name: layer-workflows description: Use when creating or modifying code in nomarr/workflows/. Workflows implement use cases, accept dependencies as parameters, orchestrate components, and return DTOs.

Workflows Layer

Purpose: Implement core use cases ("what Nomarr does").

Workflows contain the story of how Nomarr performs operations. They are recipes that:

1. Accept dependencies as parameters (DB, config, ML backends)
2. Orchestrate components to perform work
3. Return DTOs

Allowed Imports

# ✅ Allowed
from nomarr.persistence import Database
from nomarr.components.ml import compute_embeddings, run_inference
from nomarr.components.tagging import predictions_to_tags
from nomarr.helpers.dto import ProcessFileResult

Forbidden Imports

# ❌ NEVER import these in workflows
from nomarr.services import ProcessingService  # Workflows don't call services
from nomarr.interfaces import ...              # No interface imports
from pydantic import BaseModel                 # No Pydantic

File & Function Naming

• File: verb_object_wf.py (e.g., scan_library_direct_wf.py, process_file_wf.py)
• Function: verb_object_workflow(...) as the primary entrypoint
• One public workflow per file
• Everything else is _private_helper

Complexity Rule: Clear Sequences

Workflows should read like a recipe:

def process_file_workflow(
    db: Database,
    file_path: str,
    models_dir: str,
    namespace: str,
) -> ProcessFileResult:
    # Step 1: Load file
    file_record = load_file_from_db(db, file_path)
    
    # Step 2: Compute embeddings
    embeddings = compute_all_embeddings(file_path, models_dir)
    
    # Step 3: Run inference
    predictions = run_inference_for_heads(embeddings, models_dir)
    
    # Step 4: Convert to tags
    tags = predictions_to_tags(predictions, namespace)
    
    # Step 5: Write tags
    write_tags_to_db(db, file_record.id, tags)
    
    return ProcessFileResult(file=file_path, tags_written=len(tags))

Judge by clarity, not line count. Many component calls are fine if they form a clear sequence.

Accept All Dependencies as Parameters

Workflows receive everything via parameters—no global config reading:

# ✅ Good - dependencies injected
def process_file_workflow(
    db: Database,
    file_path: str,
    models_dir: str,
) -> ProcessFileResult:
    ...

# ❌ Bad - reading globals
def process_file_workflow(file_path: str) -> ProcessFileResult:
    from nomarr.config import db, models_dir  # NO GLOBALS
    ...

When to Extract

Extract to a component if:

• Workflow is doing non-trivial computation itself
• Complex branching logic embedded in workflow
• Heavy domain math/ML/statistics

Split into private helpers if:

• Workflow becomes hard to read
• Large, reusable sub-sequences

# Before - hard to read
def complex_workflow(db: Database, ...) -> Result:
    # 50 lines of discovery
    # 50 lines of validation
    # 50 lines of processing

# After - split with private helpers
def complex_workflow(db: Database, ...) -> Result:
    discovered = _discover_and_validate(db, ...)
    processed = _process_files(db, discovered, ...)
    return Result(...)

Size Guidelines

• Soft limit: ~300–400 LOC per workflow module
• If multiple exported workflows with different user stories → split files
• Exception: analytics-style modules can group related read-only workflows

Validation Checklist

Before committing workflow code, verify:

• Does this file import from services or interfaces? → Violation
• Does this file import Pydantic? → Violation
• Does this workflow read global config? → Accept as parameter instead
• Is the workflow doing heavy computation? → Extract to component
• Does the function return a DTO? → Required
• Is there one public workflow per file? → Required
• Does the file name end in _wf.py? → Convention

Layer Scripts

This skill includes validation scripts in .github/skills/layer-workflows/scripts/:

lint.py

Runs all linters on the workflows layer:

python .github/skills/layer-workflows/scripts/lint.py

Executes: ruff, mypy, vulture, bandit, radon, lint-imports

check_naming.py

Validates workflows naming conventions:

python .github/skills/layer-workflows/scripts/check_naming.py

Checks:

• Files must end in _wf.py
• Each file must have exactly one *_workflow function
• Internal helpers must be _private