ai-devkit

AI DevKit · Compare implementation with design and requirements docs to ensure alignment.

AI DevKit · Pre-push code review against design docs.

AI DevKit · Execute a feature plan task by task.

AI DevKit · Scaffold feature documentation from requirements through planning.

AI DevKit · Store reusable guidance in the knowledge memory service.

AI DevKit · Review feature design for completeness.

AI DevKit · Review feature requirements for completeness.

AI DevKit · Update planning docs to reflect implementation progress.

AI DevKit · Add tests for a new feature.

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 · Proactively orchestrate running AI agents — scan statuses, assess progress, send next instructions, and coordinate multi-agent workflows. Use when users ask to manage agents, orchestrate work across agents, or check on agent progress.

AI DevKit · Structured SDLC workflow with 8 phases — requirements, design review, planning, implementation, testing, and code review. Use when the user wants to build a feature end-to-end, or run any individual phase (new requirement, review requirements, review design, execute plan, update planning, check implementation, write tests, code review).

AI DevKit · Document a code entry point with structured analysis, dependency mapping, and saved knowledge docs. Use when users ask to document, understand, or map code for a module, file, folder, function, or API.

AI DevKit · Use the memory CLI as a durable knowledge layer. Search before non-trivial work, store verified reusable knowledge, update stale entries, and avoid saving transcripts, secrets, or one-off task progress.

AI DevKit · Review code, skills, and prompts for security vulnerabilities — OWASP Top 10, prompt injection, business logic flaws, and insecure defaults. Use when reviewing PRs, auditing modules, reviewing AI skills/prompts, or preparing for release.

AI DevKit · Analyze and simplify existing implementations to reduce complexity, improve maintainability, and enhance scalability. Use when users ask to simplify code, reduce complexity, refactor for readability, clean up implementations, improve maintainability, reduce technical debt, or make code easier to understand.

AI DevKit · Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).

AI DevKit · Test-driven development — write a failing test before writing production code. Use when implementing new functionality, adding behavior, or fixing bugs during active development.

AI DevKit · Review and improve documentation for novice users. Use when users ask to review docs, improve documentation, audit README files, evaluate API docs, review guides, or improve technical writing.

AI DevKit · Enforce evidence-based completion claims — require fresh command output before reporting success. Use when completing any task, fixing a bug, finishing a phase, running tests, building, deploying, or making any "it works" claim.

Make your agent plan before code, verify work, and review changes with a repeatable engineering workflow.