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CLI for AI-assisted repository and workspace workflows

Project description

augint-tools

Python 3.12+ PyPI Tests

CLI orchestration layer for AI-assisted repository workflows.

augint-tools provides a stable, machine-parseable command surface for humans and AI agents to coordinate development workflows. It is designed to be called directly by AI skills, replacing ad-hoc shell scripts with reliable, JSON-enabled commands.

Features

  • AI-first design: Every command supports --json output for agent parsing
  • Repo-type aware: Understands library and service repository patterns
  • Safe defaults: No destructive git operations without explicit commands
  • GitHub integration: Issue management, PR creation, CI status monitoring

Installation

pip install augint-tools

Or with uv:

uv tool install augint-tools

Quick Start

Single Repository Workflows

# Initialize repo metadata
ai-tools init --library

# Check repository status
ai-tools repo status --json

# Search/pick issues
ai-tools repo issues pick "bug"

# Create feature branch
ai-tools repo branch prepare --issue 42 --description "fix the thing"

# Run checks
ai-tools repo check run
ai-tools repo check run --preset full --fix

# Submit work (push + create PR)
ai-tools repo submit

Command Reference

Top-Level Commands

  • ai-tools init [--library|--service] - Initialize repository metadata

Repository Commands (ai-tools repo)

  • inspect - One-call repo snapshot (kind, branch, toolchain, command plan)
  • status - Show repository status (branch, dirty state, PRs, CI)
  • issues pick [query] - Issue recommendation and search
  • branch prepare - Create work branch from correct base
  • check plan - Resolve validation plan without running
  • check run - Execute validation plan
  • submit - Push branch and create PR with automerge
  • ci watch - Monitor CI run
  • ci triage - Classify CI failures

Development

Setup

uv sync --all-extras

Running Tests

uv run pytest                    # Run all tests
uv run pytest --cov             # With coverage
uv run pytest -k test_name      # Specific test

Code Quality

uv run ruff check src/ tests/   # Lint
uv run ruff format src/ tests/  # Format
uv run mypy src/               # Type check
uv run pre-commit run --all-files  # All hooks

Design Principles

  1. Human and AI first - Commands work well for both interactive use and programmatic calls
  2. JSON always available - Every orchestration command supports stable --json output
  3. Safe defaults - No destructive behavior without explicit confirmation
  4. Repo-type aware - Different defaults for libraries, services, and workspaces
  5. Skills call tools - AI skills orchestrate this CLI, not replace it with shell scripts

License

MIT

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