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Marketing deploy

Skill dasein108/slope-studio/.agent-instructions/skills/marketing-deploy

"Automated AI short-video studio: idea → published YouTube Short, $0.06/video"

Install
npx -y skills add dasein108/slope-studio --skill marketing-deploy

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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).

SKILL.md

2.7 KB, as published. Nobody here has run it

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

  1. 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.
  2. Produce + publish via the film-maker skill (it owns the pipeline): 
    studio estimate <run_id>            # if iterating an existing run, price stage 3 first
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.
  3. 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.
  4. 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.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.