agentsclimarketplace

Marketing deploy

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

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).From its SKILL.md

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.

2 things 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.
  • runs commandsInstructs the agent to run 6 commands, including `studio marketing budget --channel <name> --per-video 0.60` and 5 more.

SKILL.md

2.7 KB, 650 tokens by cl100k_base, 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.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. 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.