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 <!DOCTYPE html> <html> <head> <title>Executive Assistant Dashboard</title> </head> <body> <h1>Mission Control</h1> <section> <h2>Latest Reports</h2> <ul> <li><a href="../01-data-intake-review/outputs/intake-review.md">Latest Intake Review</a></li> <li><a href="../02-operational-memory/outputs/daily-log.md">Today's Log</a></li> <li><a href="../02-operational-memory/outputs/weekly-hype.md">This Week's Hype</a></li> <li><a href="../03-offline-communications-triage/outputs/email-triage.md">Email Triage</a></li> </ul> </section> </body> </html> ``` ## 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.