check-implementation
The check-implementation command validates that code changes align with design documents in `docs/ai/design/` and requirements in `docs/ai/requirements/`. Use this when reviewing modified files against architectural decisions to identify deviations, missing components, logic gaps, security issues, and test coverage gaps, with guidance on whether to revise design, refactor code, or proceed to testing.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/codeaholicguy/ai-devkit/HEAD/commands/check-implementation.md -o ~/.claude/commands/check-implementation.mdcheck-implementation.md
Compare the current implementation with the design in `docs/ai/design/` and requirements in `docs/ai/requirements/`. 1. If not already provided, ask for: feature/branch description, list of modified files, relevant design doc(s), and any known constraints or assumptions. 2. **Use Memory for Context** — Search memory for known constraints and prior decisions before assessing mismatches: `npx ai-devkit@latest memory search --query "<feature implementation alignment>"`. 3. For each design doc: summarize key architectural decisions and constraints, highlight components, interfaces, and data flows that must be respected. 4. File-by-file comparison: confirm implementation matches design intent, note deviations or missing pieces, flag logic gaps, edge cases, or security issues, suggest simplifications or refactors, and identify missing tests or documentation updates. 5. **Store Reusable Knowledge** — Save recurring alignment lessons/patterns with `npx ai-devkit@latest memory store ...`. 6. Summarize findings with recommended next steps. 7. **Next Command Guidance** — If major design issues are found, go back to `/review-design` or `/execute-plan`; if aligned, continue to `/writing-test`.
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 · 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.
AI DevKit · Implementation phase guidance for executing feature plans and checking implementation against design. Use when the user wants to implement planned tasks, update implementation docs, verify code matches design, or run dev-lifecycle phases 5 and 7.