ai-factory

Executes QA test cases created by /aif-qa in human-guided or automated-agent mode, including reusable browser replay scripts. Use when you need to walk through QA one case at a time, record pass/fail results, or have an agent verify and rerun cases through browser, CLI, API, automated tests, or file/document checks.

Create knowledge references from URLs, documents, or files for use by AI agents. Fetch, process, and store structured references in the configured references directory (default: .ai-factory/references/).

Perform code review on staged changes or a pull request. Checks for bugs, security issues, performance problems, and best practices. Use when user says "review code", "check my code", "review PR", or "is this code okay". The +check flag validates findings via a fresh-context subagent; validation also runs automatically when the review produced confidence markers.

Create or update a project roadmap with major milestones. Generates the configured roadmap artifact (default .ai-factory/ROADMAP.md) — a strategic checklist of high-level goals. Use when user says "roadmap", "project plan", "milestones", or "what to build next".

Add project-specific rules and conventions to the configured RULES.md artifact. Each invocation appends new rules. These rules are automatically loaded by /aif-implement before execution. Use when user says "add rule", "remember this", "convention", or "always do X".

Run a standalone read-only rules compliance gate against changed files or a git ref. Use when you need a dedicated project-rules check without a full review or verify pass.

Security audit checklist based on OWASP Top 10 and best practices. Covers authentication, injection, XSS, CSRF, secrets management, and more. Use when reviewing security, before deploy, asking "is this secure", "security check", "vulnerability".

Generate professional Agent Skills for AI agents. Creates complete skill packages with SKILL.md, references, scripts, and templates. Use when creating new skills, generating custom slash commands, or building reusable AI capabilities. Validates against Agent Skills specification.

Transfer reusable prevention lessons from another AI Factory project into the current project's skill-context through /aif-evolve. Use when building a similar project and you want to avoid problems already captured in another project's patches without copying or revealing that project's identity, paths, or identifiers.

Verify completed implementation against the plan. Checks that all tasks were implemented, nothing was forgotten, code compiles, tests pass, and quality standards are met. Use after "/aif-implement" completes, or when user says "verify", "check work", "did we miss anything".

Load the essential AI Factory project context before work begins. Reads configured DESCRIPTION, ARCHITECTURE, ROADMAP, RESEARCH, RULES, and applicable AGENTS.md files, then returns a compact session handoff suitable for continuing or forking. Use at the start of a new session or after context loss.

Custom commit workflow that replaces the built-in aif-commit. Demonstrates the skill replacement feature of the extension system.

Review an AI Factory pull request on the VPS runner and post one evidence-based verdict comment, reconciling prior review findings on subsequent runs.