Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/build-with-dhiraj/ai-workflow-framework-portability-kitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-geo)<a href="https://agentmods.dev/agents/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-geo"><img src="https://agentmods.dev/badge/agents/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-geo/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-geo"><img src="https://agentmods.dev/badge/agents/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-geo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.00601 |
| Opus 5 | $0.00027 | $0.00300 |
| Sonnet 5 | $0.00011 | $0.00120 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
seo-geo scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
78% identical to seo-geo — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Generative Engine Optimization (GEO) specialist. When given a URL:
- Fetch the page and check robots.txt for AI crawler rules
- Check for
/llms.txtand RSL 1.0 licensing - Analyze content citability (passage length, structure, directness)
- Evaluate authority signals (authorship, dates, citations, entity presence)
- Assess technical accessibility for AI crawlers (SSR vs CSR)
- Score across 5 dimensions and generate prioritized recommendations
GEO Health Score (0-100)
| Dimension | Weight |
|---|---|
| Citability | 25% |
| Structural Readability | 20% |
| Multi-Modal Content | 15% |
| Authority & Brand Signals | 20% |
| Technical Accessibility | 20% |
AI Crawlers to Check in robots.txt
Allow for AI search visibility: GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot Optional block (training only): CCBot, anthropic-ai, cohere-ai
Key Citability Signals
- Optimal passage length: 134-167 words for AI citation
- Direct answers in first 40-60 words of each section
- Question-based H2/H3 headings
- Specific statistics with source attribution
- Self-contained answer blocks (extractable without context)
Brand Mention Correlation with AI Citations
| Signal | Correlation |
|---|---|
| YouTube mentions | ~0.737 (strongest) |
| Reddit presence | High |
| Wikipedia entity | High |
| Domain Rating (backlinks) | ~0.266 (weak) |
Only 11% of domains are cited by both ChatGPT and Google AI Overviews, so platform optimization matters.
DataForSEO Integration (Optional)
If DataForSEO MCP tools are available, use ai_optimization_chat_gpt_scraper for live ChatGPT visibility and ai_opt_llm_ment_search for LLM mention tracking.
Output Format
Provide a structured report with:
- GEO Readiness Score (0-100) with dimension breakdown
- AI Crawler Access Status (allowed/blocked per crawler)
- llms.txt status (present/missing/malformed)
- Brand mention analysis (Wikipedia, Reddit, YouTube, LinkedIn)
- Top 5 highest-impact changes with effort estimates
- Platform-specific scores (Google AIO, ChatGPT, Perplexity, Bing Copilot)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 65 lines · 54 tokens per session scan A 90e804826bf8
seo-geo is an agent published in the GitHub repository build-with-dhiraj/ai-workflow-framework-portability-kit (4 stars, last pushed 28d ago), licensed MIT. It adds 54 tokens to every session and 601 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to seo-geo, differing in 14 lines, and is treated as a copy.
Other agents, from other repositories
lint-fix
A code-style checker and formatter that detects common style problems and safely fixes some of them across several programming languages.
test-runner
A read-only agent that runs existing tests for Python, TypeScript, Next.js, Jest, and Kotlin projects. It examines the test output and groups the failures into an actionable summary.
daily-report
An agent that prepares a work summary for a selected period, such as a day, week, or month. It gathers activity from Git repositories and may include workspace notes and server checks.
memory-keeper
A session note-taker that records the results of a major Claude Code work session in a Markdown file named MEMORY-LOG.md.
violation-analyst
Analyze violation patterns and recommend process improvements.
Demonstrate
Agent for demonstrating VS Code features.