marketing-deploy-100fbb — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited marketing-deploy-100fbb (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Turn one chosen bet (entry_id + idea, from the backlog pick in marketing-guru) into a published Short bound to its journal entry.
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.
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.
studio marketing link <entry_id> <run_id> --channel <name>Pulls the YouTube id from runs/<run_id>/07_publish.json; sets status: deployed.
link also captures production telemetry — cost, duration, animators/fx/model, andper-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.
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.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.