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 Infinite-Labs-AI/infinite-skills --skill ai-seogit clone --depth 1 https://github.com/Infinite-Labs-AI/infinite-skillsWrote 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/infinite-labs-ai/infinite-skills/ai-seo)<a href="https://agentmods.dev/skills/infinite-labs-ai/infinite-skills/ai-seo"><img src="https://agentmods.dev/badge/skills/infinite-labs-ai/infinite-skills/ai-seo/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/infinite-labs-ai/infinite-skills/ai-seo"><img src="https://agentmods.dev/badge/skills/infinite-labs-ai/infinite-skills/ai-seo.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 | $0.00032 | $0.00498 |
| Opus 5.5 | $0.00013 | $0.00199 |
| Sonnet 5.5 | $0.00006 | $0.00100 |
| Haiku 4.5 | $0.00003 | $0.00050 |
Grade A, and why
ai-seo 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI SEO
Make the company, product, category, use cases, and proof easy for AI answer engines to understand and cite.
Audit The Entity
Inspect what the web can verify:
- Official site pages that explain the product and category.
- Founder, team, or company pages.
- Docs, pricing, comparisons, case studies, changelog, integrations.
- Third-party mentions, directories, reviews, podcasts, GitHub, app stores, communities.
- Structured data and clean metadata.
If live research is needed, use current sources and prefer primary pages.
Diagnose Answerability
Ask:
- Can an AI system identify what this is in one sentence?
- Is the category explicit?
- Are use cases and target customers named?
- Are claims supported by public proof?
- Are comparisons stated cleanly without legal or factual overreach?
- Are there third-party corroboration points?
- Can important pages be crawled without app login or script-only rendering?
Improve The Evidence
Inventory public evidence by claim:
- Claim the company wants answer engines to repeat.
- Official source that supports it.
- Third-party corroboration.
- Query or buying question it helps answer.
- Evidence strength.
- Missing proof or crawl problem.
Then recommend assets that make weak claims easier to verify: canonical explainer, use case page, public example, customer story, fair comparison, documentation, review surface, or reputable third-party mention.
Guardrails
- Do not recommend fake reviews, synthetic citations, or spam directories.
- Do not overstate unsupported claims.
- Treat answer engines as retrieval and synthesis systems, not magic keyword boxes.
- Prefer durable public evidence over prompt-hack phrasing.
Output
AI answer presence read:
[one paragraph]
Entity clarity:
- Category:
- Audience:
- Use cases:
- Proof:
- Third-party corroboration:
Gaps:
1. [gap] -> [why it matters]
Evidence table:
| Claim | Official source | Third-party source | Query supported | Strength | Gap |
Assets to create:
| Asset | Query/job it supports | Proof required | Priority |
Schema and crawl fixes:
- [fix]
30-day presence plan:
1. [action]
2. [action]
3. [action]
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 · 84 lines · 32 tokens per session scan A 24972977cf27
ai-seo is a skill published in the GitHub repository Infinite-Labs-AI/infinite-skills (44 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 498 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.
Other skills, from other repositories
follow-loop
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pr-consumer-research
A skill for turning existing consumer, market, competitor, voice-of-customer, or product-test evidence into public-relations decisions. It links research findings to what a company can safely say in public and what it should do next.
consumer-research
A consumer research skill for studying what people buy, prefer, and say about consumer products. It helps plan studies and turn findings into useful insights.
industry-research
A skill for researching an industry and producing decision-focused analysis. It covers markets, customers, technology, costs, competitors, channels, supply chains, policy, investment, and risks.
hot-public-opinion-analysis
A public-opinion analysis skill for examining trending events, widely discussed news, industry disputes, competitor incidents, policy changes, negative narratives, rumours, and social topics.
judgment-hub
A decision-making skill for public-relations situations that require a final call, risk trade-offs, task routing, or strategic judgment.