Borrowing it
Nothing to install: this file belongs to kwakseongjae/oh-my-design. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/kwakseongjae/oh-my-design/main/.claude/skills/omd-remember/SKILL.mdgit clone --depth 1 https://github.com/kwakseongjae/oh-my-designWrote 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/kwakseongjae/oh-my-design/omd-remember)<a href="https://agentmods.dev/skills/kwakseongjae/oh-my-design/omd-remember"><img src="https://agentmods.dev/badge/skills/kwakseongjae/oh-my-design/omd-remember/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/kwakseongjae/oh-my-design/omd-remember"><img src="https://agentmods.dev/badge/skills/kwakseongjae/oh-my-design/omd-remember.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 3 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- high Prompt Injection · line 34 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 43 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00095 | $0.01620 |
| Opus 5 | $0.00048 | $0.00810 |
| Sonnet 5 | $0.00019 | $0.00324 |
| Haiku 4.5 | $0.00010 | $0.00162 |
Grade A, and why
omd:remember 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 11d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
omd:remember — Preference Logger
사용자의 디자인 선호/교정을 .omd/preferences.md에 append-only로 기록한다. 나중에 omd:learn이 배치로 DESIGN.md에 반영. CLI 호출 없음 — Read/Edit/Write 툴로 직접 처리.
트리거 발화 패턴
- "기억해 둬", "앞으로는 ~로 해"
- "우리는 ~한다 / ~하지 않는다"
- "remember that ...", "going forward ..."
- "rule of thumb: ..."
- 사용자가 당신의 디자인 선택을 명시적으로 교정
파일 포맷 (.omd/preferences.md)
frontmatter + 엔트리 시퀀스. 엔트리 하나당 ## <heading> + omd-meta 코드블록 + body.
---
schema: omd.preferences/v1
design_md_hash_at_creation: <hash 또는 빈 문자열>
---
# Preference Log
## 2026-04-30T17:48:00.000Z — ctas-never-uppercase
```omd-meta
id: pref_lqxk2_a3f9c1d4
timestamp: 2026-04-30T17:48:00.000Z
scope: components.button
signal: user-statement
confidence: explicit
status: pending
source_agent: claude-code
source_context: "src/components/Button.tsx"
```
CTAs are never uppercase
실행 절차
Step 1 — note 정규화
사용자 발화를 한 문장 영문으로 요약 (예: "앞으로 CTA 대문자 쓰지마" → CTAs are never uppercase).
Step 2 — scope 추론
note 내용에서 다음 매핑 우선순위 사용:
| 매칭 키워드 (정규식, case-i) | scope |
|---|---|
\b(buttons?|ctas?|btns?)\b |
components.button |
\b(cards?)\b |
components.card |
\b(dialogs?|modals?)\b |
components.dialog |
\b(inputs?|fields?|forms?)\b |
components.input |
\b(nav|navigation|headers?|menus?)\b |
components.navigation |
\b(badges?|chips?|pills?|tags?)\b |
components.badge |
\b(tables?|rows?|cells?)\b |
components.table |
\b(dropdowns?|selects?|comboboxes?)\b |
components.dropdown |
\b(toasts?|notifications?|snackbars?)\b |
components.toast |
\b(tabs?)\b |
components.tabs |
\b(colors?|palette|hex|hue|saturation|shades?|tints?|gradients?)\b |
color |
\b(font|typography|typeface|weight|leading|tracking|letter-?spacing)\b |
typography |
\b(spacing|gap|padding|margin|grid)\b |
spacing |
\b(voice|tone|copy|microcopy|wording|language)\b |
voice |
\b(motion|animation|transition|easing|duration)\b |
motion |
\b(layout|structure|hierarchy)\b |
layout |
\b(theme|aesthetic|vibe|mood|look|feel)\b |
visualTheme |
| (어느 것도 매칭 X) | visualTheme |
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.
- 11d ago First seen · 125 lines · 95 tokens per session scan A cd887f96ca20
omd:remember is a skill published in the GitHub repository kwakseongjae/oh-my-design (498 stars, last pushed 4d ago), licensed MIT. It adds 95 tokens to every session and 1,620 once invoked, about $0.0005 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
dembrandt
A TypeScript command-line tool that renders a web page with Playwright and examines its actual styles. It turns colours, typography, spacing, borders, shadows, animation curves, components, and breakpoints into structured design tokens.
extract-design
Extract the full design language from any website URL. Produces 8 output files including AI-optimized markdown, visual HTML preview, Tailwind config, React theme, shadcn/ui theme, Figma variables, W3C design tokens, and CSS variables. Also runs WCAG accessibility scoring. Use when user says 'extract design', 'get…
designlang-tokens
Use when styling UI for cal.com — references the extracted design system tokens instead of inventing colors, spacing, or typography.
session-memory
Persist and recall findings across skill runs within and between sessions, building a cumulative knowledge base of what has been discovered about a design system. This is the cross-skill memory layer — it saves what was found, when, and by which skill, so future runs can compare, correlate, and avoid repeating work.…
memoire-design-tooling
Use when a task spans interface understanding, design-system memory, UI audits, design CI, Figma, shadcn or Tailwind code generation, research, or agent design workflows and needs the correct Memi capability selected.
intake
The guided walkthrough for a codebase that has just installed montology (or has never written its vocabulary down) — phased questions served to the people who own the code as a clean one-question-at-a-time form in their browser (a local HTML file, no hosted service), answers returned to the agent on disk, each round…