apps-sdk-template: A minimalist Typescript ChatGPT App based on the Skybridge framework

by alpic-ai · MCP Server · ★ 114

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: CAUTION · quality 68/100

Flagged: opens a tunnel service. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

chatgptmcpopenaiskybridge

Quick Facts

Stars114
Forks20
LanguageTypeScript
CategoryMCP Server
Quality Score68/100
Last Updated2026-11-07
Created2025-10-09
Platformsmcp, node
Est. Tokens~14k

About apps-sdk-template

Apps SDK Template A minimal TypeScript template for building ChatGPT and MCP Apps with widget rendering. This template has been generated and is powered by Skybridge. Getting Started Prerequisites Node.js 24+ HTTP tunnel such as ngrok Local Development Install Start your local server Run the development server from the root directory: Open DevTools to test your app locally: http://localhost:3000/...

Compatible Skills

These tools work well together with apps-sdk-template for enhanced workflows:

  • openai-oauth — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (56%)
  • ai-gate — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (55%)
  • lowcode-vscode — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (55%)
  • open-chatgpt-atlas — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (54%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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