My 11-Skill AI Content Team (Built in Claude Code)

What It Covers

A marketer builds an 11-skill AI content system in Claude Code, structured across five layers: audience profiling, writing style generation, research and ideation, multi-platform drafting, and a self-improving feedback loop that updates all skills monthly based on real content performance data.

Key Questions Answered

• •Lookalike Content Scaling: Feed Claude Code a data dump of your top-performing posts — the system analyzes the top 30% by engagement, extracts structural and emotional patterns, then generates new content ideas mapped to those winning formulas. Without performance data, it analyzes the full dataset. Works with other creators' content if you lack your own archive.
• •Self-Improving Feedback Loop: Build a companion app that captures every piece of content the system produces, then manually input performance metrics monthly. Run a review skill that reads all performance data and automatically rewrites the underlying skill files — meaning the content system improves itself each month based on what actually performed well or poorly.
• •Audience Profile Over ICP: A content audience profile differs from a standard ICP. It maps vocabulary the audience uses versus avoids, emotional registers, validation hooks, and specific content formats they react to — all grounded in engagement data. One profile per platform per persona produces more targeted output than a single generic customer profile.
• •Orchestrator Skill Architecture: Build one master orchestrator skill that calls all other skills automatically. When activated, it loads the correct audience profile, writing style, and prior research, then executes tasks across the full system. This eliminates manual navigation between tools and enables a fully autonomous mode where content is generated overnight without human input.
• •Post Enrichment Layer: After generating a first draft, run a separate enrichment skill that appends platform-specific modules — data points, case studies, executive quotes, or narrative examples — sourced from external research. Each enriched post logs its enrichment source, word count, hook pattern type, and originating talking point file, creating a traceable content production record.