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 multiplex-ai/muggle-ai-teams --skill geo-contentgit clone --depth 1 https://github.com/multiplex-ai/muggle-ai-teamsWrote 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/multiplex-ai/muggle-ai-teams/geo-content)<a href="https://agentmods.dev/skills/multiplex-ai/muggle-ai-teams/geo-content"><img src="https://agentmods.dev/badge/skills/multiplex-ai/muggle-ai-teams/geo-content/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/multiplex-ai/muggle-ai-teams/geo-content"><img src="https://agentmods.dev/badge/skills/multiplex-ai/muggle-ai-teams/geo-content.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 | $0.00031 | $0.03734 |
| Opus 5.5 | $0.00012 | $0.01494 |
| Sonnet 5.5 | $0.00006 | $0.00747 |
| Haiku 4.5 | $0.00003 | $0.00373 |
Grade A, and why
geo-content 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 5d 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
98% identical to geo-content — 1 line 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 — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Content Quality & E-E-A-T Assessment
Purpose
AI search platforms do not just find content — they evaluate whether content deserves to be cited. The primary framework for this evaluation is E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), which per Google's December 2025 Quality Rater Guidelines update now applies to ALL competitive queries, not just YMYL (Your Money Your Life) topics. Content that scores high on E-E-A-T is dramatically more likely to be cited by AI platforms.
This skill evaluates content through two lenses:
- E-E-A-T signals — does the content demonstrate real expertise and trust?
- AI citability — is the content structured so AI platforms can extract and cite specific claims?
How to Use This Skill
- Fetch the target page(s) — homepage, key blog posts, service/product pages
- Evaluate E-E-A-T across the 4 dimensions (25% each)
- Assess content quality metrics (structure, readability, depth)
- Check for AI content quality signals
- Evaluate topical authority across the site
- Score and generate GEO-CONTENT-ANALYSIS.md
E-E-A-T Framework (100 points total)
Experience — 25 points
First-hand knowledge and direct involvement with the topic. AI platforms increasingly distinguish between content that reports on a topic and content from someone who has DONE it.
Signals to evaluate:
| Signal | Points | How to Score |
|---|---|---|
| First-person accounts ("I tested...", "We implemented...") | 5 | 5 if present and specific, 3 if generic, 0 if absent |
| Original research or data not available elsewhere | 5 | 5 if original data, 3 if references original work, 0 if none |
| Case studies with specific results | 4 | 4 if detailed with numbers, 2 if general, 0 if none |
| Screenshots, photos, or evidence of direct use | 3 | 3 if authentic evidence, 1 if stock/generic, 0 if none |
| Specific examples from personal experience | 4 | 4 if specific and unique, 2 if somewhat specific, 0 if generic |
| Demonstrations of process (not just outcome) | 4 | 4 if step-by-step from experience, 2 if partial, 0 if none |
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.
- 5d ago First seen · 345 lines · 31 tokens per session scan A 063f2e3438ea
geo-content is a skill published in the GitHub repository multiplex-ai/muggle-ai-teams (2 stars, last pushed 2d ago), licensed MIT. It adds 31 tokens to every session and 3,734 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to geo-content, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
geo-content
Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure.
geo-content
Content quality and E-E-A-T assessment for AI citability: evaluate experience, expertise, authoritativeness, trustworthiness, and content structure.
geo-content
Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure.
geo-content
Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure.
article-writing
Use when writing one long-form article end to end — answer-first lede, question-shaped headings, plus its on-page surface (title, meta, slug, FAQ, Article/FAQPage JSON-LD) — or fixing a draft that buries the answer or reads AI-padded. NOT keyword research or topic selection (that is seo-geo), NOT the editorial…
codex-handoff
Three-phase delivery workflow — Claude plans and writes a spec, a Claude subagent implements in the host working tree, a fresh Codex session adversarially reviews the diff (via the codex-plugin-cc plugin). Implementation and review are never the same model. Use for any change touching more than one file or real…