Skill: socneo/socneo-autonomous-agent (version 1.0.0)

--- name: autonomous-agent description: AI Autonomous Agent Framework with self-driven capabilities. Implements perception, judgment, execution, and reflection layers for intelligent autonomous operation. Use when building self-aware, adaptive AI systems that can operate independently while maintaining safety and user control. author: Socneo version: 1.0.0 license: MIT tags: - ai-agent - autonomous - framework - cognitive-architecture - self-learning repository: https://github.com/Socneo/autonomous-agent --- # Autonomous Agent - AI Self-Driven Framework ## Overview The Autonomous Agent framework implements a complete self-driven AI system based on the four-layer architecture: Perception, Judgment, Execution, and Reflection. This framework enables AI systems to operate autonomously while maintaining safety, user control, and continuous learning capabilities. ## Core Architecture ### Layer 1: Perception Layer Monitors system state, user activity, and environmental changes to identify actionable signals. ### Layer 2: Judgment Layer Evaluates tasks based on priority, risk, and confidence to make intelligent decisions. ### Layer 3: Execution Layer Performs tasks with resilience, error recovery, and progress tracking. ### Layer 4: Reflection Layer Learns from experiences, identifies patterns, and continuously improves performance. ## Key Features ### Intelligent Perception - **Adaptive Heartbeat**: Dynamically adjusts monitoring frequency based on activity levels - **Multi-Source Events**: Monitors file system, skill usage, user activity, and system metrics - **State Awareness**: Maintains comprehensive understanding of current system state ### Smart Judgment - **Priority Evaluation**: Calculates task priority using urgency, importance, and relevance - **Risk Assessment**: Uses decision matrix for safe autonomous operation - **Uncertainty Handling**: Explicitly manages low-confidence situations ### Resilient Execution - **Task Decomposition**: Breaks complex tasks into verifiable subtasks - **Error Recovery**: Implements retry strategies and fallback mechanisms - **Progress Tracking**: Provides real-time status updates and completion metrics ### Continuous Learning - **Auto Reflection**: Automatically analyzes task outcomes and performance - **Pattern Recognition**: Identifies optimization opportunities and best practices - **Memory System**: Four-layer memory architecture for persistent learning ## Usage Scenarios ### System Monitoring - Monitor skill performance and health - Detect anomalies and potential issues - Proactively suggest optimizations ### Task Automation - Automatically handle routine maintenance - Execute approved workflows independently - Coordinate multiple skills for complex tasks ### Adaptive Learning - Learn from user preferences and patterns - Improve decision-making over time - Develop personalized automation strategies ## Quick Start ### Basic Configuration ```bash # Initialize autonomous agent agent init --config autonomous_config.yaml # Start monitoring agent start --mode adaptive # Check status agent status ``` ### Advanced Usage ```bash # Configure perception layer agent config perception --heartbeat-interval 300 --event-sources all # Set judgment parameters agent config judgment --risk-threshold medium --confidence-threshold 0.7 # Enable reflection agent config reflection --auto-learn enabled --memory-retention 30d ``` ## Safety and Control ### Permission Management - **Tiered Permissions**: Read-only, Safe-write, Advanced, Admin levels - **Risk Isolation**: High-risk operations in sandboxed environments - **User Override**: Users can disable autonomous features at any time ### Audit and Transparency - **Operation Logging**: Complete audit trail of autonomous actions - **Decision Explainability**: Clear reasoning for autonomous decisions - **Rollback Capability**: Ability to undo autonomous changes ## Configuration ### Perception Settings - Heartbeat intervals and adaptive thresholds - Event source configurations - State capture parameters ### Judgment Parameters - Priority calculation weights - Risk assessment thresholds - Confidence level requirements ### Execution Controls - Task decomposition rules - Retry strategies and limits - Progress reporting intervals ### Memory Configuration - Memory layer retention periods - Storage backend selection - Search and retrieval settings ## Advanced Features ### Cognitive Architecture - **Reasoning Engine**: Advanced decision-making capabilities - **Goal Management**: Multi-objective optimization - **Resource Planning**: Intelligent resource allocation ### Integration Capabilities - **Skill Coordination**: Orchestrate multiple skills - **External APIs**: Connect to external services - **Data Sources**: Monitor diverse data streams ### Learning Systems - **Reinforcement Learning**: Optimize actions based on rewards - **Transfer Learning**: Apply knowledge across domains - **Meta Learning**: Learn how to learn more effectively ## Best Practices ### Safety First 1. **Start Conservative**: Begin with low autonomy levels 2. **Gradual Enablement**: Increase autonomy as trust builds 3. **Human Oversight**: Maintain human-in-the-loop for critical decisions 4. **Regular Audits**: Review autonomous actions and outcomes ### Performance Optimization 1. **Monitor Metrics**: Track autonomy effectiveness 2. **Tune Parameters**: Adjust thresholds based on performance 3. **Update Models**: Regularly update learning models 4. **Scale Gradually**: Expand capabilities incrementally ### User Experience 1. **Transparent Communication**: Clearly explain autonomous actions 2. **User Control**: Provide easy override mechanisms 3. **Feedback Loops**: Incorporate user feedback into learning 4. **Progressive Disclosure**: Reveal complexity as needed ## Troubleshooting ### Common Issues 1. **Over-Activity**: Agent performing too many autonomous actions 2. **Under-Activity**: Agent not taking enough initiative 3. **Incorrect Decisions**: Agent making poor autonomous choices 4. **Memory Issues**: Problems with learning and retention ### Diagnostic Tools - **Agent Logs**: Detailed logs of autonomous operations - **Performance Metrics**: Quantitative measures of autonomy effectiveness - **Decision Traces**: Step-by-step reasoning for autonomous decisions - **Memory Analysis**: Inspection of learned patterns and experiences ## License MIT License - See LICENSE file for details. ## Author **Socneo** - [GitHub](https://github.com/Socneo/autonomous-agent) Created with Claude Code.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)