ai-toolkit

Create and iterate on HTML UI mockups with parallel design exploration

Create Architecture Decision Records through interactive conversation

Get implementation guidance for a phase without automated code generation - user implements manually

Unified branch operations with git-workflow enforcement

Check if CHANGELOG is up to date and update as necessary

Create a proper git commit with quality checks and conventional message

Complete any issue type per its workflow requirements

Unified documentation management - generate, validate, sync, update, and analyze documentation

Execute specific implementation phases from task plans with test-first enforcement

Create standalone work items (TASK, BUG, or SPIKE) with AI-assisted type detection

Add AI-suggested comments to external Jira issues based on work context

Create Jira epics through natural language conversation (requires Jira integration)

Import Jira issue for local work with PLAN.md creation

Promote local exploration issue to Jira for team visibility

Create PLAN.md file with phase-based breakdown for tasks, bugs, and spikes

Fill and improve project brief through gap-driven conversation

Enhanced project status dashboard with intelligent context analysis

Comprehensive quality assessment with multi-agent coordination

Silently refresh AI context by reading project configuration, guidelines, and recent commits

Release new version following semantic versioning guidelines

Deep research on a topic, creating persistent documentation for future reference

Step back, reflect on current work, validate direction, and assess alignment with plan and architecture

Create local feature specifications through natural language conversation

Sync project state after manual changes via git diff analysis

Initialize project with ai-toolkit structure - minimal questions, fast setup

5-step debugging loop for unexpected issues during implementation or standalone bug fixes

Add timestamped work log entries to track manual changes and communicate with AI