openclaw-executive-assistant-local

name	openclaw-executive-assistant-local
description	Build local-first AI executive assistant workflows with OpenClaw for data intake, operational memory, and communications triage
triggers	["how do I set up a local executive assistant with OpenClaw","create an intake review workflow for unknown files","build operational memory with daily and weekly logs","triage emails offline using OpenClaw","set up local-only AI assistant workflows","create markdown artifacts from work residue","build a communications triage system","use OpenClaw for executive assistant tasks"]

OpenClaw Executive Assistant (Local)

Skill by ara.so — Hermes Skills collection.

Overview

This project provides a local-first OpenClaw workflow system for building AI-powered executive assistant capabilities. It focuses on three core operations:

1. Data intake review - Transform unknown files into structured intake reports
2. Operational memory - Convert work residue into daily logs and weekly summaries
3. Offline communications triage - Process exported emails into action lists

All operations use local files only, produce reviewable markdown artifacts, and require no live integrations.

Repository Structure

code-along/
├── INDEX.md
├── 01-data-intake-review/
│   ├── incoming/          # Files to inspect
│   ├── prompts/           # Prompt templates
│   ├── outputs/           # Generated reports
│   └── expected/          # Reference outputs
├── 02-operational-memory/
│   ├── inbox/             # Notes and work residue
│   ├── prompts/           # Daily/weekly prompts
│   ├── outputs/           # Generated logs
│   └── schedule/          # Cron examples
├── 03-offline-communications-triage/
│   ├── eml/               # Exported email files
│   ├── prompts/           # Triage prompts
│   ├── outputs/           # Triage reports
│   └── expected/          # Reference outputs
└── mission-control/       # Dashboard (optional)

Exercise 1: Data Intake Review

Purpose

Transform unknown files in an incoming/ folder into a trustworthy intake report.

Workflow

1. Place files to review:

# Add files to the incoming folder
cp ~/Downloads/unknown-file.pdf code-along/01-data-intake-review/incoming/

2. Use the intake review prompt:

The prompt template is in prompts/intake-review.md. Pass it to your AI assistant along with the contents of incoming/:

# Intake Review Prompt

You are an executive assistant performing a data intake review.

Review all files in the incoming/ folder and produce a report that includes:

1. File inventory (name, type, size, date)
2. Content summary for each file
3. Suggested categorization
4. Action items or next steps
5. Priority flags (urgent, routine, archive)

Output format: Markdown
Output location: outputs/intake-review.md

3. Expected output structure:

# Data Intake Review
*Generated: YYYY-MM-DD*

## Summary
- Total files: X
- Urgent items: Y
- Requires action: Z

## File Inventory

### [filename.ext]
- **Type:** Document/Image/Data
- **Size:** XXX KB
- **Date:** YYYY-MM-DD
- **Summary:** Brief content description
- **Category:** Work/Personal/Archive
- **Action:** Review/File/Respond
- **Priority:** High/Medium/Low

[Repeat for each file]

## Recommended Actions
1. ...
2. ...

Exercise 2: Operational Memory

Purpose

Convert daily work residue into structured logs and weekly summaries.

Daily Log Workflow

1. Add work residue to inbox:

# Add notes, snippets, or quick captures
echo "Met with design team - new mockups ready" > code-along/02-operational-memory/inbox/note-$(date +%Y%m%d).txt

2. Use the daily log prompt (prompts/daily-log.md):

# Daily Log Prompt

You are an executive assistant creating a daily work log.

Review all items in the inbox/ folder from today and produce:

1. **Date header**
2. **Wins** - Completed items
3. **Progress** - Items in motion
4. **Blockers** - Issues or delays
5. **Tomorrow** - Planned next actions
6. **Notes** - Observations or reminders

Output format: Markdown
Output location: outputs/daily-log.md
Filename pattern: daily-YYYY-MM-DD.md

3. Expected daily log output:

# Daily Log: 2026-05-11

## Wins
- ✅ Completed data intake review system
- ✅ Shipped v2.1 of client dashboard

## Progress
- 🔄 OpenClaw workshop prep (80% complete)
- 🔄 Q2 planning document (draft stage)

## Blockers
- ⚠️ Waiting on legal review for contract
- ⚠️ Need API keys from DevOps

## Tomorrow
- [ ] Finalize workshop slides
- [ ] Review Q2 budget proposal
- [ ] Team sync at 2pm

## Notes
- Design team shared new mockups in Figma
- Consider async standup format for remote team

Weekly Summary Workflow

Use the weekly hype prompt (prompts/weekly-hype.md):

# Weekly Hype Prompt

You are an executive assistant creating a weekly summary.

Review all daily logs from this week (outputs/daily-*.md) and produce:

1. **Week of [date range]**
2. **Highlights** - Major wins and milestones
3. **Momentum** - Projects advancing
4. **Attention needed** - Recurring blockers
5. **Next week focus** - Priorities for the week ahead
6. **Metrics** (optional) - Quantifiable progress

Output format: Markdown
Output location: outputs/weekly-hype.md
Filename pattern: weekly-YYYY-Www.md

Automation Example

Schedule daily log generation with cron (see schedule/cron-examples.md):

# Run daily at 6pm
0 18 * * * cd ~/openclaw-assistant && ./generate-daily-log.sh

# generate-daily-log.sh example:
#!/bin/bash
DATE=$(date +%Y-%m-%d)
AI_PROMPT=$(cat code-along/02-operational-memory/prompts/daily-log.md)
# Pass inbox contents and prompt to your AI CLI tool
# ai-cli "$AI_PROMPT" --context "code-along/02-operational-memory/inbox/*" \
#   > "code-along/02-operational-memory/outputs/daily-$DATE.md"

Exercise 3: Offline Communications Triage

Purpose

Process exported email files into structured action lists.

Workflow

1. Export emails to .eml format:

# Place exported emails in the eml/ folder
cp ~/exported-emails/*.eml code-along/03-offline-communications-triage/eml/

2. Use the email triage prompt (prompts/email-triage.md):

# Email Triage Prompt

You are an executive assistant performing email triage.

Review all .eml files in the eml/ folder and produce:

1. **Urgent** - Requires immediate response
2. **Action Required** - Needs response (24-48h)
3. **FYI** - Informational, no action needed
4. **Delegate** - Should be handled by someone else
5. **Archive** - Safe to file away

For each email include:
- From/Subject
- Brief summary
- Suggested response or action
- Priority level

Output format: Markdown
Output location: outputs/email-triage.md

3. Expected triage output:

# Email Triage Report
*Generated: YYYY-MM-DD HH:MM*

## Urgent (Response Today)

### From: [e-mail] | Re: Production Issue
- **Summary:** Database timeout errors affecting users
- **Action:** Coordinate with DevOps for immediate fix
- **Priority:** 🔴 Critical

## Action Required (24-48h)

### From: [e-mail] | Re: Contract Review
- **Summary:** Q2 vendor contract needs signature
- **Action:** Review terms, sign if acceptable
- **Priority:** 🟡 High

## FYI (No Action)

### From: [e-mail] | Re: Weekly Newsletter
- **Summary:** Company updates and team wins
- **Action:** None - informational
- **Priority:** 🟢 Low

## Delegate

### From: [e-mail] | Re: Candidate Pipeline
- **Summary:** Three candidates ready for interviews
- **Action:** Forward to hiring manager Sarah
- **Priority:** 🟡 Medium

## Archive

[Emails that can be filed with no action]

Common Patterns

Pattern 1: Copy-Paste Workflow

1. Open AI assistant (Claude, ChatGPT, etc.)
2. Copy prompt from prompts/*.md
3. Attach or paste relevant files from incoming/inbox/eml/
4. Run generation
5. Save output to outputs/*.md
6. Review and edit as needed

Pattern 2: Scripted Automation

#!/bin/bash
# automated-intake.sh

PROMPT=$(cat code-along/01-data-intake-review/prompts/intake-review.md)
FILES=$(ls code-along/01-data-intake-review/incoming/*)

# Use your AI CLI tool of choice
# ai-cli "$PROMPT" --files "$FILES" > outputs/intake-review-$(date +%Y%m%d).md

Pattern 3: Scheduled Heartbeat

# Add to crontab
# Daily log at 6pm weekdays
0 18 * * 1-5 cd ~/openclaw-assistant && ./daily-log.sh

# Weekly summary Friday at 5pm
0 17 * * 5 cd ~/openclaw-assistant && ./weekly-summary.sh

Configuration

Environment Setup

Create a .env file for AI API configuration:

# .env
ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
OPENAI_API_KEY=${OPENAI_API_KEY}
AI_MODEL=claude-3-5-sonnet-20241022

Prompt Customization

Edit prompt files to match your workflow:

# Customize intake review categories
nano code-along/01-data-intake-review/prompts/intake-review.md

# Adjust daily log sections
nano code-along/02-operational-memory/prompts/daily-log.md

# Modify email triage buckets
nano code-along/03-offline-communications-triage/prompts/email-triage.md

Troubleshooting

Issue: AI output not matching expected format

Solution: Add explicit format instructions to prompts:

CRITICAL: Output must be valid Markdown with exactly these sections:
- Summary
- File Inventory
- Recommended Actions

Use ## for section headers. Use - for bullet lists.

Issue: Large files causing context limits

Solution: Process in batches:

# Split incoming files into chunks
for file in incoming/*.pdf; do
  # Process individually
  echo "Processing $file..."
done

Issue: Automation script not running

Solution: Check cron logs and permissions:

# View cron logs
grep CRON /var/log/syslog

# Ensure scripts are executable
chmod +x *.sh

# Test script manually
./daily-log.sh

Issue: Email .eml parsing errors

Solution: Ensure proper export format from email client. Most clients support "Save as .eml" or "Export to file" options. If parsing fails, extract plain text first:

# Extract text from .eml
grep -A 1000 "^$" email.eml | tail -n +2 > email.txt

Integration Tips

With Obsidian/Notion

# Symlink outputs to your notes folder
ln -s ~/openclaw-assistant/code-along/02-operational-memory/outputs ~/Obsidian/Daily-Logs

With Git for Versioning

# Track generated logs
cd code-along/02-operational-memory/outputs
git init
git add daily-*.md weekly-*.md
git commit -m "Daily log archive"

With Markdown Viewers

# Serve outputs as local site
cd code-along
python -m http.server 8000
# Open http://localhost:8000

Best Practices

1. Review before archiving - Always human-review AI outputs before filing
2. Consistent naming - Use ISO date formats (YYYY-MM-DD) in filenames
3. Regular cleanup - Archive old logs monthly to keep folders manageable
4. Prompt iteration - Refine prompts based on output quality over time
5. Local-first - Keep sensitive data local; only upload sanitized examples