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Architecture review

Skill nledford/engineering-review-board/skills/architecture-review

A collection of AI agent skills I have written

Install
npx -y skills add nledford/engineering-review-board --skill architecture-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

  • 0 stars0 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

Review architectural boundaries, dependency direction, ports and adapters, Clean/Onion/Hexagonal/DDD hybrids, and modular monolith structure. Use for architecture-focused audits of existing systems or changes; do not use for ordinary implementation or architecture design without a review request.

SKILL.md

1.6 KB, as published. Nobody here has run it

Architecture Review Skill

Use this skill as an architecture-specific review lens for an existing system, proposal, or diff. Always load review-verification-protocol before reporting findings. For repository change reviews, also load code-review.

Do not use it for simple code changes with no boundary impact. Use the matching Clean, Hexagonal, Onion, or DDD skill when the primary task is designing or implementing an architecture rather than reviewing one.

Workflow

  1. Read project-local guidance, architecture docs, module structure, and the relevant diff before inferring the intended architecture.
  2. Map dependency direction, module and bounded-context boundaries, ports, adapters, and framework or persistence edges.
  3. Identify concrete leakage, cycles, misplaced policy, and boundary bypasses.
  4. Distinguish intentional hybrid choices from accidental drift; prefer coherent, testable structure over pattern purity.
  5. Verify findings against imports, call paths, tests, or build rules. Record skipped checks and residual architectural risk.

Output

Return findings ordered by severity with file-level evidence, impact, the smallest practical correction, skipped validation, and residual risk.

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.