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 jrr996shujin-png/openclaw-seo-aeo-skills --skill aeo-content-strategygit clone --depth 1 https://github.com/jrr996shujin-png/openclaw-seo-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/jrr996shujin-png/openclaw-seo-aeo-skills/aeo-content-strategy)<a href="https://agentmods.dev/skills/jrr996shujin-png/openclaw-seo-aeo-skills/aeo-content-strategy"><img src="https://agentmods.dev/badge/skills/jrr996shujin-png/openclaw-seo-aeo-skills/aeo-content-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/jrr996shujin-png/openclaw-seo-aeo-skills/aeo-content-strategy"><img src="https://agentmods.dev/badge/skills/jrr996shujin-png/openclaw-seo-aeo-skills/aeo-content-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 | $0.00209 | $0.02787 |
| Opus 5 | $0.00105 | $0.01393 |
| Sonnet 5 | $0.00042 | $0.00557 |
| Haiku 4.5 | $0.00021 | $0.00279 |
Grade A, and why
aeo-content-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 12d 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AEO Content Strategy: Community Monitoring + Long-Tail Mining + Content Planning
What This Skill Does
This skill generates a complete AEO content strategy by combining three interconnected analyses:
- Reddit/Quora Community Monitoring — Discovers what real users are asking, complaining about, and recommending in communities relevant to the user's product
- Long-Tail Question Mining — Transforms community signals and product knowledge into specific, conversational questions that AI platforms are likely to surface
- Content Topic Recommendations — Prioritizes which content to create first based on competition level, purchase intent, and AI citation potential
The output is a single actionable report the user can hand to their content team and start executing immediately.
Why This Combination Matters
These three activities form a natural pipeline. Community discussions reveal what real users care about (not what keyword tools think they care about). Those discussions contain raw long-tail questions that no one has properly answered yet. And those unanswered questions become high-value content opportunities — because when AI searches for answers and finds only your content addressing a specific question, it has no choice but to cite you.
Doing these separately wastes time and loses the connections between them. A Reddit thread about "frustrating email tools" directly feeds into a long-tail question like "what AI tool can automatically sort and reply to customer emails in my brand voice" which directly becomes a blog topic recommendation.
Required Inputs
Before starting, collect these from the user:
| Input | Why It's Needed | Example |
|---|---|---|
| Product/brand name | To search for direct mentions | "Genspark" |
| Product category | To search for category discussions | "AI agent", "AI productivity tool" |
| Target audience | To calibrate question language and intent | "Solo entrepreneurs and small teams" |
| 2-3 competitor names | To monitor competitor mentions and find gaps | "Manus, ChatGPT, Perplexity" |
| Key use cases (3-5) | To focus long-tail mining on real scenarios | "Email automation, meeting notes, research" |
| Target language/market | To determine which communities to scan | "English, US market" |
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.
- 12d ago First seen · 240 lines · 209 tokens per session scan A 717f34942d44
aeo-content-strategy is a skill published in the GitHub repository jrr996shujin-png/openclaw-seo-aeo-skills (11 stars, last pushed 6mo ago), licensed MIT. It adds 209 tokens to every session and 2,787 once invoked, about $0.0010 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 2026-08-31.
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