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 parallel-conceptsgit 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/parallel-concepts)<a href="https://agentmods.dev/skills/owl-listener/designer-skills/parallel-concepts"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/parallel-concepts/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/parallel-concepts"><img src="https://agentmods.dev/badge/skills/owl-listener/designer-skills/parallel-concepts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
- Socket pass
- Snyk 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 | $0.00059 | $0.00755 |
| Opus 5.5 | $0.00024 | $0.00302 |
| Sonnet 5.5 | $0.00012 | $0.00151 |
| Haiku 4.5 | $0.00006 | $0.00076 |
Grade A, and why
parallel-concepts 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 — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parallel Concepts
You are an expert in divergent exploration — holding multiple competing solutions to one problem before committing to any of them.
What You Do
You take a problem that already has a proposed solution and construct a set of genuinely different solutions to the same problem, held at equal effort until there is evidence to choose. You decide how wide the set should be and which dimension the concepts must differ on. You do not rank or eliminate them — that is concept-selection.
Why Parallel Beats Serial
Iteration and exploration buy different things. Refining one concept improves that concept. Building several in parallel improves your model of the solution space — you learn which of your assumptions were load-bearing and which were arbitrary. Stanford's parallel prototyping research (Dow, Glienke and Klemmer, 2010) found designers who produced concepts in parallel outperformed those who iterated serially on a single design for the same total effort, measured on real audience response rather than preference. Two secondary effects matter as much as the result:
- Critique lands better. With one design on the table, feedback reads as a verdict on the designer. With several, it reads as information about the options.
- The first idea loses its unearned advantage. Whatever arrives first becomes the reference point, and every later idea gets judged as a deviation from it rather than on its own terms. A parallel set removes the incumbent. The cost is real — n concepts cost roughly n times as much. The resolution is to spread early, while a concept still costs a sketch instead of a build.
What Makes Concepts Distinct
A set is only informative if its members differ on the dimension the decision turns on. The test is behavioural, not visual: does the user do something different?
- Sequence — what the user is asked for first, and what waits
- Unit of interaction — one item at a time, a batch, or a continuous stream
- Division of labour — what the person decides versus what the system decides for them
- Entry point — where the task begins and what it assumes the user already knows
- Commitment point — how far in the user goes before the action becomes irreversible Two concepts with the same steps in the same order and different visual treatment are one concept rendered twice. Cut one and spend the effort on a real third direction.
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 · 34 lines · 59 tokens per session scan A 6e6350577164
parallel-concepts is a skill published in the GitHub repository Owl-Listener/designer-skills (3,079 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 755 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 2026-09-05.
Other skills, from other repositories
ui-review
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design-craft
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design-critique
Use this skill to audit an interface that already exists and say precisely why it looks generated, templated, or unfinished — a scored rubric across composition, typography, color, states, accessibility and copy, ending in a ranked fix list. Triggers: user says "review the design", "critique this UI", "why does this…
design-reference-read
Read a reference website or screenshot the way a designer does — by measuring it. Produces a dated "design DNA" record (color roles, type scale and ratio, spacing unit, radii, elevation, motion, section rhythm per viewport, component geometry, declared tokens) with measured and inferred values kept apart, names the…
anti-slop-frontend
A mechanical, countable anti-slop checklist for AI-generated frontend. Catches the specific signatures an undirected model defaults to: AI-purple glows, Inter-everywhere, em-dashes, div-based fake screenshots, eyebrow-on-every-section, beige+brass "premium" palettes, generic Jane Doe / Acme data. Advisory layer that…
design-to-code
Implement a supplied Figma design, screenshot, live reference site or design specification as a working interface, and prove the match with measured fidelity scores per axis (color, type, space, shape, layout, motion) instead of eyeballing. Use when matching a reference or handling design handoff; preserve its layout…