Awesome-omni-skill geo-fundamentals

Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data-ai/geo-fundamentals-techwavedev" ~/.claude/skills/diegosouzapw-awesome-omni-skill-geo-fundamentals-a07707 && rm -rf "$T"

manifest: skills/data-ai/geo-fundamentals-techwavedev/SKILL.md

source content

GEO Fundamentals

Optimization for AI-powered search engines.

1. What is GEO?

GEO = Generative Engine Optimization

Goal	Platform
Be cited in AI responses	ChatGPT, Claude, Perplexity, Gemini

SEO vs GEO

Aspect	SEO	GEO
Goal	#1 ranking	AI citations
Platform	Google	AI engines
Metrics	Rankings, CTR	Citation rate
Focus	Keywords	Entities, data

2. AI Engine Landscape

Engine	Citation Style	Opportunity
Perplexity	Numbered [1][2]	Highest citation rate
ChatGPT	Inline/footnotes	Custom GPTs
Claude	Contextual	Long-form content
Gemini	Sources section	SEO crossover

3. RAG Retrieval Factors

How AI engines select content to cite:

Factor	Weight
Semantic relevance	~40%
Keyword match	~20%
Authority signals	~15%
Freshness	~10%
Source diversity	~15%

4. Content That Gets Cited

Element	Why It Works
Original statistics	Unique, citable data
Expert quotes	Authority transfer
Clear definitions	Easy to extract
Step-by-step guides	Actionable value
Comparison tables	Structured info
FAQ sections	Direct answers

5. GEO Content Checklist

Content Elements

Technical Elements

6. Entity Building

Action	Purpose
Google Knowledge Panel	Entity recognition
Wikipedia (if notable)	Authority source
Consistent info across web	Entity consolidation
Industry mentions	Authority signals

7. AI Crawler Access

Key AI User-Agents

Crawler	Engine
GPTBot	ChatGPT/OpenAI
Claude-Web	Claude
PerplexityBot	Perplexity
Googlebot	Gemini (shared)

Access Decision

Strategy	When
Allow all	Want AI citations
Block GPTBot	Don't want OpenAI training
Selective	Allow some, block others

8. Measurement

Metric	How to Track
AI citations	Manual monitoring
"According to [Brand]" mentions	Search in AI
Competitor citations	Compare share
AI-referred traffic	UTM parameters

9. Anti-Patterns

❌ Don't	✅ Do
Publish without dates	Add timestamps
Vague attributions	Name sources
Skip author info	Show credentials
Thin content	Comprehensive coverage

Remember: AI cites content that's clear, authoritative, and easy to extract. Be the best answer.

Script

Script	Purpose	Command
`scripts/geo_checker.py`	GEO audit (AI citation readiness)	`python scripts/geo_checker.py <project_path>`

AGI Framework Integration

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

Cache hit? Use cached response directly — no need to re-process.
Memory match? Inject
```
context_chunks
```
into your reasoning.
No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags geo-fundamentals <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

Strategy: This skill communicates via the shared memory system.
Orchestration: Invoked by
```
orchestrator
```
via intelligent routing.
Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

Embeddings:
```
nomic-embed-text
```
via Qdrant memory system
Lightweight analysis: Local models reduce API costs for repetitive patterns