loli_profiler: Memory instrumentation tool with CI and AI support for android app&game developers

by Tencent · Claude Skill · ★ 696

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

Security audit verdict: SAFE · quality 58/100

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. 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.

androidclaude-codecppmemoryprofilerqt5unity3dunreal-engine-4

Quick Facts

Stars696
Forks93
LanguageC++
CategoryClaude Skill
Quality Score58/100
Open Issues1
Last Updated2026-09-30
Created2021-03-04
Platformsclaude-code
Est. Tokens~17k

About loli_profiler

Overview LoliProfiler is a C/C++ memory profiling tool for Android games and applications. LoliProfiler supports profiling debuggable applications out of box. And offers engine specific (UnrealEngine4/Unity) modifications to enable profiling complex or production level games. Features Profiler client supports Windows 10/7 and Mac OSX Mojave and newer. Work with debuggable applications out of box.

Compatible Skills

These tools work well together with loli_profiler for enhanced workflows:

  • UnrealClaude — semantic(0.38)+complementary+rare_topics+same_lang+similar_pop+shared_platform (63%)
  • unreal-mcp — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)
  • UnrealGenAISupport — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)
  • claude-android-skill — semantic(0.38)+complementary+rare_topics+similar_pop+shared_platform (53%)
  • Unreal_mcp — semantic(0.33)+complementary+same_lang+similar_pop (51%)

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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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