| name | marketing-deploy |
| description | Use to produce + publish a chosen backlog bet and bind it to the journal so it can be measured later. Calls the film-maker skill to render+upload, sized to the per-video budget, then links the run. One lego-block of the growth loop. Fed by the backlog pick (marketing-guru); precedes marketing-measure-learn (after a maturation wait).
|
marketing-deploy — produce, publish, link
Turn one chosen bet (entry_id + idea, from the backlog pick in marketing-guru) into a
published Short bound to its journal entry.
Do this
- Get the spend cap from the channel budget (set once via
studio marketing budget --channel <name> --per-video 0.60 or --per-minute 0.40):
CAP=$(studio marketing budget --channel <name> --for-duration <duration_s>)
--for-duration returns the per-video --max-cost (flat for per-video budgets; rate × length
for per-minute). If it prints (budget unset), set the budget first or pass --max-cost by hand.
- Produce + publish via the film-maker skill (it owns the pipeline):
studio estimate <run_id>
studio run "<idea>" --duration 60 --tier <cheap|balanced> --max-cost $CAP \
--publish-to youtube --privacy public --channel <name>
--tier cheap ≈ stills + free motion; balanced spends --max-cost on AI clips for hero
scenes. --max-cost is the whole-video cap (images + clips + music): run reserves the
music bed and auto-downgrades paid fal music to synth if it won't fit, so total spend stays ≤ cap.
Stage 3 aborts pre-flight if the clip estimate exceeds what's left. Cheapest "still alive"
recipe ≈ $0.41 (free motion-* + one ≤6s ltx hook + free local music); see
docs/10-architecture/cost-model.md for the ladder.
- Link the run to the bet (so measure can find the video):
studio marketing link <entry_id> <run_id> --channel <name>
Pulls the YouTube id from runs/<run_id>/07_publish.json; sets status: deployed.
- Wait before measuring — give the Short 48–72h+ to accrue watch time.
Notes
link also captures production telemetry — cost, duration, animators/fx/model, and
per-stage providers — from the run manifest into the bet (T3), so learn can attribute
success to the effects used and you can track spend per bet.- Repeat backlog→deploy until ~10 videos are live to exit cold-start.
Writes run_id, video_id, video_url, status: deployed, plus telemetry
(cost_usd, duration_s, tier, video_model, animators, effects, providers,
n_scenes) onto the entry. Memory model: docs/50-marketing/memory.md.
