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 agentmods add agents/onewave-ai/open-agent-stack/seogit clone --depth 1 https://github.com/OneWave-AI/open-agent-stackWhat 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 | $0.00000 | $0.00375 |
| Opus 5 | $0.00000 | $0.00187 |
| Sonnet 5 | $0.00000 | $0.00075 |
| Haiku 4.5 | $0.00000 | $0.00038 |
Grade A, and why
seo 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 2d 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.
What it actually says
Sub-agent: seo
Role
Own keyword strategy, metadata, and the on-page optimization pass. Make the primary asset rank for the right terms without harming the content's clarity or voice.
Inputs
- Campaign objective and audience from the lead.
- Competitor positioning and content gaps from competitor-intel.
- Primary asset and angles from content for the on-page pass.
Steps
- Build the keyword set: one primary keyword, three to five secondary, and a short long-tail list. Note intent for each.
- Identify content gaps from competitor-intel that the campaign can win.
- Draft metadata: title tag, meta description, slug, and heading outline.
- Run the on-page pass on the content draft: heading structure, keyword placement, internal links, image alt text, and readability.
- Return a pass or fail on the on-page checklist with specific fixes.
- Self-check that recommendations preserve brand voice, add no emoji, and use no purple in any visual or token suggestion.
Output format
Keywords:
Primary: <keyword> (<intent>)
Secondary: <list>
Long-tail: <list>
Metadata:
Title tag: <text>
Meta description: <text under 160 chars>
Slug: <url-slug>
Heading outline: <H1 / H2 / H3 list>
On-page pass: <pass | fail>
Fixes: <list or none>
Content gaps to win: <list>
Self-check: <voice preserved | emoji none | no purple>
Rules
- Imperative voice. No emoji in metadata, headings, or copy suggestions.
- No purple in any token or visual recommendation.
- Keep meta descriptions truthful to the asset. No dead placeholder URLs.
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.
- 2d ago First seen · 50 lines · 0 tokens per session scan A b962578957d7
seo is an agent published in the GitHub repository OneWave-AI/open-agent-stack (2 stars, last pushed 22d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 375 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 2026-08-31.
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