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 gosha70/code-copilot-team --skill design-systemgit clone --depth 1 https://github.com/gosha70/code-copilot-teamWrote 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/gosha70/code-copilot-team/design-system)<a href="https://agentmods.dev/skills/gosha70/code-copilot-team/design-system"><img src="https://agentmods.dev/badge/skills/gosha70/code-copilot-team/design-system/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/gosha70/code-copilot-team/design-system"><img src="https://agentmods.dev/badge/skills/gosha70/code-copilot-team/design-system.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 | $0.00045 | $0.01579 |
| Opus 5.5 | $0.00018 | $0.00632 |
| Sonnet 5.5 | $0.00009 | $0.00316 |
| Haiku 4.5 | $0.00005 | $0.00158 |
Grade A, and why
design-system 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design System Protocol
Invoke this skill for any web UI work (new frontend, new screen, UI refactor). Its job: stop AI-generated UI from converging on the statistical mean of the training data ("AI slop") by committing design decisions before code, and enforcing them as a machine-checkable filter.
Root cause it fixes: LLMs emit the highest-probability "nice" defaults —
Inter font, purple→blue gradients, shadcn-default cards, centered hero + three
feature columns, <div onClick> accessibility. No prompt adjective escapes this;
only pre-committed constraints do. This skill produces and enforces those
constraints.
The steering bundle: DESIGN.md + design/tokens.json
Every UI-bearing project commits a bundle, not a prose memo:
DESIGN.mdat repo root — human + machine readable (YAML front-matter tokens- prose rationale and bans).
design/tokens.json— DTCG-format design tokens, two-tier primitive → semantic. Components reference only semantic tokens (else dark mode / re-theming becomes a rewrite). Compiles totokens.css(Tailwind v4@theme→ CSS variables).
If the bundle is missing, author it first (Step 1–2). If it exists, read it before writing any component and build strictly within it.
Step 1 — Derive direction from the business domain (not taste)
"Clean and modern" is the slop default, not a direction. Derive an opinionated direction from the app's domain:
- Brand archetype (pick a primary + secondary from Jung's 12 — Ruler, Hero, Outlaw, Caregiver, Creator, Explorer, Sage, …). Each maps to visual semiotics: Ruler → restrained composition, elegant type, muted+metal; Outlaw → bold edgy type, dark + acid accent; Caregiver → soft, warm, rounded, reassuring.
- Target user + key tasks → information density and IA (a compliance analyst scanning tables ≠ a consumer onboarding flow).
- Tone → voice/copy rules + type personality.
- Lock one aesthetic direction from a small enumerated set (Swiss/editorial, brutalist, industrial-mono, organic, warm-minimal, high-density-data) and commit its tokens.
- Reference-ground: 1–3 real UIs whose feeling matches — extract palette, type scale, density; use to set constraints, never to clone.
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 · 114 lines · 45 tokens per session scan A dbbc4d59985c
design-system is a skill published in the GitHub repository gosha70/code-copilot-team (7 stars, last pushed 17d ago), licensed MIT. It adds 45 tokens to every session and 1,579 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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