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Guides designing AI feedback loops: user corrections, thumbs up/down, inline editing, reinforcement signals. Improves AI adaptation via explicit/implicit feedback.
How this skill is triggered — by the user, by Claude, or both
Slash command
/model-interaction-design:feedback-loopsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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:
npx claudepluginhub owl-listener/ai-design-skills --plugin model-interaction-designAudits 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.