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TDD framework for AI workflows - define agent/skill/command standards in .drift.yaml and validate project structure programmatically

Project description

Drift

PyPI version Python 3.10+

Test-driven development for AI workflows - define your standards, validate your project, iterate to compliance.

What It Does

Drift validates your AI agent projects against custom rules you define. No built-in opinions - you write the rules in .drift.yaml, Drift validates against them.

Quick Example

1. Create .drift.yaml with your rules:

rule_definitions:
  claude_md_exists:
    description: "Project must have CLAUDE.md"
    scope: project_level
    phases:
      - name: check_file
        type: file_exists
        file_path: CLAUDE.md
        failure_message: "CLAUDE.md is missing"

2. Run validation:

uvx --from ai-drift drift

3. Fix issues, re-run until green.

That's it. Define standards → validate → fix → iterate.

Installation

Run directly with uv (no installation needed):

uvx --from ai-drift drift

Or install with uv:

uv pip install ai-drift

Common Workflow

# 1. Define your standards in .drift.yaml
# 2. Run validation
uvx --from ai-drift drift

# 3. Generate AI prompts to create missing files
uvx --from ai-drift drift draft --target-rule skill_validation > prompt.md

# 4. Fix issues manually or with AI
# 5. Re-validate until green
uvx --from ai-drift drift

What You Can Validate

Define rules to check:

  • Required files exist (CLAUDE.md, README.md, etc.)
  • Link integrity (no broken file references)
  • YAML frontmatter structure
  • Dependency health (no redundant transitive dependencies)
  • File format compliance (regex patterns)
  • Content quality (with optional LLM-based rules)

Examples: See .drift.yaml in this repo.

Documentation

Development

./test.sh          # Run tests (90%+ coverage required)
./lint.sh          # Run linters
./lint.sh --fix    # Auto-fix formatting

License

MIT

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