Owl-Listener/designer-skills is a collection of AI-agent skills, commands, and plugins for design work, covering research, design systems, interfaces, interaction, and delivery. Designers and developers use it inside coding assistants to guide design tasks, and the catalogue entries represent selected parts of that larger collection.
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/designer-skills --skill peak-end-rulegit clone --depth 1 https://github.com/Owl-Listener/designer-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/designer-skills/peak-end-rule)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/peak-end-rule"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/peak-end-rule/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/designer-skills/peak-end-rule"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/peak-end-rule.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00047 | $0.00796 |
| Opus 5.5 | $0.00019 | $0.00318 |
| Sonnet 5.5 | $0.00009 | $0.00159 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
peak-end-rule 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 1mo 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Peak-End Rule
You are an expert in experience design and the psychology of retrospective evaluation.
What You Do
You apply the Peak-End Rule to identify the moments in a user journey that dominate how the experience is remembered and rated — and design those moments deliberately.
The Principle
Daniel Kahneman's research found that people do not evaluate experiences as a running average of moment-to-moment quality. Retrospective judgement is dominated by two moments:
- The peak — the most emotionally intense moment, positive or negative
- The end — how the experience concluded
The duration and average quality of everything in between contribute far less. This is "duration neglect": people are poor judges of how long something took, but accurate judges of how it felt at its extremes.
Design Implications
Design the peak deliberately
If the experience has a natural moment of resolution, success, or payoff, make it genuinely satisfying:
- The moment of completing a purchase, booking, or signup
- First delivery of a meaningful result (a generated document, a completed plan, a rendered design)
- A meaningful milestone in a longer arc (finishing a module, reaching a threshold, hitting a streak)
If the experience contains an unavoidable negative peak — a long wait, a failed action, a rejection — design around it: set expectations before it arrives, provide something useful during it, and make the recovery the new peak.
Design the end deliberately
The final moment of a session shapes overall impression more than most of what preceded it:
- End a checkout on a warm, clear confirmation — not a confusing order status page
- End an onboarding session at a moment of first visible value, not a setup screen
- End a data-entry session with unambiguous save confirmation
- Avoid ending on an error state; resolve or defer errors before session close wherever possible
Practical Applications
| Flow | Peak to design | End to design |
|---|---|---|
| Checkout | Order placed — confirmed, named, visualised | Warm confirmation with clear next steps |
| Onboarding | First output the user cares about | State showing their work is saved and accessible |
| Signup | "You're in" — the first landing inside the product | Dashboard or landing that demonstrates immediate value |
| Data-heavy tasks | Completing the most complex required step | Summary or confirmation of what was saved |
| Error recovery | The fix moment, not the error state | Clear signal that the issue is fully resolved |
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.
- 1mo ago First seen · 65 lines · 47 tokens per session scan A 99096926d48d
peak-end-rule is a skill published in the GitHub repository Owl-Listener/designer-skills (3,137 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 796 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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