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 psyduckler/aeo-skills --skill aeo-multi-prompt-strategygit clone --depth 1 https://github.com/psyduckler/aeo-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/psyduckler/aeo-skills/aeo-multi-prompt-strategy)<a href="https://agentmods.dev/skills/psyduckler/aeo-skills/aeo-multi-prompt-strategy"><img src="https://agentmods.dev/badge/skills/psyduckler/aeo-skills/aeo-multi-prompt-strategy/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/psyduckler/aeo-skills/aeo-multi-prompt-strategy"><img src="https://agentmods.dev/badge/skills/psyduckler/aeo-skills/aeo-multi-prompt-strategy.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 | $0.00130 | $0.02096 |
| Opus 5 | $0.00065 | $0.01048 |
| Sonnet 5 | $0.00026 | $0.00419 |
| Haiku 4.5 | $0.00013 | $0.00210 |
Grade A, and why
aeo-multi-prompt-strategy 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 11d 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 — 193 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AEO Multi-Prompt Strategy
Source: github.com/psyduckler/aeo-skills Part of: AEO Skills Suite
Find authority hub pages that enter the recurring retrieval set for multiple query patterns — win many prompts with one page.
Background
Influence over AI answers compounds through repeated inclusion in the recurring retrieval set. But the most powerful form of authority isn't being cited for one prompt — it's being cited for many. When a single page enters the candidate set for 5 different prompts, it becomes an authority hub: the model has learned to retrieve it across diverse query patterns.
This matters because Gemini generates varied, specific search queries — the "long long tail" of its search behavior means each prompt generates different queries, yet the same authoritative pages keep surfacing. Authority hubs exploit this: they're comprehensive enough to match multiple query patterns, creating expanding entry points across the retrieval landscape.
In Gemini's search-first architecture, every prompt fires fresh web searches. A page that appears in the results for prompts A, B, and C is seen by the model three times as often as a page that only appears for prompt A. This repeated visibility across different retrieval contexts is what builds durable authority — not in the model's weights, but in the model's consistent retrieval behavior.
The strategic question this skill answers: should you build one comprehensive hub page, or many focused pages? The data tells you which approach the model's retrieval behavior actually rewards.
Defaults
- Model:
gemini-3-flash-preview— the same model powering Google AI Overviews - Samples: 20 runs per prompt — captures cross-prompt citation patterns
Requirements
- Gemini API key (free from aistudio.google.com) — set as
GEMINI_API_KEYenv var - Python 3.9+
- No pip dependencies (stdlib only)
What ships with it
2 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.
- 11d ago First seen · 193 lines · 130 tokens per session scan A a351e9abc57e
aeo-multi-prompt-strategy is a skill published in the GitHub repository psyduckler/aeo-skills (11 stars, last pushed 3mo ago), licensed MIT. It adds 130 tokens to every session and 2,096 once invoked, about $0.0006 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.
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