Creative Audio Workflow Lab

Clean-room music/audio workflow utilities for synthetic track metadata, release planning, content repurposing, and AI-assisted creative operations.

This repo is designed as public proof-of-work for an AI workflow builder and creative automation operator profile. It shows how music release operations can be turned into tested, auditable, human-reviewed workflow components without exposing private assets.

Safety boundary

This is a clean-room demo using synthetic fixtures. It does not include credentials, private client data, production release strategy, stems, masters, paid loops, copyrighted audio files, unreleased assets, or live execution paths.

What it does

• Normalizes synthetic track metadata
• Validates release-prep gaps such as missing cover art or descriptions
• Builds a deterministic release checklist
• Generates channel-specific repurposing prompts
• Renders markdown reports suitable for human review
• Runs tests and CI without secrets or external services

Example

PYTHONPATH=src python -m audio_workflow_lab.demo \
  --input examples/synthetic_tracks.json \
  --output-dir reports/generated

Run tests:

PYTHONPATH=src pytest -q

Example report excerpt:

## Validation
- `cover_art_missing`
- `metadata_description_missing`

Generated reports include:

• reports/generated/midnight-sketch_report.md
• reports/generated/sunset-bounce_report.md

Project layout

examples/                 Synthetic track metadata only
src/audio_workflow_lab/    Workflow utilities
tests/                    Unit tests
docs/                     Safety and workflow notes
reports/generated/        Sample generated reports

Recruiter-facing angle

This repo demonstrates practical AI-adjacent workflow design: structured inputs, validation, deterministic planning, prompt-safe repurposing, markdown reporting, tests, and explicit safety boundaries.

It is intentionally small, readable, and auditable.