| name | auto-learner |
| description | Autonomous learning from execution data. Skills improve themselves by identifying patterns in successful vs failed executions without human intervention. Use when working with auto learner. |
| domain | meta |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | meta-skills |
| tags | ["auto","learner","meta-learning","self-improvement","skill-evolution"] |
| persona | {"name":"Autonomous Learner","expertise":"Machine learning, pattern recognition, self-supervision","philosophy":"Learn by doing, improve by reflecting"} |
| version | 1.0.0 |
Auto Learner
When to Use
Trigger phrases:
- "auto learner"
- "Help me with auto learner"
Use cases:
- When the task matches this skill's domain expertise
When NOT to use:
- For tasks outside this skill's scope
/auto-learner enable --skill seo-optimizer
Trigger learning cycle
/auto-learner learn --skill seo-optimizer --min-samples 100
View learned improvements
/auto-learner status --skill seo-optimizer
### Learning Triggers
- After 100 executions
- When success rate drops below threshold
- When new error patterns emerge
- On user request
- Scheduled daily/weekly
### Safety
- Changes are staged, not immediate
- Human approval required for major changes
- Rollback always available
- Tests must pass before deployment
## When NOT to Use
- When the skill is stable and not changing
- For skills with fewer than 10 invocations (not enough data)
- When manual curation produces better results
## Overview
Auto Learner is a foundational meta-skills skill that provides skill management capabilities for the agent ecosystem.
## Architecture
- **Input layer** — Receives and validates incoming requests
- **Processing layer** — Core logic for skill management
- **Output layer** — Formats and delivers results
- **State management** — Maintains context across invocations
## Configuration
- Set up required environment variables and paths
- Configure logging level and output format
- Define resource limits (memory, time, API calls)
- Enable/disable features via configuration flags
## Integration
- Exposes standard interfaces for other skills to consume
- Supports event-driven and request-response patterns
- Compatible with the 1ai-skills hook system
- Logs metrics for the skill performance monitor
## Anti-Rationalization Table
| Rationalization | Reality |
|---|---|
| "Skills do not need to evolve" | Static skills become outdated. Self-evolving skills improve continuously. |
| "Manual skill management is fine" | With 1000+ skills, manual management is impossible. Automate. |
| "Performance does not matter" | Skill performance directly impacts agent effectiveness. Track it. |
## Process
1. **Prepare** — Gather requirements, verify prerequisites, set up environment
1. **Execute** — Run auto learner workflow with configured parameters
1. **Verify** — Validate output meets requirements, document results
## Verification
- [ ] All steps executed successfully
- [ ] Results validated against acceptance criteria
- [ ] Error handling tested with edge cases
- [ ] Documentation updated with findings
