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/misonl/ling/app-buildernpx skills add MisonL/Ling --skill app-buildergit clone --depth 1 https://github.com/MisonL/LingWhat 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 | $0.00041 | $0.00977 |
| Opus 5 | $0.00020 | $0.00489 |
| Sonnet 5 | $0.00008 | $0.00195 |
| Haiku 4.5 | $0.00004 | $0.00098 |
Grade A, and why
app-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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Builder - 应用构建编排器
分析用户请求，确定技术栈，规划结构，并协调智能体执行。
选择性阅读规则
只阅读与当前请求相关的文件！ 先查看内容地图，再读取所需文档。
| File（文件） | Description（描述） | When to Read（阅读时机） |
|---|---|---|
project-detection.md |
关键词矩阵、项目类型检测 | 开始新项目 |
tech-stack.md |
2026 默认技术栈及替代方案 | 选择技术时 |
agent-coordination.md |
智能体流水线、执行顺序 | 协调多智能体协作时 |
scaffolding.md |
目录结构、核心文件 | 创建项目结构时 |
feature-building.md |
功能分析、错误处理 | 在现有项目中添加功能时 |
templates/SKILL.md |
Project templates（项目模板） | 为新项目搭建脚手架时 |
模板（13）
用于新项目快速脚手架搭建。只读取匹配模板！
| Template（模板） | Tech Stack（技术栈） | When to Use（适用场景） |
|---|---|---|
| nextjs-fullstack | Next.js + Prisma | Full-stack（全栈）Web 应用 |
| nextjs-saas | Next.js + Stripe | SaaS（软件即服务）产品 |
| nextjs-static | Next.js + Framer | Landing page（落地页） |
| nuxt-app | Nuxt 3 + Pinia | Vue（前端框架）全栈应用 |
| express-api | Express + JWT | REST API（接口服务） |
| python-fastapi | FastAPI | Python API（Python 接口） |
| react-native-app | Expo + Zustand | Mobile app（移动端应用） |
| flutter-app | Flutter + Riverpod | Cross-platform（跨平台）移动端 |
| electron-desktop | Electron + React | Desktop app（桌面端应用） |
| chrome-extension | Chrome MV3 | Browser extension（浏览器扩展） |
| cli-tool | Node.js + Commander | CLI（命令行）应用 |
| monorepo-turborepo | Turborepo + pnpm | Monorepo（单仓多包） |
相关智能体
| Agent（智能体） | 角色 |
|---|---|
project-planner |
任务拆解、依赖图构建 |
frontend-specialist |
UI（用户界面）组件、页面 |
backend-specialist |
API（接口）、业务逻辑 |
database-architect |
数据结构（Schema）、迁移 |
devops-engineer |
部署、预览 |
使用示例
What ships with it
19 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agent-coordination.md 4.7 KB
- feature-building.md 1.3 KB
- project-detection.md 1.7 KB
- scaffolding.md 3.4 KB
- tech-stack.md 1.2 KB
- templates/astro-static/TEMPLATE.md 2.5 KB
- templates/chrome-extension/TEMPLATE.md 2.6 KB
- templates/cli-tool/TEMPLATE.md 2.5 KB
- templates/electron-desktop/TEMPLATE.md 2.4 KB
- templates/express-api/TEMPLATE.md 2.1 KB
- templates/flutter-app/TEMPLATE.md 2.1 KB
- templates/monorepo-turborepo/TEMPLATE.md 2.5 KB
- templates/nextjs-fullstack/TEMPLATE.md 4.4 KB
- templates/nextjs-saas/TEMPLATE.md 3.9 KB
- templates/nextjs-static/TEMPLATE.md 6.7 KB
- templates/nuxt-app/TEMPLATE.md 4.6 KB
- templates/python-fastapi/TEMPLATE.md 2.4 KB
- templates/react-native-app/TEMPLATE.md 4.7 KB
- templates/SKILL.md 1.8 KB
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 · 76 lines · 41 tokens per session scan A 6af23f9a3731
app-builder is a skill published in the GitHub repository MisonL/Ling (9 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 977 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…