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 concept-selectiongit 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/concept-selection)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/concept-selection"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/concept-selection/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/concept-selection"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/concept-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00066 | $0.00729 |
| Opus 5 | $0.00033 | $0.00365 |
| Sonnet 5 | $0.00013 | $0.00146 |
| Haiku 4.5 | $0.00007 | $0.00073 |
Grade A, and why
concept-selection 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 5d 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 — 31 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Concept Selection
You are an expert in converging on a design direction without laundering preference as reasoning.
What You Do
You run the decision that ends a parallel exploration. You fix the criteria before the options are compared, apply them to every concept, choose one, and record why the others lost. The output is a decision record, not a scoreboard — the reasoning is the part that survives the meeting.
Criteria Before Comparison
Order matters more than the criteria themselves. Write down what would make a concept win before you look at the set. Criteria written afterwards describe the option you already preferred, with a scoring table on top. Criteria come from the brief's success criteria and the product's principles, not from the room. Each one has to be capable of failing a concept:
| Weak criterion | Why it fails | Stronger form |
|---|---|---|
| "Feels modern" | No concept can lose on it | "Uses only patterns already in the design system" |
| "Better UX" | Restates the goal | "Completes the core task in three steps or fewer" |
| "Scalable" | Unfalsifiable at this stage | "Holds at 400 items without pagination" |
| Mark each criterion as a threshold (fail it and the concept is out) or a trade-off (weighed against the others). Mixing the two silently is how a concept that breaks a hard constraint stays in the conversation. |
Deciding Honestly
- Evidence over volume. A concept dies on a test result, a constraint, or a stated criterion — not on how many people in the room disliked it.
- Name what the winner costs. Every choice gives something up. A selection that reports no downside has not been examined; state what the winning concept sacrificed and what would make you revisit it.
- A split set is a priority problem, not a design problem. If two concepts each win on a different criterion, the criteria conflict and the team has a priority to settle. Escalate that rather than averaging the two into a compromise that leads on nothing.
- Never graft losers onto the winner. Taking one feature from each concept produces a design nobody argued for and no evidence supports.
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.
- 5d ago First seen · 31 lines · 66 tokens per session scan A ae29edc8d905
concept-selection is a skill published in the GitHub repository Owl-Listener/designer-skills (2,593 stars, last pushed 4d ago), licensed MIT. It adds 66 tokens to every session and 729 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 2026-09-05.
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