realkimbarrett/avatar-extraction
VerifiedSkill library that encodes direct-response ad strategy for AI agents.
What is realkimbarrett/avatar-extraction?
The library organizes advertising expertise into focused, reusable skills covering paid acquisition on Meta, TikTok, and YouTube, offer design, creative testing, and campaign diagnosis. Each skill specifies its purpose, required inputs, reasoning steps, and expected outputs so agents follow proven structures rather than guessing.
Skills are grouped into Foundations, Copy Chief, Operator OS, QA, and Orchestrators. They combine audience psychology with execution tactics, letting agents first map awareness levels and mechanisms before producing headlines, creatives, or funnel paths.
Media buyers, founders running paid ads, and agencies seeking repeatable results will find it useful. It targets direct-response environments with real budgets and conversion pressure rather than broad content marketing.
What you can build with realkimbarrett/avatar-extraction
Booked-call campaign creation
Chain avatar extraction, offer extraction, awareness mapping, and angle generation to produce positioning, ad creative, and conversion paths that move cold traffic toward scheduled calls.
Campaign performance review
Apply diagnosis and QA skills to existing ads and funnels to identify weak angles, generic language, or conversion leaks and receive prioritized fixes.
Offer and positioning refinement
Use extraction and mechanism-building skills on customer language and market data to sharpen offers and differentiate from competitors before creative production.
Install realkimbarrett/avatar-extraction
- 1Clone or download the repository and place the skills folder inside .agents/skills/
- 2Confirm your agent framework supports the Agent Skills pattern (Claude Code, Cursor, or custom setups).
- 3Reference individual SKILL.md files to understand inputs and outputs for each skill.
- 4Start a workflow by calling the first skill such as avatar-extraction and pass results to the next skill in sequence.
- 5Review generated examples in the repo to see complete campaign outputs before running on live data.
realkimbarrett/avatar-extraction: pros & cons
Pros
- +Combines copywriting depth with practical ad operations in one system
- +Skills are modular and explicitly designed to chain into full campaigns
- +Opinionated approach prioritizes conversion over generic polished text
- +Covers the full loop from research through testing and diagnosis
Cons
- –Requires an agent runtime that supports the specific skill format
- –Strongly focused on direct-response paid ads; less relevant for brand or organic work
- –Users must learn the intended skill order rather than receiving fully automated flows
Frequently asked questions
It shifts focus from writing prompts to loading structured skills that already encode expert reasoning steps.
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