openclaw-executive-assistant-workflows

# OpenClaw Executive Assistant Workflows > Skill by [ara.so](https://ara.so) — Hermes Skills collection. This skill covers building local-first executive assistant workflows using OpenClaw patterns. The project demonstrates three core workflows: data intake review, operational memory tracking, and offline communications triage. All operations use local files and produce reviewable markdown artifacts. ## What This Project Does OpenClaw executive assistant workflows help you: - **Data Intake Review**: Transform unknown files into trustworthy intake reports - **Operational Memory**: Convert work residue into daily logs and weekly momentum documents - **Offline Communications Triage**: Turn exported emails into prioritized action lists All workflows are local-only with no live integrations, using copy/paste prompts and markdown outputs. ## Project Structure ``` code-along/ ├── INDEX.md ├── 01-data-intake-review/ │ ├── incoming/ # Files to inspect │ ├── prompts/ │ │ └── intake-review.md │ ├── outputs/ │ │ └── intake-review.md │ └── expected/ │ └── report-outline.md ├── 02-operational-memory/ │ ├── inbox/ # Notes and work residue │ ├── prompts/ │ │ ├── daily-log.md │ │ └── weekly-hype.md │ ├── outputs/ │ │ ├── daily-log.md │ │ └── weekly-hype.md │ └── schedule/ │ ├── cron-examples.md │ └── heartbeat-note.md ├── 03-offline-communications-triage/ │ ├── eml/ # Exported email files │ ├── prompts/ │ │ └── email-triage.md │ ├── outputs/ │ │ └── email-triage.md │ └── expected/ │ └── report-outline.md └── mission-control/ # Optional dashboard ``` ## Workflow 1: Data Intake Review Transform incoming files into structured reports. ### Setup ```bash cd code-along/01-data-intake-review ``` ### Usage Pattern 1. Place unknown files in `incoming/` 2. Read the prompt template in `prompts/intake-review.md` 3. Run the prompt against the incoming files 4. Generate `outputs/intake-review.md` ### Example Prompt Structure ```markdown # Data Intake Review Prompt Review all files in the incoming/ directory and create a structured report: ## Summary - Total files reviewed - File types present - Overall assessment ## File-by-File Analysis For each file: - Filename - Type - Size - Key contents - Recommendation (keep/archive/delete/action) ## Action Items List any files requiring immediate attention ## Archive Candidates List files safe to archive ``` ### Expected Output Format ```markdown # Intake Review - [DATE] ## Summary 3 files reviewed - 1 PDF document - 1 CSV dataset - 1 text note ## File Analysis ### document.pdf - **Type**: PDF proposal - **Size**: 2.4 MB - **Contents**: Q2 budget proposal draft - **Recommendation**: ACTION - Review by EOD Friday ### data.csv - **Type**: CSV dataset - **Size**: 145 KB - **Contents**: Customer feedback responses - **Recommendation**: KEEP - Reference for analysis ## Action Items 1. Review document.pdf budget proposal 2. Schedule feedback analysis meeting ``` ## Workflow 2: Operational Memory Convert daily work residue into structured logs and weekly summaries. ### Setup ```bash cd code-along/02-operational-memory ``` ### Daily Log Generation ```markdown # Daily Log Prompt Review all notes and work artifacts in inbox/ from today. Generate a daily log with: ## Work Completed - Tasks finished - Decisions made - Problems solved ## In Progress - Ongoing work - Blockers encountered ## Notes for Tomorrow - Priorities - Follow-ups needed ## Quick Wins - Small achievements worth noting ``` ### Weekly Summary Generation ```markdown # Weekly Hype Prompt Review the past week's daily logs. Create a weekly summary: ## 🎯 Goals Achieved What got done this week ## 📈 Progress Made Movement on larger initiatives ## 🔥 Highlights Wins worth celebrating ## 🚀 Next Week Focus Top 3 priorities ## 💡 Lessons Learned Key insights from the week ``` ### Automation with Cron Example cron configuration for daily logs: ```bash # Daily log at 5 PM 0 17 * * * cd ~/code-along/02-operational-memory && ./generate-daily-log.sh # Weekly summary on Friday at 4 PM 0 16 * * 5 cd ~/code-along/02-operational-memory && ./generate-weekly-hype.sh ``` ### Example Script ```bash #!/bin/bash # generate-daily-log.sh DATE=$(date +%Y-%m-%d) PROMPT_FILE="prompts/daily-log.md" OUTPUT_FILE="outputs/daily-log-${DATE}.md" # Read prompt and process inbox echo "Generating daily log for ${DATE}..." # Copy prompt template cp "$PROMPT_FILE" "$OUTPUT_FILE" # Add date header echo "# Daily Log - ${DATE}" >> "$OUTPUT_FILE" # Process would happen here via AI agent # This is a manual copy/paste workflow in the workshop ``` ## Workflow 3: Offline Communications Triage Transform exported emails into actionable reports. ### Setup ```bash cd code-along/03-offline-communications-triage ``` ### Email Export Process Export emails to `.eml` format and place in the `eml/` directory. ### Triage Prompt Structure ```markdown # Email Triage Prompt Review all .eml files in the eml/ directory. Create a triage report: ## 🚨 Urgent - Respond Today Emails requiring immediate attention ## 📋 Action Required - This Week Emails needing response within 3-5 days ## 📖 For Review Informational emails worth reading ## 🗑️ Low Priority Can be archived or handled later For each email include: - From - Subject - Date - Brief summary - Recommended action - Estimated time required ``` ### Expected Output ```markdown # Email Triage Report - 2026-04-25 ## 🚨 Urgent - Respond Today ### Email 1 - **From**: sarah@company.com - **Subject**: Q2 Budget Approval Needed - **Date**: 2026-04-25 09:15 AM - **Summary**: Budget requires sign-off before Monday meeting - **Action**: Review and approve budget spreadsheet - **Time**: 30 minutes ## 📋 Action Required - This Week ### Email 2 - **From**: team@project.com - **Subject**: Design Review Feedback - **Date**: 2026-04-24 02:30 PM - **Summary**: Design mockups ready for review - **Action**: Provide feedback on 3 design options - **Time**: 45 minutes ## 📖 For Review ### Email 3 - **From**: newsletter@industry.com - **Subject**: Weekly Industry Update - **Date**: 2026-04-24 08:00 AM - **Summary**: Market trends and competitor news - **Action**: Read when available - **Time**: 15 minutes ``` ## Mission Control Dashboard Optional central hub for linking all artifacts. ```markdown # Mission Control Last updated: 2026-04-25 17:00 ## 📥 Latest Intake Review [View Report](../01-data-intake-review/outputs/intake-review.md) - 3 files processed - 1 action item pending ## 📝 Today's Log [View Log](../02-operational-memory/outputs/daily-log-2026-04-25.md) - 5 tasks completed - 2 in progress ## 📧 Email Triage [View Report](../03-offline-communications-triage/outputs/email-triage.md) - 1 urgent response needed - 3 actions this week ``` ## Best Practices ### Prompt Design - Keep prompts clear and structured - Define expected output format explicitly - Include examples in prompt templates - Use consistent markdown formatting ### File Organization ``` workflow-name/ ├── incoming/ # Raw inputs ├── prompts/ # Reusable prompt templates ├── outputs/ # Generated artifacts └── expected/ # Reference outputs ``` ### Artifact Naming Use consistent naming conventions: ``` daily-log-YYYY-MM-DD.md weekly-hype-YYYY-WW.md intake-review-YYYY-MM-DD.md email-triage-YYYY-MM-DD.md ``` ### Workflow Execution 1. Read the prompt template 2. Customize for current context 3. Process input files 4. Generate markdown artifact 5. Review and refine output 6. Archive or act on results ## Troubleshooting ### Prompt Not Producing Expected Output - Check that input files are in correct location - Verify prompt includes output format specification - Add explicit examples to prompt - Break complex prompts into smaller steps ### Missing Context in Reports - Include date/time stamps in prompts - Reference previous artifacts when needed - Add context sections to prompt templates ### Automation Issues - Verify file paths in cron scripts - Check script permissions: `chmod +x script.sh` - Test scripts manually before scheduling - Log script output for debugging ### File Format Problems - Ensure `.eml` files are properly exported - Check markdown syntax in outputs - Validate file encoding (UTF-8) ## Integration with AI Coding Agents This workflow integrates with AI agents by: 1. **Structured Prompts**: Templates that agents can execute 2. **File-Based I/O**: Clear input/output locations 3. **Markdown Artifacts**: Reviewable, versionable outputs 4. **Local-First**: No API dependencies Agents can automate: - Reading prompt templates - Processing input directories - Generating formatted outputs - Updating mission control dashboard