A self-evolution engine for AI agents that implements a Sense-Learn-Act cycle to continuously optimize task performance through feedback and pattern recognition.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install workbuddy-smart-learning
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install workbuddy-smart-learning using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is WorkBuddy Smart Learning System?
The WorkBuddy Smart Learning System is a sophisticated self-evolution framework designed to transform how AI agents interact with their environment. By utilizing a continuous SEA (Sense-Learn-Act) loop, this system enables your assistant to gather data from every interaction and evolve its internal logic. It represents a significant advancement for developers using Openclaw Skills who require an agent that doesn't just execute tasks but learns from successes and failures alike.
The system integrates six core modules that handle everything from explicit user feedback to implicit signal tracking and high-value pattern recognition. By distilling raw execution data into reusable templates and avoidance rules, the WorkBuddy Smart Learning System ensures that your AI environment becomes more personalized, efficient, and reliable over time.
WorkBuddy Smart Learning System Use Cases
- Automating the creation of reusable project templates based on successful high-frequency coding patterns.
- Reducing task failure rates by identifying and codifying avoid_*.yaml rules from negative feedback.
- Optimizing agent response times by monitoring tool usage statistics and task complexity distributions.
- Proactively suggesting workflow improvements during new sessions when high-value patterns are detected.
How WorkBuddy Smart Learning System Works
- Data Collection: The system captures explicit feedback (ratings/tags) and implicit signals like tool success rates and task cancellation frequencies.
- Task Profiling: Every execution is traced to analyze complexity and tool performance, creating a detailed profile of the agent's work.
- Pattern Recognition: A scoring algorithm identifies high-value workflows based on frequency, recency, and stability.
- Knowledge Distillation: Using adaptive thresholds, the system distills positive outcomes into templates and negative outcomes into pitfall avoidance rules.
- Real-Time Execution: When a new request arrives, the system uses Jaccard similarity to match the request against distilled templates for optimized execution.
WorkBuddy Smart Learning System Setup
Navigate to the skill directory and use the CLI to manage the learning cycle:
cd .workbuddy/skills/workbuddy-smart-learning
# Run a full learning cycle and generate a comprehensive report
python learn.py full
# Match a user request against learned templates
python learn.py match "your_keyword"
WorkBuddy Smart Learning System Data Schema & Taxonomy
The skill organizes its memory within the .workbuddy/memory/ directory using a structured JSON and YAML hierarchy:
| Directory | Data Type | Purpose |
|---|---|---|
/feedback/ |
JSON | Stores explicit user ratings and improvement notes |
/signals/ |
JSON | Tracks 5 types of implicit behavioral metrics |
/task_profiles/ |
JSON | Contains execution traces and tool performance logs |
/patterns/ |
JSON | Stores identified high-value workflows and threshold states |
/templates/ |
YAML | Holds distilled templates and avoidance (pitfall) rules |
WorkBuddy Smart Learning System Advanced Features
- Adaptive Threshold Engine: Automatically raises or lowers distillation sensitivity based on the accuracy of past alerts.
- Triple-Tier Alerting: Categorizes discoveries into URGENT (immediate action), TREND (weekly focus), and STABLE (long-term template candidates).
- Implicit Signal Scoring: Calculates a 'Health Score' (0-100) based on task duration, tool success, and repetition rates.
- Zero-Friction Learning: Background collection of behavioral signals ensures the system evolves without interrupting the developer's workflow in Openclaw Skills.
SKILL.md
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