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/marcusrbrown/systematic/frontend-designnpx skills add marcusrbrown/systematic --skill frontend-designgit clone --depth 1 https://github.com/marcusrbrown/systematicWrote 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/marcusrbrown/systematic/frontend-design)<a href="https://agentmods.dev/skills/marcusrbrown/systematic/frontend-design"><img src="https://agentmods.dev/badge/skills/marcusrbrown/systematic/frontend-design.svg" alt="Measured on agentmods" 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 | $0.00073 | $0.03961 |
| Opus 5 | $0.00036 | $0.01980 |
| Sonnet 5 | $0.00015 | $0.00792 |
| Haiku 4.5 | $0.00007 | $0.00396 |
Grade A, and why
frontend-design 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 4d 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 — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frontend Design
Guide creation of distinctive, production-grade frontend interfaces that avoid generic AI aesthetics. This skill covers the full lifecycle: detect what exists, plan the design, build with intention, and verify visually.
Authority Hierarchy
Every rule in this skill is a default, not a mandate.
- Existing design system / codebase patterns -- highest priority, always respected
- User's explicit instructions -- override skill defaults
- Skill defaults -- apply in greenfield work or when the user asks for design guidance
When working in an existing codebase with established patterns, follow those patterns. When the user specifies a direction that contradicts a default, follow the user.
Workflow
Detect context -> Plan the design -> Build -> Verify visually
Layer 0: Context Detection
Before any design work, examine the codebase for existing design signals. This determines how much of the skill's opinionated guidance applies.
What to Look For
- Design tokens / CSS variables:
--color-*,--spacing-*,--font-*custom properties, theme files - Component libraries: shadcn/ui, Material UI, Chakra, Ant Design, Radix, or project-specific component directories
- CSS frameworks:
tailwind.config.*,styled-componentstheme, Bootstrap imports, CSS modules with consistent naming - Typography: Font imports in HTML/CSS,
@font-facedeclarations, Google Fonts links - Color palette: Defined color scales, brand color files, design token exports
- Animation libraries: Framer Motion, GSAP, anime.js, Motion One, Vue Transition imports
- Spacing / layout patterns: Consistent spacing scale usage, grid systems, layout components
Use the platform's native file-search and content-search tools (e.g., Glob/Grep in OpenCode) to scan for these signals. Do not use shell commands for routine file exploration.
Mode Classification
Based on detected signals, choose a mode:
- Existing system (4+ signals across multiple categories): Defer to it. The skill's aesthetic opinions (typography, color, motion) yield to the established system. Structural guidance (composition, copy, accessibility, verification) still applies.
- Partial system (1-3 signals): Follow what exists; apply skill defaults only for areas where no convention was detected. For example, if Tailwind is configured but no component library exists, follow the Tailwind tokens and apply skill guidance for component structure.
- Greenfield (no signals detected): Full skill guidance applies.
- Ambiguous (signals are contradictory or unclear): Ask the user before proceeding.
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.
- 4d ago First seen · 326 lines · 73 tokens per session scan A ee05a20946f0
frontend-design is a skill published in the GitHub repository marcusrbrown/systematic (24 stars, last pushed yesterday), licensed MIT. It adds 73 tokens to every session and 3,961 once invoked, about $0.0004 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
plan-protocol
Guidelines for creating and managing implementation plans with citations.
plan-review
Criteria for reviewing implementation plans against quality standards.
code-review
Comprehensive code review methodology with severity classification and confidence thresholds.
code-philosophy
Internal logic and data flow philosophy (The 5 Laws of Elegant Defense). Understand deeply to ensure code guides data naturally and prevents errors.
frontend-philosophy
Visual & UI philosophy (The 5 Pillars of Intentional UI). Understand deeply to avoid "AI slop" and create distinctive, memorable interfaces.
opencode-ensemble
Use when coordinating multiple coding agents, delegating independent software work, managing OpenCode Ensemble teams, choosing teammate roles or models, reviewing teammate output, or deciding whether parallel execution is appropriate.