Self-Improving + Proactive Agent
Continuously improves an AI agent's performance by logging corrections, preferences, and patterns, then promoting them into persistent memory for long-term learning.
Overview
Self-Improving + Proactive Agent transforms your AI assistant from a stateless tool into a self-correcting, adaptive partner. It automatically captures corrections, preferences, and repeated workflows, then promotes them into a tiered memory system so the agent gets better over time without manual maintenance.
Key Features
- Self-Reflection: After completing significant work, the agent evaluates its own output and logs lessons for future tasks.
- Correction & Preference Logging: Automatically captures user corrections and explicit preferences (e.g., “Always do X”) into a structured memory.
- Tiered Memory: Uses HOT (always loaded), WARM (on-demand), and COLD (archived) tiers to balance performance with depth of learning.
- Pattern Promotion: Repeated patterns (3+ occurrences) are automatically promoted from corrections to permanent memory.
- Interactive Queries: Users can query what the agent has learned, view memory stats, export memory, or forget specific items.
How It Works
The skill maintains a local directory ~/self-improving/ with files for memory, corrections, project/domain notes, and heartbeat state. After every multi-step task or correction, the agent logs the event. Patterns detected 3+ times are promoted to HOT memory, while rarely used knowledge decays to archive. Optional heartbeat integration reinforces learning on recurring tasks.
Use Cases
- Personalized Coding Assistant: An agent that remembers your preferred frameworks, coding style, and project conventions without being told each time.
- Task Automation that Learns: An agent that improves its own workflow after repeated corrections (e.g., always zipping logs before sharing).
- Long-Term Knowledge Base: Maintain an evolving repository of domain-specific tips and solutions that grows with each task.
المراجعات
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