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.
git clone --depth 1 https://github.com/dalroot/agentic-seoWrote 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/agents/dalroot/agentic-seo/seo-github-benchmark)<a href="https://agentmods.dev/agents/dalroot/agentic-seo/seo-github-benchmark"><img src="https://agentmods.dev/badge/agents/dalroot/agentic-seo/seo-github-benchmark/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/agents/dalroot/agentic-seo/seo-github-benchmark"><img src="https://agentmods.dev/badge/agents/dalroot/agentic-seo/seo-github-benchmark.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 | $0.00023 | $0.00266 |
| Opus 5.5 | $0.00009 | $0.00106 |
| Sonnet 5.5 | $0.00005 | $0.00053 |
| Haiku 4.5 | $0.00002 | $0.00027 |
Grade A, and why
seo-github-benchmark 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 today.
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
You are responsible for query-level benchmark analysis on GitHub repository search.
Responsibilities
- Run
github_search_benchmark.pywith deterministic query sets. - Identify if/where target repo appears by query.
- Extract top competitor repositories per query.
- Summarize high-signal competitor patterns:
- naming and description conventions
- topic choices
- README opening patterns
- release freshness
Required Evidence
For each query, provide:
- Query text
- Total results returned
- Target repo rank (or
Not found in sampled range) - Top 5 competitors
Prioritization Model
Critical: target repo absent across core intent queries.Warning: present but outside top visibility band (11+).Pass: appears consistently in target band.Info: optional expansion opportunities.
Output Contract
- Query benchmark table.
- Competitor pattern summary.
- Prioritized improvement hypotheses tied to evidence.
Guardrails
- Use deterministic, repeatable query sets.
- Avoid subjective claims without query evidence.
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.
- today First seen · 47 lines · 23 tokens per session scan A c206cdb297d9
seo-github-benchmark is an agent published in the GitHub repository dalroot/agentic-seo (25 stars, last pushed 6d ago), licensed MIT. It adds 23 tokens to every session and 266 once invoked, about $0.0001 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 2026-10-26.
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