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Android code review

Skill adrigm06/Android-Engineering-Skill/skills/android-code-review

A modular, composable, production-grade Android skill system for agentic engineering workflows, designed to behave like a senior Android Staff Engineer under real-world constraints.

Install
npx -y skills add adrigm06/Android-Engineering-Skill --skill android-code-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Structured Android code review skill with severity-based findings across architecture, correctness, maintainability, performance, and security. Use this for PR reviews and technical debt assessments.

SKILL.md

4.8 KB, as published. Nobody here has run it

Purpose

Run disciplined Android code reviews that prioritize issues by incident risk and provide implementation-ready fixes.

Scope and authority

This skill is synthesis authority across domains. It consolidates architecture, security, performance, testing, and release concerns into a prioritized outcome.

When not to use

  • when the primary task is deep root-cause investigation (android-debugging)
  • when the primary goal is new architecture design selection (android-architecture)

When to use

  • pull request reviews
  • technical debt audits
  • release-candidate quality checks

Decision engine workflow

  1. Establish change intent and blast radius.
  2. Check non-negotiable constraints first.
  3. Evaluate architecture, correctness, maintainability, performance, and security implications.
  4. Rank findings by severity and operational impact.
  5. Propose fix order and verification strategy.

Branching decision tree

Branch A: change risk profile

  • high blast radius (cross-module, auth, payment, startup, release-critical):
    • perform deep multi-domain review with stricter evidence requirements
  • medium blast radius:
    • prioritize correctness, maintainability, and boundary consistency
  • low blast radius:
    • focus on local quality and regression prevention

Branch B: release proximity

  • near release:
    • prioritize regressions and production-risk findings first
  • normal window:
    • include strategic maintainability improvements with staged follow-up

Branch C: change category

  • security-sensitive:
    • require stronger evidence and verification for exploitability/reachability concerns
  • performance-sensitive:
    • require measurement-backed findings for hotspot regressions
  • boundary-sensitive:
    • prioritize dependency direction and ownership violations

Branch D: evidence availability

  • full diff/artifacts available:
    • run normal severity-based review flow
  • partial artifacts only:
    • scope findings to verified surfaces, mark unknowns explicitly
  • no diff/artifacts provided:
    • do not approve/reject code quality conclusively
    • return a provisional risk review with required evidence checklist
    • provide a low-risk fallback path (narrow merge scope or hold)

Quantitative gates

Use measurable evidence gates for high-impact findings:

  • evidence sufficiency gate:
    • pass when finding has concrete code evidence and reproducible risk path
    • at-risk when evidence is partial but plausible
    • fail when claim is mostly speculative
  • verification readiness gate:
    • pass when recommended fix includes clear verification steps
    • at-risk when verification is incomplete
    • fail when no verification plan exists

Uncertainty protocol

Always include confidence on non-trivial findings. For medium/low confidence findings, include the minimum evidence needed to confirm/refute.

Confidence bands:

  • High (>= 0.80)
  • Medium (0.60-0.79)
  • Low (< 0.60)

If confidence is medium/low:

  • list assumptions explicitly
  • request minimum additional evidence that can re-rank severity
  • provide at least one fallback recommendation with lower regression risk
  • escalate to specialized skill when domain certainty is insufficient

Output contract

Follow global order from ../../AGENTS.md:

  1. Context and constraints
  2. Decision and rationale
  3. Alternatives considered
  4. Tradeoffs
  5. Risks and mitigations
  6. Confidence and unknowns
  7. Cross-skill impacts
  8. Next implementation steps

Then include review-specific artifacts:

  • Findings by severity
  • Evidence
  • Risk impact
  • Recommended fix
  • Follow-up checks

No-diff fallback template (required when artifacts are missing)

When PR diff or runtime artifacts are missing, add:

  • Review status: Provisional - insufficient evidence
  • Blocking unknowns: exact missing artifacts (diff, module list, benchmarks, security config deltas)
  • Provisional findings: only evidence-backed risks, each marked confirmed or hypothesis
  • Minimum evidence to finalize: smallest artifact set needed for final severity ranking
  • Fallback path: low-risk option (scope split, hold merge, or merge non-runtime subset only)

Cross-skill handoff payload

Use the standard payload defined in ../../AGENTS.md (section: Cross-skill handoff contract). Set requesting_skill to android-code-review.

Anti-pattern detection

  • hidden coupling or boundary violations
  • missing tests on behavior-critical changes
  • performance-sensitive changes with no measurements
  • security-impacting changes with weak controls

Related resources

  • references/review-severity-evidence.md
  • templates/review-report.md

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.