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 Cristhianzl/claude-skills-czl --skill optimizing-discoverabilitygit clone --depth 1 https://github.com/Cristhianzl/claude-skills-czlWrote 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/cristhianzl/claude-skills-czl/optimizing-discoverability)<a href="https://agentmods.dev/skills/cristhianzl/claude-skills-czl/optimizing-discoverability"><img src="https://agentmods.dev/badge/skills/cristhianzl/claude-skills-czl/optimizing-discoverability/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/cristhianzl/claude-skills-czl/optimizing-discoverability"><img src="https://agentmods.dev/badge/skills/cristhianzl/claude-skills-czl/optimizing-discoverability.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 | $0.00000 | $0.01474 |
| Opus 5.5 | $0.00000 | $0.00590 |
| Sonnet 5.5 | $0.00000 | $0.00295 |
| Haiku 4.5 | $0.00000 | $0.00147 |
Grade A, and why
optimizing-discoverability 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimizing discoverability — SEO · AEO · GEO
Three lenses on one goal, each aiming at a different "searcher": SEO wants the click on a results page; AEO wants to be the answer (featured snippet, People Also Ask, voice); GEO wants to be the source an AI cites (ChatGPT, Claude, Perplexity, AI Overviews). They compound: answer engines and generative engines overwhelmingly pull from pages that already index and rank well — SEO is the foundation; AEO and GEO are layers on top, not replacements.
Read first (always)
List learnings/ and read anything relevant — the project's niche, existing rankings, target queries, analytics stack, and past decisions live there and override the defaults here.
Grounding — what this skill is based on
- Google's official AI-features guidance (developers.google.com/search/docs/fundamentals/ai-optimization-guide): AI features "are rooted in our core Search ranking and quality systems" — pages must be indexed and snippet-eligible; the winning content is "unique, non-commodity" with a real point of view.
- The GEO study (Aggarwal et al., KDD 2024 — arxiv.org/abs/2311.09735): first large-scale test (9 tactics, 10k queries) of what makes generative engines cite a page. Adding citations to sources, quotations, and statistics boosted visibility 30–40%; keyword stuffing made it worse.
Myths — officially debunked, don't spend effort here
Google states these explicitly (for Google surfaces):
llms.txtand "AI text files" are ignored by Google Search. (For non-Google engines it's an emerging, optional standard — seereferences/geo.md; never treat it as the strategy.)- No chunking required — you don't need to fragment content "for the AI".
- No special schema is required for AI features; structured data clarifies what's already visible, it doesn't unlock anything hidden.
- Keyword density / long-tail stuffing is obsolete — systems understand synonyms and meaning; the GEO study measured stuffing reducing AI visibility.
- Artificial mentions/link schemes remain counterproductive.
What ships with it
4 files 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 · 61 lines · 0 tokens per session scan A 169231e07bbc
optimizing-discoverability is a skill published in the GitHub repository Cristhianzl/claude-skills-czl (5 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,474 tokens. 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 2026-09-16.
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