Borrowing it
Nothing to install: this file belongs to 3x-haust/Mimikyu. 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/3x-haust/Mimikyu/main/CLAUDE.mdgit clone --depth 1 https://github.com/3x-haust/MimikyuWrote 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/instructions/3x-haust/mimikyu/claude-md)<a href="https://agentmods.dev/instructions/3x-haust/mimikyu/claude-md"><img src="https://agentmods.dev/badge/instructions/3x-haust/mimikyu/claude-md/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/instructions/3x-haust/mimikyu/claude-md"><img src="https://agentmods.dev/badge/instructions/3x-haust/mimikyu/claude-md.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 | $0.04153 | $0.04153 |
| Opus 5 | $0.02076 | $0.02076 |
| Sonnet 5 | $0.00831 | $0.00831 |
| Haiku 4.5 | $0.00415 | $0.00415 |
Grade A, and why
Mimikyu CLAUDE.md scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
#### 보조: curl 직접 호출 (MCP 불가 시 fallback) Copies of this mod
1 near-identical copy found in the catalogue:
- Mimikyu AGENTS.md — 100% identical, 27 lines differ
How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mimikyu (따라큐) - Figma Pixel Perfect Publisher
개요
Figma 디자인을 픽셀 퍼펙트하게 React + Tailwind CSS로 재현하는 도구. Figma MCP (Framelink + 공식)로 디자인 데이터를 추출하고, Playwright + PIL로 픽셀 단위 검증 루프를 돌린다.
프로젝트 구조
Mimikyu/
├── scripts/ # 공용 스크립트 (screenshot.ts, compare.py, mimikyu.ts, verify.ts)
└── <각 프로젝트> # 폴더 구조 강제 없음 (아래 참조)
폴더 구조를 강제하지 않는다.
- 사용자가 경로/기존 프로젝트/폴더 규칙을 주면 → 그대로 따른다 (기존 컨벤션을 존중, 엉뚱한 폴더 추가 금지).
- 아무 정보가 없으면 → AI가 합리적인 구조를 스스로 정하고 진행한다. 물어보지 않고 진행 (예: 스택 네이티브 스캐폴드 + 앱 옆 designs/screenshots/diffs).
- 사용자 정보 > AI 자율 판단.
기술 스택
- React 19 + TypeScript + Tailwind CSS v4 (@tailwindcss/vite) + Vite
- Playwright (headless Chromium 스크린샷)
- Python PIL/Pillow (픽셀 비교 + 히트맵)
- pnpm
- Figma MCP 서버:
- Framelink (
framelink-figma) —get_figma_data,download_figma_images - 공식 Figma (
figma) —view_node,add_figma_file,read_comments
- Framelink (
워크플로우
Phase 1: Figma 디자인 데이터 추출 및 저장
MCP 도구를 사용하여 Figma 데이터를 가져오고, designs/figma-data.json에 저장한다.
이후 검증 루프에서는 MCP를 다시 호출하지 않고 이 파일을 참조하여 수정한다.
1. Framelink MCP — 디자인 구조 + 스타일 데이터
mcp__framelink-figma__get_figma_data(
fileKey: "<FILE_KEY>",
nodeId: "<NODE_ID>" // URL의 node-id 파라미터
)
- 레이아웃 구조, 스타일, 좌표, 타이포그래피 등 전체 디자인 데이터 반환
depth옵션으로 트리 탐색 깊이 제어 가능
2. Framelink MCP — 이미지 에셋 다운로드
mcp__framelink-figma__download_figma_images(
fileKey: "<FILE_KEY>",
nodeId: "<NODE_ID>",
outputDir: "public/assets/"
)
- 아이콘, 일러스트 등 이미지 에셋을 로컬에 다운로드
3. 공식 Figma MCP — 노드 시각 확인
mcp__figma__view_node(
file_key: "<FILE_KEY>",
node_id: "<NODE_ID>" // "1:46" 형식
)
- 특정 노드의 썸네일 이미지 확인용
Figma URL 파싱
https://www.figma.com/design/<FILE_KEY>/Title?node-id=<NODE_ID>
추출할 데이터:
absoluteBoundingBox→ 각 요소의 정확한 x, y, width, heightfills→ 배경색, 그라디언트style→ font-family, font-size, font-weight, line-height, letter-spacingcornerRadius→ border-radiuseffects→ box-shadowstrokes→ border
보조: curl 직접 호출 (MCP 불가 시 fallback)
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.
- yesterday Changed · +27 lines · +342 tokens per session dfa433f59af9
- 9d ago First seen · 292 lines · 3,811 tokens per session scan A 1513169d5ea0
Mimikyu CLAUDE.md is an instructions file published in the GitHub repository 3x-haust/Mimikyu (25 stars, last pushed yesterday), licensed MIT. It adds 4,153 tokens to every session, about $0.0208 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.