debroid: Autonomous, headless Android debugger designed for AI coding agents

by PatilShreyas · Codex Skill · ★ 276

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

Security audit verdict: SAFE · quality 66/100

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.

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.

agentic-aiandroidantigravityclaude-codeclicodexdebuggerdebuggingdebugging-tooldeveloper-tools

Quick Facts

Stars276
Forks14
LanguageKotlin
CategoryCodex Skill
LicenseApache-2.0
Quality Score66/100
Open Issues7
Last Updated2026-10-26
Created2026-08-02
Platformsclaude-code, cli, codex
Est. Tokens~18k

About debroid

Debroid 🤖⚡ The Headless Android Debugger for AI Agents Debroid (DEBug + AnDROID) is a headless CLI that speaks the Java Debug Wire protocol (JDWP) so AI agents — Claude Code, Grok Build, Codex, OpenCode, Cursor, Antigravity, or anything with a terminal — can debug a live Android app the way a human would in Android Studio: set breakpoints, catch exceptions, step through code, inspect and mutate...

Compatible Skills

These tools work well together with debroid for enhanced workflows:

  • adbfriend — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (58%)
  • KMPilot — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)

debroid alternative? Top 6 similar tools

Looking for a debroid alternative? If you're comparing debroid 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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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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