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 mverab/eGEOagents --skill content-scoringgit clone --depth 1 https://github.com/mverab/eGEOagentsWrote 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/mverab/egeoagents/content-scoring)<a href="https://agentmods.dev/skills/mverab/egeoagents/content-scoring"><img src="https://agentmods.dev/badge/skills/mverab/egeoagents/content-scoring/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/mverab/egeoagents/content-scoring"><img src="https://agentmods.dev/badge/skills/mverab/egeoagents/content-scoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00034 | $0.00893 |
| Opus 5.5 | $0.00014 | $0.00357 |
| Sonnet 5.5 | $0.00007 | $0.00179 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
content-scoring 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.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Scoring Skill
When scoring content for GEO optimization:
The 10 GEO Criteria
Score each criterion 0-10:
| # | Criterion | What to Look For |
|---|---|---|
| 1 | Ranking Emphasis | "best", "top", "#1", superlatives, leadership claims |
| 2 | User Intent | Direct answers, addresses specific needs, solves problems |
| 3 | Competitive Diff | Unique advantages, "unlike others", differentiators |
| 4 | Social Proof | Stats, testimonials, reviews, customer counts, ratings |
| 5 | Narrative | Engaging flow, persuasive language, compelling story |
| 6 | Authority | Expert tone, credentials, specific knowledge, confidence |
| 7 | USPs | Clear unique value, what makes it special |
| 8 | Urgency | Time limits, scarcity, "now", limited availability |
| 9 | Scannable | Headers, bullets, short paragraphs, clear structure |
| 10 | Factual | Verifiable claims, specific numbers, accurate info |
Scoring Guide
- 0-2: Missing or severely lacking
- 3-4: Present but weak
- 5-6: Adequate, room for improvement
- 7-8: Good, minor improvements possible
- 9-10: Excellent, near optimal
Output Format
┌─────────────────────────────────────────────────────────────┐
│ 📊 GEO CONTENT SCORE │
├─────────────────────────────────────────────────────────────┤
│ │
│ OVERALL SCORE: XX/100 │
│ ██████████████████░░░░░░░░░░ XX% │
│ │
│ BREAKDOWN │
│ ───────── │
│ 1. Ranking Emphasis ████████░░ 8/10 │
│ 2. User Intent ██████████ 10/10 │
│ 3. Competitive Diff ████░░░░░░ 4/10 │
│ 4. Social Proof ██░░░░░░░░ 2/10 ⚠️ Priority │
│ 5. Narrative ██████░░░░ 6/10 │
│ 6. Authority ████████░░ 8/10 │
│ 7. USPs ██████░░░░ 6/10 │
│ 8. Urgency ░░░░░░░░░░ 0/10 ⚠️ Priority │
│ 9. Scannable ████████░░ 8/10 │
│ 10. Factual ██████████ 10/10 │
│ │
│ TOP PRIORITIES │
│ ────────────── │
│ 1. Add social proof (+15-20 points potential) │
│ 2. Add urgency signals (+5-10 points potential) │
│ 3. Strengthen competitive differentiation (+8 points) │
│ │
│ EVIDENCE │
│ ──────── │
│ ✓ Good: "industry-leading solution" (ranking emphasis) │
│ ✗ Missing: No customer testimonials (social proof) │
│ ✗ Missing: No time-sensitive offers (urgency) │
│ │
└─────────────────────────────────────────────────────────────┘
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 · 78 lines · 34 tokens per session scan A ee1cba09d07a
content-scoring is a skill published in the GitHub repository mverab/eGEOagents (207 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 893 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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