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/kazdenc/builder-skills/normalizenpx skills add kazdenc/builder-skills --skill normalizegit clone --depth 1 https://github.com/kazdenc/builder-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/kazdenc/builder-skills/normalize)<a href="https://agentmods.dev/skills/kazdenc/builder-skills/normalize"><img src="https://agentmods.dev/badge/skills/kazdenc/builder-skills/normalize.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 | $0.00056 | $0.00808 |
| Opus 5 | $0.00028 | $0.00404 |
| Sonnet 5 | $0.00011 | $0.00162 |
| Haiku 4.5 | $0.00006 | $0.00081 |
Grade A, and why
normalize 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.
This is a copy
91% identical to normalize — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze and redesign the feature to perfectly match our design system standards, aesthetics, and established patterns.
Plan
Before making changes, deeply understand the context:
-
Discover the design system: Search for design system documentation, UI guidelines, component libraries, or style guides (grep for "design system", "ui guide", "style guide", etc.). Study it thoroughly until you understand:
- Core design principles and aesthetic direction
- Target audience and personas
- Component patterns and conventions
- Design tokens (colors, typography, spacing)
CRITICAL: If something isn't clear, ask. Don't guess at design system principles.
-
Analyze the current feature: Assess what works and what doesn't:
- Where does it deviate from design system patterns?
- Which inconsistencies are cosmetic vs. functional?
- What's the root cause—missing tokens, one-off implementations, or conceptual misalignment?
-
Create a normalization plan: Define specific changes that will align the feature with the design system:
- Which components can be replaced with design system equivalents?
- Which styles need to use design tokens instead of hard-coded values?
- How can UX patterns match established user flows?
IMPORTANT: Great design is effective design. Prioritize UX consistency and usability over visual polish alone. Think through the best possible experience for your use case and personas first.
Execute
Systematically address all inconsistencies across these dimensions:
- Typography: Use design system fonts, sizes, weights, and line heights. Replace hard-coded values with typographic tokens or classes.
- Color & Theme: Apply design system color tokens. Remove one-off color choices that break the palette.
- Spacing & Layout: Use spacing tokens (margins, padding, gaps). Align with grid systems and layout patterns used elsewhere.
- Components: Replace custom implementations with design system components. Ensure props and variants match established patterns.
- Motion & Interaction: Match animation timing, easing, and interaction patterns to other features.
- Responsive Behavior: Ensure breakpoints and responsive patterns align with design system standards.
- Accessibility: Verify contrast ratios, focus states, ARIA labels match design system requirements.
- Progressive Disclosure: Match information hierarchy and complexity management to established patterns.
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 · 71 lines · 56 tokens per session scan A fc1b68d70588
normalize is a skill published in the GitHub repository kazdenc/builder-skills (44 stars, last pushed 5mo ago), licensed MIT. It adds 56 tokens to every session and 808 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to normalize, differing in 10 lines, and is treated as a copy.
Other skills, from other repositories
magic-ui
Use this skill when users want to add, customize, or troubleshoot Magic UI components in React/Next.js projects. It covers component selection, shadcn registry installation (@magicui/), integration patterns, and practical quality checks for accessibility and maintainability.
kiranism-shadcn-dashboard
Guide for building features, pages, tables, forms, themes, and navigation in this Next.js 16 shadcn dashboard template. Use this skill whenever the user wants to add a new page, create a feature module, build a data table, add a form, configure navigation items, add a theme, set up RBAC access control, or work with…
audit
Perform comprehensive audit of interface quality across accessibility, performance, theming, and responsive design. Generates detailed report of issues with severity ratings and recommendations.
critique
Evaluate design effectiveness from a UX perspective. Assesses visual hierarchy, information architecture, emotional resonance, and overall design quality with actionable feedback.
omd:autopilot
One-prompt autonomous product design and implementation. Use automatically for broad greenfield UI requests such as 'from scratch', '새 제품/화면을 알아서 만들어줘', or requests that delegate DESIGN.md creation. It decides whether to reuse, establish, refresh, or skip a project design system; asks at most one consequential…
omd:slop-audit
실제 제품 route의 UI·UX copy를 검사해 제품 맥락 없이 반복된 생성형 기본 패턴, 브랜드 근거 없는 장식, 카드·그라데이션·아이콘 타일 남용, 번역투와 추상 카피를 rule ID와 line ref로 진단한다. 'AI slop 잡아줘', '템플릿 같아', '왜 AI가 만든 화면 같지?', 'anti-slop audit' 요청에 사용한다. 접근성 오류와 취향 차이를 별도 등급으로 구분한다.