Guides designing AI feedback loops: user corrections, thumbs up/down, inline editing, reinforcement signals. Improves AI adaptation via explicit/implicit feedback.
npx claudepluginhub owl-listener/ai-design-skills --plugin model-interaction-designThis skill uses the workspace's default tool permissions.
Feedback loops are how users tell the AI what's working and what isn't. Designing these loops well is the difference between an AI that improves over time and one that repeats the same mistakes.
Audits and redesigns AI-generated feedback for pedagogical quality, timing, and learning impact. Use when building or reviewing automated feedback in digital learning tools.
Detects corrections and learnings from user messages. Embedded in ask-question skill—do not invoke feedback-capture separately.
Learns user preferences from corrections (3+), steering patterns, periodic checkpoints, and explicit triggers to adapt Claude's behavior across sessions.
Share bugs, ideas, or general feedback.
Feedback loops are how users tell the AI what's working and what isn't. Designing these loops well is the difference between an AI that improves over time and one that repeats the same mistakes.
The most valuable feedback is correction — but it's also the hardest to design for:
When to ask for feedback matters:
Feedback is only valuable if it changes something. The user needs to see that their feedback matters: