Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/psyduckler/aeo-skillsnpx agentmods add skills/psyduckler/aeo-skills/aeo-optimizeWrote 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-optimize)<a href="https://agentmods.dev/skills/psyduckler/aeo-skills/aeo-optimize"><img src="https://agentmods.dev/badge/skills/psyduckler/aeo-skills/aeo-optimize/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-optimize"><img src="https://agentmods.dev/badge/skills/psyduckler/aeo-skills/aeo-optimize.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 | $0.00198 | $0.02033 |
| Opus 5.5 | $0.00079 | $0.00813 |
| Sonnet 5.5 | $0.00040 | $0.00407 |
| Haiku 4.5 | $0.00020 | $0.00203 |
Grade A, and why
aeo-optimize 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 2mo 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AEO Optimize
Source: github.com/psyduckler/aeo-skills Part of: AEO Skills Suite (v2 Core) Reads:
aeo-data/*.json(evidence) + optionalaeo-reports/*.mdWrites: A Markdown work queue (printed to chat or saved to file)
The action layer of the AEO loop. aeo-baseline measures, aeo-report analyzes, aeo-optimize recommends.
How this skill works
This is a SKILL.md-only skill — there is no script to run. The agent reads the methodology in this file plus the references in references/, then applies that methodology to the user's actual data.
The agent should:
- Locate the latest evidence file. Sort
aeo-data/*.jsonby run timestamp and load the latest. - Optionally read the latest aeo-report output at
aeo-reports/*.mdfor pre-computed trend signals. - Optionally fetch one or more brand URLs when a recommendation involves a specific page (use
web_fetch). - Apply the playbooks in
references/action-playbooks.mdto map gaps in the data to concrete actions. - Output a prioritized Markdown work queue with the structure shown below.
Output format
# AEO Action Plan — <brand>
Generated from `aeo-data/<latest-evidence-file>` covering prompts: <list>.
## Quick wins (high impact, low effort)
### 1. Refresh `<url>` with entity "<entity>"
**Evidence:** Mentioned in 14/20 Gemini responses for prompt `<prompt_id>` but missing from your page (`citation_rate = 60%, position avg #4.2`).
**Action:** Add a section that names "<entity>" and explains how it relates to <topic>. Aim for 2–3 sentences of natural mention.
**Effort:** ~30 min
**Expected gain:** Citation rate from 60% → 75–80% based on the recurring retrieval set.
### 2. Add JSON-LD `<schema-type>` to `<url>`
**Evidence:** All cited competitor pages on prompt `<prompt_id>` include `<schema-type>` markup; your page does not.
**Action:** Use [aeo-schema](../aeo-schema/) to generate the JSON-LD block.
**Effort:** ~15 min
**Expected gain:** Improves structural signals for citation.
## Strategic plays (higher effort, higher impact)
### 3. Create comparison page: `<You> vs <Competitor>`
**Evidence:** 8/20 Gemini runs for prompt `<prompt_id>` cited a comparison page from <competitor> (`<competitor>.com/<you>-vs-<competitor>`). You have no comparison page in your sitemap.
**Action:** Draft a 1500–2000 word vs page covering pricing, features, integrations, ideal use case.
**Effort:** ~4 hours
**Expected gain:** Entry into the recurring retrieval set for this prompt; ~15–25pp lift in citation rate over 4–8 weeks.
## Maintenance (alerts, decay)
### 4. Refresh `<url>` — citation rate decaying
**Evidence:** prompt `<prompt_id>` shows citation rate decay: 80% → 45% over the last 6 baselines (METHODOLOGY.md §4: HIGH severity).
**Action:** Audit the page for stale claims, outdated stats, missing competitive context. Update timestamps. Re-publish.
**Effort:** ~1 hour
**Expected gain:** Reverse the decay; restore to 70%+ over 2–4 weeks.
## Hub-page consolidation
### 5. Double down on `<url>` — your strongest hub
**Evidence:** This URL is cited across 4 of 6 tracked prompts (66% coverage). It is your highest-leverage page.
**Action:** Add 3–5 additional sections covering the entities and questions surfaced in `aeo-report` for those prompts. One page that wins more prompts is cheaper than five pages winning one each.
**Effort:** ~2 hours
**Expected gain:** Reinforces hub status; may pick up additional prompts.
## Cannibalization fixes
### 6. Consolidate `<url-a>` and `<url-b>`
**Evidence:** Both pages are cited for prompt `<prompt_id>` with 60%/40% share — the model can't decide which to surface. Internal competition is diluting both.
**Action:** Pick the canonical URL (the one ranking better; usually the older or more comprehensive). 301 the other. Merge unique content into the canonical.
**Effort:** ~1 hour
**Expected gain:** Concentrated citation share on one URL; cleaner signal to the model.
What ships with it
3 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.
- 2mo ago First seen · 133 lines · 198 tokens per session scan A ef0986e5f16c
aeo-optimize is a skill published in the GitHub repository psyduckler/aeo-skills (13 stars, last pushed 5mo ago), licensed MIT. It adds 198 tokens to every session and 2,033 once invoked, about $0.0008 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.
aeo-optimize compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| aeo-optimizepsyduckler/aeo-skills | Skill | 13 | 5mo ago | MIT | A | 198 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |