Core Eeat Agent Skill Main

Content Generator Framework

A universal content generation framework based on CORE + EEAT principles, optimized for both traditional SEO and AI search engines (GEO).

Quick Reference

Note: types/content-data.ts is a reference document. Actual types live in your project's src/lib/ directory.

Supported Content Types

Content Type Decision Flowchart

START: What is the primary goal?
│
├─► Teach HOW to do something? → blog (guides)
├─► Compare YOUR product vs competitor? → alternative
├─► Compare MULTIPLE products (listicle)? → best-of
├─► Show WHO benefits from your product? → use-case
├─► Answer common questions? → faq (or faqs[] field)
├─► Showcase CUSTOMER SUCCESS? → testimonial
├─► Describe PRODUCT features? → landing
└─► Share INSIGHTS or trends? → blog (insights)

Edge cases? See content-type-decisions.md for detailed decision trees.

Framework Layers

┌─────────────────────────────────────────┐
│           CORE + EEAT Principles        │  ← Universal methodology
├─────────────────────────────────────────┤
│     Components (SEO + GEO + UI)         │  ← Reusable patterns
├─────────────────────────────────────────┤
│         Content Type Guides             │  ← Type-specific rules
├─────────────────────────────────────────┤
│      Types (TypeScript Interfaces)      │  ← Data structures
└─────────────────────────────────────────┘

Core Principles Summary

CORE (Content Optimization)

EEAT (Trust Signals)

Generation Workflow

🚨 PRE-FLIGHT (MANDATORY):
   → Read batch-generation-safety.md
   → Complete quality-gates.md Gate 1
   → Set batch size (max 10 articles)

0. Research keywords → Read keyword-research/GUIDE.md, check CSV data
1. Select content type → Read GUIDE.md
2. Apply CORE principles → Check core-principles.md
3. Add EEAT signals → Check eeat-signals.md
4. Include components → Check page-enhancements.md
5. Add SEO schema → Check seo-schemas.md
6. Optimize for GEO → Check geo-optimization.md, geo-strategies.md
7. Validate per batch → Run quality-gates.md Gates 2-4
8. Final check → Run quality-checklist.md, quality-technical.md

⚠️ IF ERRORS: See failure-recovery.md

Keyword Research Integration

Use keyword data from SEMrush, Ahrefs, and GSC to inform content:

Data Sources

Workflow

1. Find target keyword in CSV data
2. Extract metrics: volume, difficulty, current position
3. Identify SERP features: AI Overview, Featured Snippet
4. Populate content fields: targetKeyword, monthlySearchVolume, keywords

Quick Reference

// From keyword research
targetKeyword: 'youtube transcript download',
monthlySearchVolume: 2400,  // From SEMrush/Ahrefs

See keyword-research/GUIDE.md for detailed instructions.

Usage with Project-Specific Data

This framework is designed to be used alongside a project-specific data skill:

content-generator-framework/    ← This skill (universal)
├── keyword-research/           # CSV parsing tools & guides
│   ├── GUIDE.md               # Usage guide
│   ├── types.ts               # Type definitions (reference)
│   └── parsers.ts             # CSV parsing functions (reference)
└── ...

your-project-content-data/      ← Your project skill (specific)
├── SKILL.md                    # Products, competitors, author, Step 0
├── types.md                    # Type reference (actual types in src/lib/)
├── brand.md                    # Voice, CTAs, positioning
├── data/
│   ├── statistics.md           # Verified stats
│   └── citations.md            # Source library
└── examples/                   # Content examples (.md format)

data/keyword-research/          ← Project root (gitignored CSVs)
├── semrush/                    # SEMrush exports
├── ahrefs/                     # Ahrefs exports
└── gsc/                        # GSC exports

Note: Use .md files for skills. Actual TypeScript types live in your project's src/lib/ directory. Keyword research CSVs are stored in data/keyword-research/ at project root.

Create yours: See PROJECT-DATA-CREATOR.md