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 agentmods add skills/murphytrueman/design-system-ops/context-engine-buildernpx skills add murphytrueman/design-system-ops --skill context-engine-buildergit clone --depth 1 https://github.com/murphytrueman/design-system-opsWhat 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 | $0.00135 | $0.04159 |
| Opus 5 | $0.00068 | $0.02080 |
| Sonnet 5 | $0.00027 | $0.00832 |
| Haiku 4.5 | $0.00014 | $0.00416 |
Grade A, and why
context-engine-builder 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 3d 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 — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context engine builder
A skill for generating a context engine — a structured, multi-layered knowledge base that gives AI agents the complete picture of a design system. The engine encodes seven dimensions of system knowledge (UX, UI, content, accessibility, ethical, technical, and business intelligence) as machine-readable blueprints that agents load, reason over, and apply without requiring implicit knowledge or human interpretation.
Context
A design system is more than a component library. It encodes decisions about user experience patterns, visual language, content voice, accessibility requirements, ethical guardrails, technical constraints, and business rules. These decisions live in different places — Figma files, code repos, wikis, Slack threads, the heads of senior team members — and most of them are invisible to AI agents.
When an AI agent interacts with a design system, it typically receives a narrow slice: component props, maybe a description, perhaps some token values. It does not receive the reasoning behind those components, the constraints that govern their use, or the relationships between design decisions and business outcomes. The result is output that is technically valid but contextually wrong — a login form that uses the right components but ignores the system's established authentication patterns, or a dashboard that follows the grid but violates the system's data visualisation principles.
A context engine front-loads this knowledge. Instead of letting agents discover context through trial and error (or not discover it at all), the engine encodes it as structured data that agents load at the start of a task. The seven blueprints are not arbitrary categories — they represent the seven dimensions of knowledge that, when missing, produce the most common classes of AI-generated design system errors.
The practical output is a set of structured files — one per blueprint — that live alongside the codebase and are consumed by AI agents, MCP servers, and developer tooling. Together they form the machine-readable brain of the design system.
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.
- 3d ago First seen · 378 lines · 135 tokens per session scan A aa6449da4ff7
context-engine-builder is a skill published in the GitHub repository murphytrueman/design-system-ops (174 stars, last pushed 11d ago), licensed MIT. It adds 135 tokens to every session and 4,159 once invoked, about $0.0007 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
design-engineering
Premium design engineering skill for agentic workflows — produces high-end, distinctive UI designs using DESIGN.md as the portable contract across Pencil MCP (in-IDE canvas), Figma MCP (team handoff + design tokens), and Google Stitch (vibe exploration + AI generation). Enforces anti-generic principles, WCAG 2.2 AA…
generate-figma-screen
Kod veya açıklamadan Figma'da tam ekran/sayfa oluşturur. Yayınlanmış design system bileşenlerini arayıp instance olarak yerleştirir; hardcode değer yerine DS token'larını kullanır. "Figma'da ekran oluştur", "kodu Figma'ya çevir", "landing page çiz", "ekran tasarla", "generate screen", "UI'ı Figma'ya aktar"…
figma-a11y-audit
Figma ekranını erişilebilirlik açısından denetler. Renk kontrastı (WCAG AA/AAA), minimum dokunma hedefi, fokus sırası, metin boyutu ve platform-bazlı ekran okuyucu önerileri (VoiceOver, TalkBack, ARIA) üretir. "a11y audit", "erişilebilirlik kontrol", "kontrast kontrol", "accessibility check", "ekran okuyucu spec"…
fmcp-intent-router
F-MCP ile ilgili herhangi bir kullanıcı talebinin ilk giriş noktası. Kullanıcının niyetini analiz eder, hangi hedef SKILL'in çalıştırılacağına karar verir, o SKILL için gereken eksik input'ları tek turda toplar, özet+onay alır ve ondan sonra hedef SKILL'i çalıştırır. "figma", "ekran oluştur", "tasarım yap", "component…
fmcp-screen-recipes
Fast path cookbook — standart ekran tipleri (login/payment/profile/list/detail/form/onboarding/dashboard/settings) için 5 mega-adımlı recipe. Max 15 op/execute, cache-first discovery, her adımda Türkçe micro-report.
generate-figma-library
Kod tabanından Figma'da profesyonel design system kütüphanesi inşa eder. Variable collection, primitive/semantic token, bileşen (variant, auto-layout, property), sayfa yapısı ve tema desteği oluşturur. "DS kütüphanesi oluştur", "design system inşa et", "token'ları Figma'ya yaz", "bileşen kütüphanesi kur", "generate…