ad-pipeline-skeleton

Plan AI-generated advertising campaigns through four staged gates from brief to measurement.

Updated Sep 10, 2026
One-click install
npx skills add https://github.com/joydai2026-del/skills --skill ad-pipeline-skeleton-joydai2026-del
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ad-pipeline-skeleton
Source: https://github.com/joydai2026-del/skills/tree/main/ad-pipeline-skeleton
Command: npx skills add https://github.com/joydai2026-del/skills --skill ad-pipeline-skeleton-joydai2026-del

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? AI ad pipelines often generate images before the message is settled and place approvals after expensive renders, causing repeated rework. This Skill defines the stage order and human approval gates so nothing expensive happens before the cheap decision that governs it. ## Core Features & Use Cases - Four-act pipeline: DECIDE (brief, concepts), MAKE (copy, stills, video, QA), DISTRIBUTE (plan, launch, operate), LEARN (data plan, test, measure), with four gates placed before irreversible spend. - Fillable gate artifacts: Reference forms for concept approval, rights and claims clearance (two levels), launch, and post-launch review, plus an experiment plan and asset naming scheme. - Rework diagnosis: A seven-question audit to find why a pipeline keeps redoing the same work, usually a gate sitting after the commitment it was meant to protect. - Use Case: A team launching a generative-AI video campaign uses the brief template, clears rights before production, generates video from approved stills, and runs a holdout-based test before scaling spend. ## Quick Start Use the ad-pipeline-skeleton skill to plan my upcoming AI-generated ad campaign from brief through launch and measurement.

Frequently Asked Questions about ad-pipeline-skeleton

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I plan an AI-generated ad campaign end to end?▼

Follow four acts: DECIDE (write a one-page brief, then multiple genuinely different concepts), MAKE (copy first, then stills, then video), DISTRIBUTE (plan placements, launch, operate daily), and LEARN (data plan before launch, test on small budget, measure and feed back). Four human gates sit before irreversible spend.

What order should I create ad copy, images, and video in?▼

Settle each decision in the cheapest medium that can settle it: copy settles the message, stills settle the look, and video settles the timing. Generate video from the approved still and describe the movement rather than re-describing the picture, or the model reinvents the approved look.

When should rights and claims clearance happen in an ad pipeline?▼

Clear the concept before any production spend, since an unlicensed face, voice, song, or claim is a liability the moment it exists. Then clear the finished files again before publishing, because what got made is never exactly what was pitched.

How do I QA AI-generated ads before human review?▼

Run an automated pass against criteria written before seeing the output, and do not show the judge model the prompt, since a judge holding the prompt grades prompt-matching rather than ad quality. Cap retries based on the requirement's success rate, then change the method rather than re-rolling the seed.

Why does my ad pipeline keep redoing the same work?▼

Rework usually means a gate sits after the commitment it was meant to protect. Audit where issues are caught, what was already paid for, whether the prior gate produced a filled artifact, and whether a later stage re-described an approved ad instead of using it.

How do I measure whether an ad campaign actually worked?▼

Name a counterfactual: a holdout, geo split, or matched market tells you whether the campaign caused results, while a race between your own ads only tells you which ad won. Prefer lift measurement over last-click attribution, and measure the guardrail metric alongside the winning number.