newliver666/apk-reverse

Suitable for Android APK reverse engineering analysis

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newliver666/apk-reverse has 4.5k+ stars and 1k+ forks on GitHub, is written in Python, and was last pushed on Oct 16, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 44 minutes ago
Added to GitGenius on September 29th, 2026
Created on September 19th, 2026
Open Issues & Pull Requests: 2 (+0)
GitHub issues: Enabled
Number of forks: 1,002
Total Stargazers: 4,971 (+23)
Total Subscribers: 20 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 0.7 hours
Mean response time: 0.7 hours
90th percentile: 0.7 hours
Tracked items: 1

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Open issues: 0
New in 7 days: 0
Closed in 7 days: 0
Avg open age: N/A days
Stale 30+ days: 0
Stale 90+ days: 0

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Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

apk-reverse is a tool for Android APK reverse engineering, debloating, ad removal, surgical dex patching, repacking, and runtime or server analysis.

The tool addresses the challenge of analyzing and modifying Android applications by providing structured workflows for APK manipulation. It operates as an agent skill designed to be consumed by AI assistants like Claude Code or Codex, organizing its guidance through progressive disclosure: a decision-oriented procedure document, detailed references loaded on demand, and parameterized scripts ready for direct execution. The approach emphasizes gated workflows with explicit pass criteria, symptom-indexed failure catalogs, and override rules to prevent reasoning past known problems.

The tool suits developers and security researchers who need to analyze, modify, or remove components from Android applications. It is particularly valuable for those working with AI-assisted workflows, as it structures knowledge specifically for agent consumption rather than human tutorial reading. The project acknowledges that its failure mode is not ignorance but models reasoning from first principles despite available guidance, so it implements procedural gates, symptom indices as stop signals, and a two-strike rule to prevent repeated attempts at the same failed hypothesis.

Development activity shows active maintenance with continuous integration workflows in place. The project maintains both English and Chinese documentation, indicating sustained effort toward accessibility. The codebase is written in Python and targets Python 3.9 and later, with explicit scope documentation and a failure catalogue that reflects accumulated operational experience.