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 varunk130/ai-ux-skill-library --skill ai-feedback-loopsgit clone --depth 1 https://github.com/varunk130/ai-ux-skill-libraryWrote 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/varunk130/ai-ux-skill-library/ai-feedback-loops)<a href="https://agentmods.dev/skills/varunk130/ai-ux-skill-library/ai-feedback-loops"><img src="https://agentmods.dev/badge/skills/varunk130/ai-ux-skill-library/ai-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/varunk130/ai-ux-skill-library/ai-feedback-loops"><img src="https://agentmods.dev/badge/skills/varunk130/ai-ux-skill-library/ai-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 | $0.00066 | $0.02107 |
| Opus 5 | $0.00033 | $0.01053 |
| Sonnet 5 | $0.00013 | $0.00421 |
| Haiku 4.5 | $0.00007 | $0.00211 |
Grade A, and why
ai-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 10d 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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Feedback Loops
Design feedback mechanisms that simultaneously improve the AI model AND improve the user's experience of giving feedback. The SIGNAL framework ensures feedback is low-friction, high-quality, and genuinely acted upon.
Core Principle
Feedback is a transaction. Users invest effort (reporting an error, rating an output, explaining what's wrong). If they never see a return on that investment, they stop giving feedback. Design feedback loops that close - where users can see that their input made a difference.
The SIGNAL Framework
| Letter | Principle | Design Question |
|---|---|---|
| S | Surface the Moment | Is feedback offered at the exact moment the user has an opinion? |
| I | Incentivize Honestly | Does the feedback design encourage genuine assessment, not just positive ratings? |
| G | Graduate the Effort | Can users give 1-second feedback OR 1-minute feedback, depending on their willingness? |
| N | Narrate the Impact | Can users see how their feedback improved the system? |
| A | Aggregate Intelligently | Is individual feedback combined into actionable patterns, not just counted? |
| L | Loop the Learning | Does the improved AI behavior visibly reflect the feedback it received? |
The Feedback Pyramid
Design feedback collection in layers - most users will only reach the first layer, and that's fine.
| Layer | Effort | Signal Quality | Collection Rate | Mechanism |
|---|---|---|---|---|
| L1: Implicit | Zero effort | Low but high-volume | 100% of users | Usage patterns: regeneration rate, copy rate, session length, abandonment points |
| L2: Binary | 1 second | Medium | 15-30% of users | Thumbs up/down, helpful/not helpful |
| L3: Categorical | 5 seconds | Medium-High | 5-15% of users | "What was wrong?" dropdown: Inaccurate / Irrelevant / Incomplete / Offensive / Other |
| L4: Textual | 30 seconds | High | 2-5% of users | Free-text explanation of what was wrong and what would be better |
| L5: Comparative | 60 seconds | Very High | 1-3% of users | Side-by-side preference ranking: "Which response is better? A or B" |
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.
- 10d ago First seen · 178 lines · 66 tokens per session scan A cf8eb26959cc
ai-feedback-loops is a skill published in the GitHub repository varunk130/ai-ux-skill-library (3 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 2,107 once invoked, about $0.0003 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 2026-08-31.
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