fangkelvin

Proactive Agent Skill

AI 智能体v1.0.0
作者:fangkelvin更新于 2026-05-18proactive-agent-skill@clawhub_fangkelvin/proactive-agent-skill
任务自动化工作流编排

据 AlphaMoat 统计,截至 2026-10,Proactive Agent Skill 累计下载 42,663 次、获 15 个星标,按累计下载量在本次参与排名的 10,914 个 AI 技能中排名第 606 位,在「AI 智能体」分类(1,632 个)中排名第 111 位。

列表默认按累计下载量排序,也可按星标排序;详情页排名按累计下载量计算。

累计下载
4.3万
星标
15
全站下载量排名
#606
全站前 6% · 「AI 智能体」前 7%
平台累计下载星标评论
ClawHub ↗2.0万11-
SkillHub ↗4.3万15—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

包含工作缓冲、自主定时任务与心跳间隔配置,目标是让 agent 主动发现问题并承接后续动作,而不是被动等指令。适合想引入记忆持久化或构建自改进 agent 的场景。

资料发布于 2026-10-27

可以用来做什么

  • 给 agent 制定一套主动巡检与跟进规则。
  • 配置心跳间隔,让 agent 定期自检待办。

什么时候用

  • agent 过于被动、需要预判需求时。
  • 要引入持久记忆与自改进机制时。

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

帮我把这个 agent 调得更主动一些。
设计一套心跳机制,让 agent 定期检查待办。

月度数据趋势

累计下载4.3万
1.6万3.0万4.5万2026-05 · 累计下载 1.8万2026-07 · 累计下载 1.9万2026-08 · 累计下载 1.9万2026-09 · 累计下载 2.2万2026-10 · 累计下载 4.3万26-0526-0926-10
星标15
1314152026-05 · 星标 132026-07 · 星标 152026-08 · 星标 152026-09 · 星标 152026-10 · 星标 1526-0526-0926-10

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份累计下载环比星标
2026-104.3万—15
2026-092.2万—15
2026-081.9万+1.6%15
2026-071.9万—15
2026-051.8万—13

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。

5.0 KB · ClawHub v1.0.0
---
name: proactive-agent
description: "Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Includes WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns."
homepage: https://lobehub.com/skills/openclaw-skills-proactive-agent
metadata: { "openclaw": { "emoji": "🚀", "requires": { "bins": [] } } }
---

# Proactive Agent Skill

Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve.
展开全部(共 206 行)

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

#### 2. Autonomous Crons
```bash
# 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
```json
{
  "proactive": {
    "heartbeatInterval": 1800,
    "autonomousCrons": {
      "daily": ["08:00", "20:00"],
      "weekly": ["Monday 09:00"]
    },
    "memory": {
      "compactionThreshold": 1000,
      "retentionDays": 30
    }
  }
}
```

## Usage Examples

### 1. Implementing WAL Protocol
```markdown
# 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
```bash
# 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
```bash
# 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.

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fangkelvin 的其他技能

数据月份:2026-10 · 下载/星标数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。