القائمة
Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Senior agent organizer with expertise in assembling and coordinating multi-agent teams. Your focus spans task analysis, agent capability mapping, workflow design, and team optimization.
AI agent design principles. Agent loops, tool calling, memory architectures, multi-agent coordination, human-in-the-loop gates, and guardrails. Use when building AI agents, autonomous workflows, or any system where an LLM plans and executes multi-step tasks.
API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.
| name | geo-fundamentals |
| description | Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity). |
| allowed-tools | Read, Write, Edit, Glob, Grep |
| version | 1.0.0 |
| last-updated | "2026-03-12T00:00:00.000Z" |
| applies-to-model | gemini-2.5-pro, claude-3-7-sonnet |
Traditional SEO optimizes for keyword ranking in blue links. GEO optimizes for citation and inclusion in AI-generated answers.
When users ask ChatGPT, Claude, Perplexity, or Google SGE a question, an AI synthesizes an answer from sources it considers authoritative. GEO is the practice of making your content the kind that AI systems cite, quote, and summarize.
The mechanisms differ from traditional SEO:
Based on observable patterns in AI retrieval:
| Signal | What It Means |
|---|---|
| Authoritative domain | .gov, .edu, established publications get higher base trust |
| Factual specificity | Numbers, dates, named sources > vague descriptions |
| Structured content | Lists, tables, step-by-step = easier to extract |
| Complete answers | Content that fully addresses the question in one place |
| Original research | Data, studies, surveys the AI can't find elsewhere |
| Freshness | Recently updated content has advantage for time-sensitive topics |
AI models extract answers. Put the answer at the top of the content, not after a 300-word preamble.
❌ Old blog pattern:
[intro paragraph explaining what the article is about]
[background context]
[finally... the answer in paragraph 5]
✅ GEO pattern:
[Direct answer in first 1–2 sentences]
[Supporting evidence, data, explanation]
[Deeper context for readers who want more]
# Format that AI can easily extract:
## Definition
[Term] is [concise definition]. [Supporting context].
## Key Facts
- [Specific, numbered fact with source]
- [Specific statistic — "X% of Y according to Z study"]
- [Named, verifiable claim]
## Step-by-Step Process
1. [Precise step]
2. [Precise step]
3. [Precise step]
❌ "Many developers prefer TypeScript"
✅ "In the 2024 Stack Overflow Developer Survey, 64% of respondents said they used TypeScript"
❌ "Deployment takes some time"
✅ "Vercel cold-start latency for serverless functions averages 200–400ms for Node.js 20 runtimes"
| Format | GEO Value | Why |
|---|---|---|
| FAQ pages | High | Matches question-answer format of AI responses |
| Comparison tables | High | Easily extracted for comparison queries |
| How-to guides with numbered steps | High | Directly answerable procedural questions |
| Definition/explanation articles | High | Definitional queries are common AI use cases |
| Long-form opinion pieces | Low | Hard to extract a clear answer from |
| News articles | Medium | Good for recency, lower for evergreen queries |
<!-- Ensure Perplexity and other AI crawlers can access your content -->
<!-- Check robots.txt — don't accidentally block AI crawlers -->
# robots.txt — allow AI crawlers
User-agent: PerplexityBot
Allow: /
User-agent: ChatGPT-User
Allow: /
User-agent: ClaudeBot
Allow: /
<!-- Structured data helps AI understand content context -->
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "What is tribunal-kit?",
"acceptedAnswer": {
"@type": "Answer",
"text": "tribunal-kit is an npm package that installs an anti-hallucination agent kit..."
}
}]
}
</script>
Traditional SEO metrics (keyword rank, backlinks) don't fully apply. Track:
| Script | Purpose | Run With |
|---|---|---|
scripts/geo_checker.py | Audits content for GEO best practices | python scripts/geo_checker.py <url> |
When this skill produces a recommendation or design decision, structure your output as:
━━━ Geo Fundamentals Recommendation ━━━━━━━━━━━━━━━━
Decision: [what was chosen / proposed]
Rationale: [why — one concise line]
Trade-offs: [what is consciously accepted]
Next action: [concrete next step for the user]
─────────────────────────────────────────────────
Pre-Flight: ✅ All checks passed
or ❌ [blocking item that must be resolved first]
AI coding assistants often fall into specific bad habits when dealing with this domain. These are strictly forbidden:
// VERIFY or check package.json / requirements.txt.Slash command: /review or /tribunal-full
Active reviewers: logic-reviewer · security-auditor
// VERIFY: [reason].Review these questions before confirming output:
✅ Did I rely ONLY on real, verified tools and methods?
✅ Is this solution appropriately scoped to the user's constraints?
✅ Did I handle potential failure modes and edge cases?
✅ Have I avoided generic boilerplate that doesn't add value?
CRITICAL: You must follow a strict "evidence-based closeout" state machine.