Feedback Loops
User correction, thumbs up/down, inline editing, and reinforcement signals.
Install
$ agentstack add skill-owl-listener-ai-design-skills-feedback-loops ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-owl-listener-ai-design-skills-feedback-loops) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Feedback Loops
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.
Types of Feedback
- Explicit feedback: Thumbs up/down, star ratings, "this was helpful/not helpful" buttons
- Implicit feedback: Regeneration (user asks again), editing (user modifies the output), abandonment (user leaves)
- Corrective feedback: User provides the right answer ("No, I meant X not Y")
- Preference feedback: User chooses between alternatives ("I prefer option B")
- Contextual feedback: Feedback tied to a specific part of the output, not the whole response
Designing for Correction
The most valuable feedback is correction — but it's also the hardest to design for:
- Inline editing: Let users edit AI output directly. Track what they change.
- Partial acceptance: Let users keep some parts and reject others.
- Explanation requests: "Why did you do it this way?" — the user's question reveals what went wrong.
- Redo with guidance: "Try again but make it more formal" — correction through re-prompting.
Feedback Timing
When to ask for feedback matters:
- Too early: User hasn't evaluated the output yet. Feedback is premature.
- Too late: User has moved on. The moment for feedback has passed.
- Interruptive: Modal dialogs or required ratings break flow.
- Ambient: Passive signals (edits, regeneration) collected without asking.
Design for ambient feedback first. Add explicit feedback sparingly.
Closing the Loop
Feedback is only valuable if it changes something. The user needs to see that their feedback matters:
- Immediate adaptation: The AI adjusts in the current conversation
- Persistent learning: The AI remembers preferences across sessions
- Acknowledgment: "I'll keep that in mind" — even if adaptation is delayed
Design Artefacts
- Feedback mechanism inventory per feature
- Implicit signal definitions (what counts as positive/negative)
- Feedback-to-adaptation mapping (what changes based on what feedback)
- Correction flow specifications
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Owl-Listener
- Source: Owl-Listener/ai-design-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.