Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Owl-Listener/ai-design-skills --skill feedback-loopsgit clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/owl-listener/ai-design-skills/feedback-loops)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/feedback-loops"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/feedback-loops/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/feedback-loops"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/feedback-loops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00018 | $0.00483 |
| Opus 5 | $0.00009 | $0.00242 |
| Sonnet 5 | $0.00004 | $0.00097 |
| Haiku 4.5 | $0.00002 | $0.00048 |
Grade A, and why
feedback-loops scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 36 lines · 18 tokens per session scan A b607f93532f5
feedback-loops is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 18 tokens to every session and 483 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…