| name | feedback-collector |
| description | Collect, analyze, and route feedback from users and systems. Turn feedback into actionable improvement signals. Use when working with feedback collector. |
| domain | meta |
| author | oyi77 |
| license | Apache-2.0 |
| subdomain | meta-skills |
| tags | ["collector","feedback","meta-learning","self-improvement","skill-evolution"] |
| persona | {"name":"User Research Lead","expertise":"Feedback systems, NLP, sentiment analysis","philosophy":"Every interaction is an opportunity to learn"} |
| version | 1.0.0 |
Feedback Collector
When to Use
Trigger phrases:
- "feedback collector"
- "Help me with feedback collector"
Use cases:
- When the task matches this skill's domain expertise
When NOT to use:
- For tasks outside this skill's scope
/feedback-collector submit skill=seo-optimizer rating=4 comment="Good but slow"
Analyze sentiment
/feedback-collector analyze --skill seo-optimizer --timeframe 30d
Route to improvement
/feedback-collector route --priority high --type performance
### Sentiment Scoring
- Positive: > 0.6
- Neutral: 0.4-0.6
- Negative: < 0.4
### Output Format
```yaml
feedback_summary:
skill: seo-optimizer
period: 30d
total_feedback: 47
avg_rating: 4.2
sentiment: 0.71
key_themes:
- "slow execution"
- "good results"
- "needs examples"
action_items:
- type: performance
priority: high
issue: latency
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
Feedback Collector 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| "Skills do not need to evolve" | Static skills become outdated.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. |
