The AI-native design pipeline — concept to shipped MP4 in one workflow (step 8/8) · programmatic design

Checkpoint

One last thing before we move on. pass this to mark the lesson done, or skip and keep moving.

Final design drill. You're consulting for five teams. Audit each one's pipeline against the canonical seven steps.

Write audit_pipelines(teams) that takes a list of team dicts and returns a dict mapping each team's name to a verdict dict shaped like:

{"score": int, "missing": list[str]}

Each team dict has a name and a steps list (same shape as the previous drill — each step has step and tool fields).

Use the same scoring rule: 15 points per UNIQUE canonical step covered (step name in canonical list AND non-empty tool), capped at 100.
missing is a list of canonical step names that were NOT covered. Preserve canonical order: ["concept", "reference-gen", "composition", "audio", "captions", "render", "review"].
Five teams run. Expected output:

acme:        score=100 missing=[]
bravo:       score=90  missing=['captions']
charlie:     score=75  missing=['audio', 'captions']
delta:       score=60  missing=['reference-gen', 'audio', 'captions']
echo:        score=45  missing=['reference-gen', 'audio', 'captions', 'review']

Checkpoint

One last thing before we move on. pass this to mark the lesson done, or skip and keep moving.

Final design drill. You're consulting for five teams. Audit each one's pipeline against the canonical seven steps.

Write audit_pipelines(teams) that takes a list of team dicts and returns a dict mapping each team's name to a verdict dict shaped like:

{"score": int, "missing": list[str]}

Each team dict has a name and a steps list (same shape as the previous drill — each step has step and tool fields).

Use the same scoring rule: 15 points per UNIQUE canonical step covered (step name in canonical list AND non-empty tool), capped at 100.
missing is a list of canonical step names that were NOT covered. Preserve canonical order: ["concept", "reference-gen", "composition", "audio", "captions", "render", "review"].
Five teams run. Expected output:

acme:        score=100 missing=[]
bravo:       score=90  missing=['captions']
charlie:     score=75  missing=['audio', 'captions']
delta:       score=60  missing=['reference-gen', 'audio', 'captions']
echo:        score=45  missing=['reference-gen', 'audio', 'captions', 'review']

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The AI-native design pipeline — concept to shipped MP4 in one workflow — step 8 of 8