feedback-loops
Framework for capturing and processing user feedback to iteratively improve AI performance.
Install
mkdir -p .claude/skills/feedback-loops && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/15980" && unzip -o skill.zip -d .claude/skills/feedback-loops && rm skill.zipInstalls to .claude/skills/feedback-loops
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
User correction, thumbs up/down, inline editing, and reinforcement signals.Key capabilities
- →Capture explicit feedback like thumbs up/down
- →Capture implicit feedback such as regeneration or editing
- →Capture corrective feedback where users provide the right answer
- →Capture preference feedback by choosing between alternatives
- →Design for correction through inline editing and partial acceptance
- →Implement ambient feedback collection
How it works
This skill outlines how to design and implement feedback loops to capture explicit, implicit, and corrective signals from users, ensuring the AI learns and improves over time.
Inputs & outputs
When to use feedback-loops
- →Capture user correction
- →Implement feedback loop
- →Analyze system performance
- →Process reinforcement signals
About this skill
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
When not to use it
- →When feedback is not intended to improve the AI over time
- →When interruptive feedback mechanisms are preferred
- →When feedback is not expected to change something in the AI's behavior
Limitations
- →Designing for correction is the hardest to implement
- →Feedback timing matters and can be too early or too late
- →Interruptive feedback breaks user flow
How it compares
This skill focuses on designing for various types of feedback, especially corrective and ambient signals, to enable continuous AI improvement, which differs from simple rating systems.
Compared to similar skills
feedback-loops side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| feedback-loops (this skill) | 0 | 3mo | No flags | Intermediate |
| data-engineering | 13 | 7mo | Review | Advanced |
| crawl4ai | 21 | 8mo | Review | Intermediate |
| data-cleaning-pipeline | 13 | 5mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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