
**Steps:**
1. Collect daily logs from the week
2. Copy prompt from `prompts/weekly-hype.md`
3. Generate weekly summary
4. Save to `outputs/weekly-hype.md`
**Expected Output:**
```markdown
# Weekly Hype — Week of YYYY-MM-DD
## Wins This Week
- Major accomplishment
- Milestone reached
## Key Themes
Pattern or focus area that emerged
## Momentum Builders
What's giving energy and progress
## Carry Forward
What needs attention next week
```
#### Automation with Cron
Reference: `schedule/cron-examples.md`
**Daily log generation:**
```bash
# Run every weekday at 5 PM
0 17 * * 1-5 /path/to/generate-daily-log.sh
```
**Weekly summary:**
```bash
# Run every Friday at 6 PM
0 18 * * 5 /path/to/generate-weekly-hype.sh
```
**Example script pattern:**
```bash
#!/bin/bash
# generate-daily-log.sh
INBOX_DIR="$HOME/code-along/02-operational-memory/inbox"
OUTPUT_DIR="$HOME/code-along/02-operational-memory/outputs"
PROMPT_FILE="$HOME/code-along/02-operational-memory/prompts/daily-log.md"
DATE=$(date +%Y-%m-%d)
OUTPUT_FILE="$OUTPUT_DIR/daily-log-$DATE.md"
# Collect inbox contents
CONTEXT=$(cat "$INBOX_DIR"/*.txt 2>/dev/null)
# Call AI assistant via API or CLI
# (Replace with your OpenClaw integration method)
echo "Generating daily log for $DATE..."
# Example: pipe prompt + context to AI CLI tool
cat "$PROMPT_FILE" | your-ai-cli --context "$CONTEXT" > "$OUTPUT_FILE"
echo "Daily log saved to $OUTPUT_FILE"
```
### Exercise 3: Offline Communications Triage
Process exported emails into actionable triage reports.
**Location:** `code-along/03-offline-communications-triage/`
**Steps:**
1. Export emails as `.eml` files to `eml/` folder
2. Copy prompt from `prompts/email-triage.md`
3. Provide email contents to AI assistant
4. Save triage report to `outputs/email-triage.md`
**Expected Output:**
```markdown
# Email Triage Report
## Urgent Actions Required
- From: sender@example.com | Subject: Critical issue
Action: Respond by EOD
## Follow-up Needed
- From: colleague@company.com | Subject: Project update
Action: Schedule call this week
## FYI / Low Priority
- From: newsletter@service.com | Subject: Weekly digest
Action: Read when time permits
## Can Archive
- From: automated@system.com | Subject: Confirmation
Action: None, archive
## Summary Stats
- Total emails: 15
- Urgent: 2
- Follow-up: 5
- FYI: 6
- Archive: 2
```
**Example Triage Prompt:**
```markdown
Analyze the following exported emails and create a triage report:
[Email contents from .eml files]
For each email:
- Extract sender, subject, key points
- Determine priority level
- Suggest action needed
- Estimate response timeframe
Group by urgency: Urgent Actions, Follow-up Needed, FYI, Can Archive.
Include summary statistics.
```
## Key Patterns
### Local-First Workflow
```bash
# Directory structure for each exercise
exercise/
├── incoming/ # Input files
├── prompts/ # AI instructions
├── outputs/ # Generated markdown
└── expected/ # Reference examples
```
### Prompt Engineering Pattern
All prompts follow this structure:
1. **Context:** What you're working with
2. **Task:** What to analyze or generate
3. **Output format:** Specific markdown structure
4. **Quality criteria:** What makes a good result
### Markdown Artifact Generation
All outputs are markdown files for:
- Version control tracking
- Easy diff viewing
- Plain text searchability
- No vendor lock-in
## Configuration
### Custom Prompt Templates
Edit prompt files in each exercise's `prompts/` folder:
```markdown
# prompts/custom-intake.md
Review these files with focus on [YOUR_CRITERIA]:
[FILE_CONTENTS]
Generate a report with:
1. [YOUR_SECTION_1]
2. [YOUR_SECTION_2]
3. [YOUR_SECTION_3]
```
### Output Customization
Modify expected output structure by updating `expected/` reference files.
## Common Issues & Troubleshooting
### Issue: Prompt Not Generating Expected Output
**Solution:** Check that you're including:
- Full context from input files
- Clear output format specification
- Examples from `expected/` folder
### Issue: Daily Log Missing Important Items
**Solution:** Ensure all work residue is in `inbox/` before generation. Create a checklist:
```markdown
## Pre-Log Checklist
- [ ] Notes from meetings
- [ ] Code commit messages
- [ ] Slack/email snippets
- [ ] TODO items completed
- [ ] Ideas or blockers
```
### Issue: Email Triage Misclassifying Priority
**Solution:** Enhance prompt with specific criteria:
```markdown
Priority levels:
- URGENT: deadline < 24hrs, blocks others, executive request
- FOLLOW-UP: deadline < 1 week, requires response
- FYI: informational, no response needed
- ARCHIVE: confirmation, automated, already resolved
```
### Issue: Weekly Summary Too Generic
**Solution:** Include more context signals in prompt:
```markdown
For each day's log, identify:
- Completed items (look for "done", "shipped", "merged")
- Momentum patterns (recurring themes, growing projects)
- Energy indicators (excited, blocked, breakthrough)
- Connections (how items relate across days)
```
## Best Practices
1. **Review Before Saving:** Always review AI-generated outputs before committing
2. **Iterate Prompts:** Refine prompts based on output quality
3. **Version Control:** Git-track all prompts and outputs for improvement tracking
4. **Schedule Consistency:** Run daily logs at same time each day
5. **Folder Hygiene:** Clear `inbox/` after processing, archive old outputs
## Integration Tips
### Git Workflow
```bash
# Track generated artifacts
git add code-along/*/outputs/*.md
# Commit with context
git commit -m "Daily log 2026-05-11: shipped feature X, blocked on Y"
# Review changes over time
git log --oneline -- code-along/02-operational-memory/outputs/
```
### Dashboard Setup
Create a simple `mission-control/index.html`:
```html
Executive Assistant Dashboard
Mission Control
```
## Additional Resources
- **Main Walkthrough:** Open `webinar-runbook.html` in browser
- **Exercise Index:** `code-along/INDEX.md`
- **DataCamp Webinar:** https://www.datacamp.com/webinars/build-your-own-executive-assistant-with-openclaw
---
This skill enables AI coding agents to guide developers through building practical, local-first executive assistant workflows using OpenClaw patterns.