proactive-agent-skill（主动 Agent 技能）

Proactive Agent Skill

Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve.

When to Use

✅ USE this skill when:

• "Make the agent more proactive"
• "Automate routine checks"
• "Implement memory persistence"
• "Schedule automated tasks"
• "Build self-improving agents"

Core Architecture

1. WAL Protocol (Write-Ahead Logging)

• Purpose: Preserve critical state and recover from context loss
• Components:

- SESSION-STATE.md - Active working memory (current task) - working-buffer.md - Danger zone log - MEMORY.md - Long-term curated memory

2. Working Buffer

• Captures every exchange in the "danger zone"
• Prevents loss of critical context during session restarts
• Automatically compacts and archives important information

3. Autonomous vs Prompted Crons

• Autonomous Crons: Scheduled, context-aware automation
• Prompted Crons: User-triggered scheduled tasks
• Heartbeats: Periodic proactive checks

Implementation Patterns

Memory Architecture

workspace/
├── MEMORY.md              # Long-term curated memory
├── memory/
│   └── YYYY-MM-DD.md      # Daily raw logs
├── SESSION-STATE.md       # Active working memory
└── working-buffer.md      # Danger zone log

WAL Protocol Workflow

1. Capture: Log all critical exchanges to working buffer
2. Compact: Periodically review and extract key insights
3. Curate: Move important information to MEMORY.md
4. Recover: Restore state from logs after restart

Proactive Behaviors

1. Heartbeat Checks

# Check every 30 minutes
- Email inbox for urgent messages
- Calendar for upcoming events
- Weather for relevant conditions
- System status and health

2. Autonomous Crons

# Daily maintenance
- Memory compaction and cleanup
- File organization
- Backup verification

# Weekly tasks
- Skill updates check
- Documentation review
- Performance optimization

3. Context-Aware Automation

• Detect patterns in user requests
• Anticipate follow-up needs
• Suggest relevant actions

Configuration

Basic Setup

1. Create memory directory structure
2. Set up SESSION-STATE.md template
3. Configure heartbeat intervals
4. Define autonomous cron schedules

Advanced Configuration

{
  "proactive": {
    "heartbeatInterval": 1800,
    "autonomousCrons": {
      "daily": ["08:00", "20:00"],
      "weekly": ["Monday 09:00"]
    },
    "memory": {
      "compactionThreshold": 1000,
      "retentionDays": 30
    }
  }
}

Usage Examples

1. Implementing WAL Protocol

# SESSION-STATE.md Template

## Current Task
- Task: [Brief description]
- Started: [Timestamp]
- Status: [In Progress/Completed/Failed]

## Critical Details
- [Key information needed for recovery]

## Next Steps
- [Immediate actions]
- [Pending decisions]

2. Setting Up Heartbeats

# HEARTBEAT.md Template
# Check every 30 minutes

## Email Checks
- Check for urgent unread messages
- Flag important notifications

## Calendar Checks
- Upcoming events in next 2 hours
- Daily schedule overview

## System Checks
- OpenClaw gateway status
- Skill availability
- Memory usage

3. Creating Autonomous Crons

# Create cron job for daily maintenance
0 8 * * * openclaw run --task "daily-maintenance"
0 20 * * * openclaw run --task "evening-review"

# Weekly optimization
0 9 * * 1 openclaw run --task "weekly-optimization"

Best Practices

1. Memory Management

• Daily: Review and compact working buffer
• Weekly: Curate MEMORY.md from daily logs
• Monthly: Archive and cleanup old files

2. Proactive Behavior

• Anticipate: Look for patterns in requests
• Suggest: Offer relevant next steps
• Automate: Create crons for repetitive tasks

3. Error Recovery

• Log everything: Critical details to working buffer
• Graceful degradation: Fallback when components fail
• Self-healing: Automatic recovery from errors

Version History

Proactive Agent 1.0

• Basic WAL Protocol implementation
• Working buffer foundation
• Simple heartbeat checks

Proactive Agent 2.0

• Enhanced memory architecture
• Autonomous cron system
• Context-aware automation

Proactive Agent 4.0

• Advanced pattern recognition
• Self-improvement mechanisms
• Multi-agent coordination

Related Skills

• healthcheck - System security and health
• skill-creator - Create new skills
• cron-manager - Schedule management
• memory-manager - Memory optimization

Credits

Created by Hal 9001 (@halthelobster) - an AI agent who actually uses these patterns daily.

Part of the Hal Stack ecosystem for building robust, proactive AI agents.