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.
npx skills add build-with-dhiraj/ai-workflow-framework-portability-kit --skill seo-geogit 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/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-geo)<a href="https://agentmods.dev/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-geo"><img src="https://agentmods.dev/badge/skills/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/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-geo"><img src="https://agentmods.dev/badge/skills/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 | $0.00110 | $0.02064 |
| Opus 5.5 | $0.00044 | $0.00826 |
| Sonnet 5.5 | $0.00022 | $0.00413 |
| Haiku 4.5 | $0.00011 | $0.00206 |
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 1mo 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
83% identical to seo-geo — 238 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Search / GEO Optimization (February 2026)
Key Statistics
| Metric | Value | Source |
|---|---|---|
| AI Overviews reach | 1.5 billion users/month across 200+ countries | |
| AI Overviews query coverage | 50%+ of all queries | Industry data |
| AI-referred sessions growth | 527% (Jan-May 2025) | SparkToro |
| ChatGPT weekly active users | 900 million | OpenAI |
| Perplexity monthly queries | 500+ million | Perplexity |
Critical Insight: Brand Mentions > Backlinks
Brand mentions correlate 3x more strongly with AI visibility than backlinks. (Ahrefs December 2025 study of 75,000 brands)
| Signal | Correlation with AI Citations |
|---|---|
| YouTube mentions | ~0.737 (strongest) |
| Reddit mentions | High |
| Wikipedia presence | High |
| LinkedIn presence | Moderate |
| Domain Rating (backlinks) | ~0.266 (weak) |
Only 11% of domains are cited by both ChatGPT and Google AI Overviews for the same query, so platform-specific optimization is essential.
GEO Analysis Criteria (Updated)
1. Citability Score (25%)
Optimal passage length: 134-167 words for AI citation.
Strong signals:
- Clear, quotable sentences with specific facts/statistics
- Self-contained answer blocks (can be extracted without context)
- Direct answer in first 40-60 words of section
- Claims attributed with specific sources
- Definitions following "X is..." or "X refers to..." patterns
- Unique data points not found elsewhere
Weak signals:
- Vague, general statements
- Opinion without evidence
- Buried conclusions
- No specific data points
2. Structural Readability (20%)
92% of AI Overview citations come from top-10 ranking pages, but 47% come from pages ranking below position 5, demonstrating different selection logic.
Strong signals:
- Clean H1->H2->H3 heading hierarchy
- Question-based headings (matches query patterns)
- Short paragraphs (2-4 sentences)
- Tables for comparative data
- Ordered/unordered lists for step-by-step or multi-item content
- FAQ sections with clear Q&A format
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 256 lines · 110 tokens per session scan A 5caa15df724b
seo-geo is a skill published in the GitHub repository build-with-dhiraj/ai-workflow-framework-portability-kit (5 stars, last pushed 23d ago), licensed MIT. It adds 110 tokens to every session and 2,064 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 83% identical to seo-geo, differing in 238 lines, and is treated as a copy.
Other skills, from other repositories
codeql
Run CodeQL database creation and security queries, add data-extension models, or process CodeQL SARIF. Use when CodeQL is explicitly requested; use security-review for a broader manual security review.
huggingface-vision-trainer
Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language models.
sharp-edges
Identify error-prone APIs, dangerous configuration, and footgun designs that enable security mistakes. Use for API ergonomics, misuse resistance, secure defaults, or pit-of-success reviews; use code-review for general defects.
code-quality
Agents should invoke this skill for code reviews, linting/formatting setup, maintainability checks, complexity concerns, warning cleanup, coding standards, or quality gates in Rust, TypeScript, Python, shell, and mixed repos.
code-reviewer
Review code for bugs, logic errors, security vulnerabilities, code quality issues, and adherence to project conventions, using confidence-based filtering to report only high-priority issues that truly matter. Use this skill when reviewing a small set of changes locally (such as unstaged diff), when dispatched as a…
semgrep-rule-creator
Creates custom Semgrep rules for detecting security vulnerabilities, bug patterns, and code patterns. Use when writing Semgrep rules or building custom static analysis detections.