by GulajavaMinistudio · Codex Skill · ★ 83
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/GulajavaMinistudio/awesome-copilot-id.git
Taken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. 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.
| Stars | 83 |
| Forks | 13 |
| Language | JavaScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 68/100 |
| Open Issues | 1 |
| Last Updated | 2026-10-20 |
| Created | 2025-12-11 |
| Platforms | claude-code, codex, node |
| Est. Tokens | ~22k |
Awesome Copilot Indonesia 🇮🇩 - AI Agents & SDLC Workflows A curated collection of custom agents, skills, rules, and prompts. Originally for GitHub Copilot, this collection now fully supports OpenCode, Google Antigravity, CommandCode, ChatGPT Codex, Pi Dev Coding Agent, and Oh My Pi (), specifically tailored for Indonesian developers and development workflows.
These tools work well together with awesome-copilot-id for enhanced workflows:
Looking for a awesome-copilot-id alternative? If you're comparing awesome-copilot-id with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
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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: