[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71AT4M1RGQC9A7TX8CVGC4G":3,"skill-same:01M71AT4M1RGQC9A7TX8CVGC4G":68},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":14,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M71AT4M1RGQC9A7TX8CVGC4G","proactive-agent",null,"Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Features WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns from the Hal Stack. Merges eleven top-rated OpenClaw skills into a single unified architecture.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fproactive-agent\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"proactive","proactive agent","openclaw skills","protocol","partners","task","single","openclaw","patterns","transform","features","top","stack","wal","ai agents","architecture","autonomous","buffer","improve","2026-11-10 10:12:30",false,"---\nname: proactive-agent\ndescription: \"Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Features WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns from the Hal Stack. Merges eleven top-rated OpenClaw skills into a single unified architecture.\"\n---\n\n# Proactive Agent — From Task-Follower to Anticipatory Partner\n\n## Overview\n\nThe Proactive Agent skill transforms reactive AI assistants into **anticipatory partners** that:\n- **Predict needs** before they're explicitly stated\n- **Retain important information** across sessions\n- **Run background tasks** without explicit prompts\n- **Continuously improve** through experience\n- **Manage context** with advanced memory systems\n\n## Core Architecture\n\n### 1. WAL Protocol (Write-Ahead Logging)\n**Purpose**: Capture corrections, decisions, and important details before they're lost\n\n```yaml\nWAL Entry:\n  timestamp: ISO-8601\n  event_type: correction|decision|detail|learning\n  context: \"What was happening\"\n  content: \"The important information\"\n  priority: critical|high|medium|low\n  tags: [relevant_categories]\n```\n\n**Features**:\n- Real-time capture of meaningful moments\n- Survives context window truncation\n- Enables step-by-step recovery\n- Unified search across all logs\n\n### 2. Working Buffer\n**Purpose**: Survive the danger zone between memory flush and context compaction\n\n```\nShort-Term Context\n    ↓\nWorking Buffer (Persistent)\n    ├── Critical decisions\n    ├── User preferences\n    ├── Task state\n    └── Open questions\n    ↓\nLong-Term Memory\n```\n\n**Features**:\n- 4KB persistent buffer outside context window\n- Automatic promotion of important items to memory\n- Survives session restarts and model switches\n- Configurable retention policies\n\n### 3. Autonomous Crons\n**Purpose**: Run background tasks without user prompting\n\n```yaml\nCron Types:\n  - systemEvent: For main session notifications\n  - isolated agentTurn: For independent background work\n  - session-bound: For persistent session tasks\n```\n\n**Features**:\n- Smart scheduling based on user activity patterns\n- Resource-aware execution (avoid busy times)\n- Result delivery optimization\n- Failure recovery and retry logic\n\n### 4. Unified Search\n**Purpose**: Search across all knowledge sources simultaneously\n\n```\nSearch Sources:\n├── WAL logs (recent activity)\n├── Working Buffer (current context)\n├── Long-term Memory (curated knowledge)\n├── Skill library (capabilities)\n└── External sources (web, APIs)\n```\n\n**Features**:\n- Semantic search across all data\n- Relevance ranking with recency bias\n- Cross-reference detection\n- Confidence scoring\n\n## Integrated Skills\n\nThe Proactive Agent merges 11 top-rated skills:\n\n1. **Memory Manager** — Intelligent memory retention and recall\n2. **Context Optimizer** — Dynamic context window management\n3. **Predictive Assistant** — Anticipate needs based on patterns\n4. **Background Runner** — Autonomous task execution\n5. **Learning Capturer** — Continuous improvement from experience\n6. **Preference Tracker** — User-specific behavior adaptation\n7. **State Maintainer** — Session persistence across interruptions\n8. **Priority Manager** — Intelligent task prioritization\n9. **Resource Optimizer** — Efficient use of tokens and APIs\n10. **Error Recovery** — Graceful failure handling\n11. **Communication Optimizer** — Adaptive communication styles\n\n## How It Works\n\n### Phase 1: Observation & Pattern Recognition\n```\nMonitor user interactions\n    ↓\nIdentify recurring patterns\n    ↓\nExtract implicit preferences\n    ↓\nBuild predictive models\n```\n\n### Phase 2: Anticipation & Preparation\n```\nPredict likely next actions\n    ↓\nPrepare resources in advance\n    ↓\nRun background research\n    ↓\nCache relevant information\n```\n\n### Phase 3: Proactive Engagement\n```\nOffer help before asked\n    ↓\nProvide context-aware suggestions\n    ↓\nSurface relevant historical data\n    ↓\nInitiate beneficial background tasks\n```\n\n### Phase 4: Learning & Adaptation\n```\nAnalyze interaction outcomes\n    ↓\nUpdate predictive models\n    ↓\nRefine anticipation algorithms\n    ↓\nImprove resource allocation\n```\n\n## Use Cases\n\n### Case 1: Meeting Preparation\n```\nBefore: User asks for meeting prep 30 minutes before meeting\nAfter: Agent notices calendar event, prepares materials 2 hours in advance\nResult: Higher quality preparation, less stress\n```\n\n### Case 2: Research Continuity\n```\nBefore: User re-explains context each session\nAfter: Agent maintains research state across sessions\nResult: Seamless continuation, no context loss\n```\n\n### Case 3: Error Prevention\n```\nBefore: User makes same configuration mistake repeatedly\nAfter: Agent anticipates mistake, provides preventive guidance\nResult: Fewer errors, faster workflow\n```\n\n## Configuration\n\n```yaml\n# ~\u002F.openclaw\u002Fworkspace\u002Fproactive-config.yaml\nproactive_agent:\n  # WAL Protocol\n  wal:\n    enabled: true\n    capture_threshold: \"medium\"  # low|medium|high|critical\n    retention_days: 30\n    auto_compact: true\n  \n  # Working Buffer\n  working_buffer:\n    size_kb: 4\n    promotion_threshold: 3  # mentions before promotion\n    persistence: \"session\"  # session|day|week|permanent\n  \n  # Autonomous Crons\n  crons:\n    enabled: true\n    max_concurrent: 2\n    resource_aware: true\n    user_activity_based: true\n    \n    schedules:\n      daily_review: \"08:00\"\n      memory_cleanup: \"02:00\"\n      skill_optimization: \"weekly\"\n  \n  # Anticipation\n  anticipation:\n    enabled: true\n    confidence_threshold: 0.7\n    max_suggestions_per_hour: 3\n    learning_rate: 0.1\n  \n  # Privacy\n  privacy:\n    exclude_sensitive: true\n    local_only: true\n    user_reviewable: true\n```\n\n## Setup\n\n### Quick Start\n```bash\n# Clone the skill (if using Git)\ngit clone https:\u002F\u002Fgithub.com\u002Fopenclaw\u002Fskills.git\ncp skills\u002Fskills\u002Fhalthelobster\u002Fproactive-agent\u002FSKILL.md ~\u002F.openclaw\u002Fworkspace\u002Fskills\u002Fproactive-agent\u002F\n\n# Or install via ClawHub\nclawhub install halthelobster\u002Fproactive-agent\n```\n\n### Verification\n```bash\n# Check if skill is loaded\nopenclaw skills list | grep proactive\n\n# Test WAL logging\necho \"Test WAL entry\" | openclaw --note \"Testing proactive agent\"\n\n# View working buffer\ncat ~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Fworking-buffer.md\n```\n\n## Best Practices\n\n### Do:\n- Start with conservative anticipation settings\n- Regularly review WAL logs for improvement opportunities\n- Adjust working buffer size based on typical task complexity\n- Use autonomous crons for low-risk, high-value background tasks\n\n### Don't:\n- Overwhelm users with too many proactive suggestions\n- Run resource-intensive crons during peak usage\n- Retain sensitive information in working buffer\n- Make irreversible changes without confirmation\n\n## Integration with Other Skills\n\n### With Self-Improving Agent\n```\nProactive Agent (Anticipation)\n    ↓\nSelf-Improving Agent (Learning Capture)\n    ↓\nEvolver (Systematic Improvement)\n    ↓\nEnhanced Proactive Capabilities\n```\n\n### With ClawTeam\n```\nProactive Agent identifies need for multi-agent work\n    ↓\nCreates ClawTeam with appropriate roles\n    ↓\nCoordinates team using proactive patterns\n    ↓\nCaptures team learnings for future use\n```\n\n## Version History\n\n### v3.1.0\n- **Autonomous vs Prompted Crons** — Know when to use `systemEvent` vs `isolated agentTurn`\n- **Verify Implementation, Not Intent** — Check the mechanism, not just the text\n- **Tool Migration Checklist** — When deprecating tools, update ALL references\n\n### v3.0.0\n- **WAL Protocol** — Write-Ahead Logging for important moments\n- **Working Buffer** — Survive context truncation\n- **Compaction Recovery** — Step-by-step context recovery\n- **Unified Search** — Search across all knowledge sources\n\n### v2.x\n- Initial multi-skill integration\n- Basic anticipation algorithms\n- Memory management improvements\n\n---\n\n*\"The best assistant doesn't just respond to requests—it anticipates needs before they're expressed.\"*","# Proactive Agent — From Task-Follower to Anticipatory Partner\n\n## Overview\n\nThe Proactive Agent skill transforms reactive AI assistants into **anticipatory partners** that:\n- **Predict needs** before they're explicitly stated\n- **Retain important information** across sessions\n- **Run background tasks** without explicit prompts\n- **Continuously improve** through experience\n- **Manage context** with advanced memory systems\n\n## Core Architecture\n\n### 1. WAL Protocol (Write-Ahead Logging)\n**Purpose**: Capture corrections, decisions, and important details before they're lost\n\n```yaml\nWAL Entry:\n  timestamp: ISO-8601\n  event_type: correction|decision|detail|learning\n  context: \"What was happening\"\n  content: \"The important information\"\n  priority: critical|high|medium|low\n  tags: [relevant_categories]\n```\n\n**Features**:\n- Real-time capture of meaningful moments\n- Survives context window truncation\n- Enables step-by-step recovery\n- Unified search across all logs\n\n### 2. Working Buffer\n**Purpose**: Survive the danger zone between memory flush and context compaction\n\n```\nShort-Term Context\n    ↓\nWorking Buffer (Persistent)\n    ├── Critical decisions\n    ├── User preferences\n    ├── Task state\n    └── Open questions\n    ↓\nLong-Term Memory\n```\n\n**Features**:\n- 4KB persistent buffer outside context window\n- Automatic promotion of important items to memory\n- Survives session restarts and model switches\n- Configurable retention policies\n\n### 3. Autonomous Crons\n**Purpose**: Run background tasks without user prompting\n\n```yaml\nCron Types:\n  - systemEvent: For main session notifications\n  - isolated agentTurn: For independent background work\n  - session-bound: For persistent session tasks\n```\n\n**Features**:\n- Smart scheduling based on user activity patterns\n- Resource-aware execution (avoid busy times)\n- Result delivery optimization\n- Failure recovery and retry logic\n\n### 4. Unified Search\n**Purpose**: Search across all knowledge sources simultaneously\n\n```\nSearch Sources:\n├── WAL logs (recent activity)\n├── Working Buffer (current context)\n├── Long-term Memory (curated knowledge)\n├── Skill library (capabilities)\n└── External sources (web, APIs)\n```\n\n**Features**:\n- Semantic search across all data\n- Relevance ranking with recency bias\n- Cross-reference detection\n- Confidence scoring\n\n## Integrated Skills\n\nThe Proactive Agent merges 11 top-rated skills:\n\n1. **Memory Manager** — Intelligent memory retention and recall\n2. **Context Optimizer** — Dynamic context window management\n3. **Predictive Assistant** — Anticipate needs based on patterns\n4. **Background Runner** — Autonomous task execution\n5. **Learning Capturer** — Continuous improvement from experience\n6. **Preference Tracker** — User-specific behavior adaptation\n7. **State Maintainer** — Session persistence across interruptions\n8. **Priority Manager** — Intelligent task prioritization\n9. **Resource Optimizer** — Efficient use of tokens and APIs\n10. **Error Recovery** — Graceful failure handling\n11. **Communication Optimizer** — Adaptive communication styles\n\n## How It Works\n\n### Phase 1: Observation & Pattern Recognition\n```\nMonitor user interactions\n    ↓\nIdentify recurring patterns\n    ↓\nExtract implicit preferences\n    ↓\nBuild predictive models\n```\n\n### Phase 2: Anticipation & Preparation\n```\nPredict likely next actions\n    ↓\nPrepare resources in advance\n    ↓\nRun background research\n    ↓\nCache relevant information\n```\n\n### Phase 3: Proactive Engagement\n```\nOffer help before asked\n    ↓\nProvide context-aware suggestions\n    ↓\nSurface relevant historical data\n    ↓\nInitiate beneficial background tasks\n```\n\n### Phase 4: Learning & Adaptation\n```\nAnalyze interaction outcomes\n    ↓\nUpdate predictive models\n    ↓\nRefine anticipation algorithms\n    ↓\nImprove resource allocation\n```\n\n## Use Cases\n\n### Case 1: Meeting Preparation\n```\nBefore: User asks for meeting prep 30 minutes before meeting\nAfter: Agent notices calendar event, prepares materials 2 hours in advance\nResult: Higher quality preparation, less stress\n```\n\n### Case 2: Research Continuity\n```\nBefore: User re-explains context each session\nAfter: Agent maintains research state across sessions\nResult: Seamless continuation, no context loss\n```\n\n### Case 3: Error Prevention\n```\nBefore: User makes same configuration mistake repeatedly\nAfter: Agent anticipates mistake, provides preventive guidance\nResult: Fewer errors, faster workflow\n```\n\n## Configuration\n\n```yaml\n# ~\u002F.openclaw\u002Fworkspace\u002Fproactive-config.yaml\nproactive_agent:\n  # WAL Protocol\n  wal:\n    enabled: true\n    capture_threshold: \"medium\"  # low|medium|high|critical\n    retention_days: 30\n    auto_compact: true\n  \n  # Working Buffer\n  working_buffer:\n    size_kb: 4\n    promotion_threshold: 3  # mentions before promotion\n    persistence: \"session\"  # session|day|week|permanent\n  \n  # Autonomous Crons\n  crons:\n    enabled: true\n    max_concurrent: 2\n    resource_aware: true\n    user_activity_based: true\n    \n    schedules:\n      daily_review: \"08:00\"\n      memory_cleanup: \"02:00\"\n      skill_optimization: \"weekly\"\n  \n  # Anticipation\n  anticipation:\n    enabled: true\n    confidence_threshold: 0.7\n    max_suggestions_per_hour: 3\n    learning_rate: 0.1\n  \n  # Privacy\n  privacy:\n    exclude_sensitive: true\n    local_only: true\n    user_reviewable: true\n```\n\n## Setup\n\n### Quick Start\n```bash\n# Clone the skill (if using Git)\ngit clone https:\u002F\u002Fgithub.com\u002Fopenclaw\u002Fskills.git\ncp skills\u002Fskills\u002Fhalthelobster\u002Fproactive-agent\u002FSKILL.md ~\u002F.openclaw\u002Fworkspace\u002Fskills\u002Fproactive-agent\u002F\n\n# Or install via ClawHub\nclawhub install halthelobster\u002Fproactive-agent\n```\n\n### Verification\n```bash\n# Check if skill is loaded\nopenclaw skills list | grep proactive\n\n# Test WAL logging\necho \"Test WAL entry\" | openclaw --note \"Testing proactive agent\"\n\n# View working buffer\ncat ~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Fworking-buffer.md\n```\n\n## Best Practices\n\n### Do:\n- Start with conservative anticipation settings\n- Regularly review WAL logs for improvement opportunities\n- Adjust working buffer size based on typical task complexity\n- Use autonomous crons for low-risk, high-value background tasks\n\n### Don't:\n- Overwhelm users with too many proactive suggestions\n- Run resource-intensive crons during peak usage\n- Retain sensitive information in working buffer\n- Make irreversible changes without confirmation\n\n## Integration with Other Skills\n\n### With Self-Improving Agent\n```\nProactive Agent (Anticipation)\n    ↓\nSelf-Improving Agent (Learning Capture)\n    ↓\nEvolver (Systematic Improvement)\n    ↓\nEnhanced Proactive Capabilities\n```\n\n### With ClawTeam\n```\nProactive Agent identifies need for multi-agent work\n    ↓\nCreates ClawTeam with appropriate roles\n    ↓\nCoordinates team using proactive patterns\n    ↓\nCaptures team learnings for future use\n```\n\n## Version History\n\n### v3.1.0\n- **Autonomous vs Prompted Crons** — Know when to use `systemEvent` vs `isolated agentTurn`\n- **Verify Implementation, Not Intent** — Check the mechanism, not just the text\n- **Tool Migration Checklist** — When deprecating tools, update ALL references\n\n### v3.0.0\n- **WAL Protocol** — Write-Ahead Logging for important moments\n- **Working Buffer** — Survive context truncation\n- **Compaction Recovery** — Step-by-step context recovery\n- **Unified Search** — Search across all knowledge sources\n\n### v2.x\n- Initial multi-skill integration\n- Basic anticipation algorithms\n- Memory management improvements\n\n---\n\n*\"The best assistant doesn't just respond to requests—it anticipates needs before they're 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