Architectural Invariant Design: Capacitor 7 Android 16KB Page-Alignment Runtime Invariant
Capacitor 7 Android 16KB Page-Alignment Runtime Invariant defines an industrial-grade behavioral standard for mobile engineering. Enforces that all bundled native NDK dependencies, custom C/C++ plugins, and third-party shared libraries (.so) in Capacitor 7 projects strictly comply with Android 15 (API level 35+) 16KB memory page alignment. Non-compliant 4KB-aligned ELF segments fail dynamic linker verification on 16KB kernels, leading to immediate SIGSEGV crashes at process startup. Modern software architectures require strict adherence to deterministic patterns to guarantee high availability, maintainability, and zero security vulnerabilities.
Hazard Analysis: What Breaks Without This Rule
The following failure modes have been cataloged from hundreds of hours of production code reviews where AI assistants produced non-compliant implementations:
| Failure Mode | AI Symptom (The Anti-Pattern) | Severity | Production Impact & Remedy |
|---|---|---|---|
| AI Hallucination & Deprecation Drift | Shipping unaligned 4KB ELF dynamic libraries or unverified native NDK binaries that trigger SIGSEGV dynamic linker crashes on Android 15+ devices. | Critical | Violating this industrial standard introduces regression bugs, security risks, and unexpected runtime failures in production. Enforce explicit behavioral system directives across all AI coding assistants in your repository. |
| Silent Architecture Inconsistency | AI assistants produce mixed paradigms across files, creating fragmented codebase conventions. | Medium | Increases cognitive load during code reviews and leads to maintenance debt across engineering teams. Pin project conventions in .cursor/rules, CLAUDE.md, and AGENTS.md so all team agents follow identical guardrails. |
Step-by-Step Production Directives
Each rule constraint below is injected into the AI agent's system prompt to establish deterministic boundaries:
Engineers MUST ALWAYS compile native C/C++ code using NDK r27 or newer with linker flags `-Wl,-z,max-page-size=16384`.
Engineers MUST ALWAYS compile native C/C++ code using NDK r27 or newer with linker flags `-Wl,-z,max-page-size=16384`.
Engineers MUST NEVER package unverified legacy third-party `.so` binaries without running dynamic linker alignment audits via readelf or llvm-objdump.
Engineers MUST NEVER package unverified legacy third-party `.so` binaries without running dynamic linker alignment audits via readelf or llvm-objdump.
Build scripts SHOULD execute automated pre-bundle validation checks checking `LOAD` segment alignment boundaries across all `.so` files in `android/app/build/intermediates/merged_native_libs`.
Build scripts SHOULD execute automated pre-bundle validation checks checking `LOAD` segment alignment boundaries across all `.so` files in `android/app/build/intermediates/merged_native_libs`.
Gradle build files MUST configure packagingOptions to avoid unaligned zip compression of native libraries when targeting Android 15+.
Gradle build files MUST configure packagingOptions to avoid unaligned zip compression of native libraries when targeting Android 15+.
CI pipelines MUST fail builds immediately if any ELF shared library contains a loadable segment with alignment less than 0x4000 (16384 bytes).
CI pipelines MUST fail builds immediately if any ELF shared library contains a loadable segment with alignment less than 0x4000 (16384 bytes).
Frequently Asked Technical Questions
Common questions developers face when configuring AI coding assistants for this stack:
Q:Why is Capacitor 7 Android 16KB Page-Alignment Runtime Invariant critical for AI coding assistants?
Enforces that all bundled native NDK dependencies, custom C/C++ plugins, and third-party shared libraries (.so) in Capacitor 7 projects strictly comply with Android 15 (API level 35+) 16KB memory page alignment. Non-compliant 4KB-aligned ELF segments fail dynamic linker verification on 16KB kernels, leading to immediate SIGSEGV crashes at process startup. Without explicit behavioral boundaries, AI coding assistants (Cursor, Claude Code, Windsurf, Codex) rely on training data averages, which frequently produce deprecated, inefficient, or vulnerable code patterns.
Q:How does adopting this rule prevent AI hallucinations?
This rule explicitly declares what the AI must avoid (Shipping unaligned 4KB ELF dynamic libraries or unverified native NDK binaries that trigger SIGSEGV dynamic linker crashes on Android 15+ devices.) and prescribes verified positive examples. Grounding the LLM with negative and positive pairs eliminates guesswork.
Q:Which AI coding environments are supported?
This specification is multi-platform compatible: Cursor (.cursor/rules/*.mdc & .cursorrules), Anthropic Claude Code (CLAUDE.md), Windsurf (.windsurfrules), Codex / Cline (AGENTS.md), and GitHub Copilot (.github/copilot-instructions.md).
Q:Can I combine this rule with other technology stacks?
Yes. Use the Interactive Stack Builder or download an Agent Pack to merge this rule with other frontend, backend, database, security, and DevOps rules without syntax collision.