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 user-satisfaction-signalsgit 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/user-satisfaction-signals)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/user-satisfaction-signals"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/user-satisfaction-signals/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/user-satisfaction-signals"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/user-satisfaction-signals.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 | $0.00021 | $0.00514 |
| Opus 5 | $0.00010 | $0.00257 |
| Sonnet 5 | $0.00004 | $0.00103 |
| Haiku 4.5 | $0.00002 | $0.00051 |
Grade A, and why
user-satisfaction-signals 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 12d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Satisfaction Signals
Users rarely tell you directly whether they're satisfied. Most satisfaction signals are implicit — buried in behavior patterns that you have to design systems to capture and interpret.
Explicit Satisfaction Signals
These are signals users give intentionally:
- Thumbs up/down: Direct quality rating
- Star ratings: Graded satisfaction
- Written feedback: Comments about what worked or didn't
- NPS or satisfaction surveys: Periodic overall assessment
- Feature requests: Signals of engagement even when expressing a gap
Implicit Satisfaction Signals
These are behavioral signals that indicate satisfaction or dissatisfaction: Positive signals:
- Using the output as-is (no edits)
- Copying the output
- Returning to use the feature again
- Increasing usage over time
- Trying more advanced features Negative signals:
- Regenerating the response (asking the AI to try again)
- Editing the output heavily
- Rephrasing the same request multiple times
- Abandoning mid-task
- Decreasing usage over time
- Switching to manual methods Ambiguous signals:
- Long sessions (engaged or struggling?)
- Many turns (deep work or frustrated iteration?)
- Silence after a response (satisfied or confused?)
Designing Signal Collection
- Instrument the product: Track edits, regenerations, copy events, session duration, and return patterns
- Minimise explicit feedback burden: Don't ask for ratings on every response
- Contextualise signals: A regeneration during creative brainstorming means something different than a regeneration during fact-finding
- Segment by task type: Satisfaction patterns vary by what the user is trying to do
- Combine signals: No single signal is reliable. Look for patterns across multiple signals.
From Signals to Insights
Raw signals need interpretation:
- Signal clustering: Which negative signals appear together? That pattern indicates a specific problem.
- Trend analysis: Are signals improving or degrading over time?
- Cohort comparison: Do new users show different signals than experienced users?
- Correlation with outcomes: Which signals best predict task success or retention?
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.
- 12d ago First seen · 51 lines · 21 tokens per session scan A f58d95f46815
user-satisfaction-signals is a skill published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 514 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…