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 lisbeth718/pseo-skills --skill pseo-llm-visibilitygit clone --depth 1 https://github.com/lisbeth718/pseo-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/lisbeth718/pseo-skills/pseo-llm-visibility)<a href="https://agentmods.dev/skills/lisbeth718/pseo-skills/pseo-llm-visibility"><img src="https://agentmods.dev/badge/skills/lisbeth718/pseo-skills/pseo-llm-visibility/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/lisbeth718/pseo-skills/pseo-llm-visibility"><img src="https://agentmods.dev/badge/skills/lisbeth718/pseo-skills/pseo-llm-visibility.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 | $0.00090 | $0.02222 |
| Opus 5 | $0.00045 | $0.01111 |
| Sonnet 5 | $0.00018 | $0.00444 |
| Haiku 4.5 | $0.00009 | $0.00222 |
Grade A, and why
pseo-llm-visibility 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.
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pSEO LLM Visibility
Optimize programmatic pages for citation and visibility in AI-generated answers. This is a distinct layer on top of traditional SEO — different crawlers, different extraction patterns, different signals.
Why This Matters for pSEO
- AI-driven search traffic is growing rapidly and represents a significant share of organic discovery
- LLMs cite only a handful of domains per response vs. 10 blue links in traditional search
- Traditional SEO rank is a weak predictor of AI citation — many cited pages rank outside the top 20 in Google
- Content freshness, structure, and extractability matter more than backlinks for LLM visibility
- Google AI Overviews appear on a large and growing share of searches, and most AI-assisted searches result in fewer outbound clicks
Core Principles
- Extractable, not just readable: Content must be structured in self-contained chunks that LLMs can pull verbatim
- Answer-first: Lead with the direct answer, then provide supporting context
- Entity-rich: Reference entities and relationships, not just keywords
- Multi-engine: Optimize for Bing (ChatGPT), Google (AI Overviews), and direct AI crawlers (Perplexity) simultaneously
- Machine-readable: Schema, llms.txt, and clean HTML structure help LLMs understand page semantics
Implementation Steps
1. Create llms.txt
Place a Markdown file at the site root (/llms.txt) that guides LLMs to the most important content. This is a proposed standard gaining rapid adoption — think of it as a curated sitemap for AI.
# [Site Name]
> [One-sentence description of what this site covers]
## Key Pages
- [Category Hub A](/category-a): Description of this category
- [Category Hub B](/category-b): Description of this category
## Content Types
- [Page Type]: [What these pages contain and why they're useful]
## Data Sources
- [Where the data comes from, how often updated]
## Full Content
- [/llms-full.txt](/llms-full.txt): Complete content index
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.
- 10d ago First seen · 212 lines · 90 tokens per session scan A ed73e4acefb5
pseo-llm-visibility is a skill published in the GitHub repository lisbeth718/pseo-skills (53 stars, last pushed 7mo ago), licensed MIT. It adds 90 tokens to every session and 2,222 once invoked, about $0.0005 per session on Opus 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…