dev-review
The dev-review skill conducts a comprehensive pre-push code review for Phase 9 of the AI DevKit development lifecycle. It performs holistic checks including design alignment verification, dependency analysis, breaking change detection, and cross-cutting concern review, then delivers findings ordered by severity with file and line references before determining readiness for PR creation.
git clone --depth 1 https://github.com/codeaholicguy/ai-devkit /tmp/dev-review && cp -r /tmp/dev-review/skills/dev-review ~/.claude/skills/dev-reviewSKILL.md
# Dev Review Run final pre-push review for configured AI docs features. Before changing docs or code, propose the concrete review plan and wait for user approval unless the user already approved the exact phase plan. ## Phase Contract 1. Run `npx ai-devkit@latest lint` before phase work. 2. If working on a named feature, run `npx ai-devkit@latest lint --feature <name>`. 3. Check `git status -sb` and `git diff --stat`. 4. Read feature docs and relevant changed files before findings. 5. Apply the `verify` skill before claiming readiness. 6. If parent `dev-lifecycle` established usable task tracing, emit review phase, progress, blocker/finding, next-step, and final evidence/readiness events per `task`. ## Code Review Use for Phase 9. Take a holistic review stance: findings first, ordered by severity, grounded in file/line references. 1. Gather context: feature description, modified files, design docs, risky areas, tests already run. 2. Verify design alignment by summarizing architectural intent and checking implementation matches. 3. For each modified file, grep exported names to trace callers and dependents. Read relevant signatures, call sites, and type definitions. 4. Check consistency against 1-2 similar modules. 5. Search for existing utilities the new code could reuse or now duplicates. Flag near-matches honestly; do not force a wrong abstraction. 6. Verify contract integrity at API, type, config, and schema boundaries. 7. Check boundary discipline: external data is validated at edges, internal code is not littered with redundant guards, and transport/storage/framework types do not leak through domain APIs. 8. Check reader load: needless layers, pass-through methods, broad shallow interfaces, mutable state scope, and unclear value ownership. 9. Check domain fit: branch growth, synchronized flags, repeated shape assumptions, temporal decomposition, and missing state models. 10. Check dependency health, including circular dependencies or version conflicts from new imports. 11. Check breaking changes. For public/external APIs, recommend parallel change and deprecation over in-place mutation. For in-repo-only callers, all callers should be migrated and legacy APIs deleted. 12. Check rollback safety, especially irreversible migrations or one-way data/state changes. 13. Review file by file for correctness, logic, edge cases, redundancy, security, performance, error handling, and test coverage. 14. Check cross-cutting concerns: naming conventions, documentation updates, missing tests, config/migration changes. 15. Summarize blocking issues, important follow-ups, and nice-to-haves. Per finding include file, issue, impact severity, and recommendation. 16. If task tracing is available, add blockers and set `blocked` for blocking findings; if review passes with final evidence, close the task per `task`. 17. Complete final checklist: design match, no logic gaps, security addressed, integration points verified, tests cover changes, docs updated. Done: if the checklist passes, the feature is ready to push and create a PR. If blocking issues remain, return to `dev-implementation` or `dev-testing`.
AI DevKit · Exchange information with active Codex, Claude Code, and other AI agents using ai-devkit agent list, detail, and send. Use when an agent needs to find another active agent, read its recent context, send it information, or request information back.
AI DevKit · Manage running AI agents with ai-devkit agent commands. Use when an agent needs to identify itself, list agents, start workers, inspect agent detail, assign work, group agents, resume sessions, stop agents, or delegate work to other agents.
AI DevKit · Supervise multi-agent workflows over repeated passes: poll progress, unblock waiting agents, coordinate dependencies, relay outputs, resolve conflicts, and verify completion. Use only for ongoing multi-agent coordination, not one-off list/detail/send/start/kill actions.
AI DevKit · Use when the user asks to brainstorm, ideate, generate ideas, expand options, challenge ideas, pressure-test ideas, compare concepts, narrow choices, name something, plan content angles, explore strategy, evaluate product ideas, technical approaches, experiments, or decisions.
AI DevKit · Compare implementation with design and requirements docs to ensure alignment.
AI DevKit · Pre-push code review against design docs.
AI DevKit · Safe git commit workflow for AI coding agents. Use when the user asks to commit, prepare a commit, stage changes, create a PR-ready checkpoint, or finish work with a conventional commit while avoiding unrelated user changes.
AI DevKit · Design phase guidance for reviewing feature design against requirements. Use when the user wants to validate architecture, review design docs, resolve design trade-offs, or run dev-lifecycle phase 3.