| name | pattern-recognition |
| description | Identify patterns in skill execution, errors, and successes. Recognize when situations match previous patterns and apply learned solutions. Use when working with pattern recognition. |
| domain | meta |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | meta-skills |
| tags | ["meta-learning","pattern","recognition","self-improvement","skill-evolution"] |
| persona | {"name":"Pattern Recognition Expert","expertise":"Pattern matching, anomaly detection, similarity analysis","philosophy":"History repeats itself"} |
| version | 1.0.0 |
Pattern Recognition
When to Use
Trigger phrases:
- "pattern recognition"
- "Help me with pattern recognition"
Use cases:
- When the task matches this skill's domain expertise
When NOT to use:
- For tasks outside this skill's scope
/pattern-recognition analyze --skill seo-optimizer --lookback 30d
Match current situation
/pattern-recognition match "current query" --skill seo-optimizer
Suggest based on patterns
/pattern-recognition suggest --for "error message"
### Applications
- Predict likely failures before they happen
- Suggest optimizations based on past successes
- Group similar tasks for batch processing
- Identify outliers requiring special handling
## 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
Pattern Recognition 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| "Skills do not need to evolve" | Static skills become outdated.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. **Design** — Define interface, identify patterns, plan implementation
1. **Implement** — Write code following existing conventions, add tests
1. **Verify** — Run tests, check integration, validate behavior
## Verification
- [ ] All steps executed successfully
- [ ] Results validated against acceptance criteria
- [ ] Error handling tested with edge cases
- [ ] Documentation updated with findings
