Borrowing it
Nothing to install: this file belongs to prashishh/seo-geo-report-engine. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/prashishh/seo-geo-report-engine/main/.agents/skills/geo-audit/SKILL.mdgit clone --depth 1 https://github.com/prashishh/seo-geo-report-engineWrote 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/prashishh/seo-geo-report-engine/geo-audit)<a href="https://agentmods.dev/skills/prashishh/seo-geo-report-engine/geo-audit"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/geo-audit/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/prashishh/seo-geo-report-engine/geo-audit"><img src="https://agentmods.dev/badge/skills/prashishh/seo-geo-report-engine/geo-audit.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 | $0.00166 | $0.02301 |
| Opus 5 | $0.00083 | $0.01151 |
| Sonnet 5 | $0.00033 | $0.00460 |
| Haiku 4.5 | $0.00017 | $0.00230 |
Grade A, and why
geo-audit 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
geo-audit
Measures how a brand shows up in AI answers and turns the gaps into a falsifiable fix list. The
engine is Ahrefs Brand Radar (see knowledge/ahrefs-mcp-map.md); the methodology is
playbooks/geo-playbook.md. House method: PERCEIVE → ANALYZE → VALIDATE → ACT.
Data priority everywhere: Ahrefs MCP > CLI connectors > web spot-checks.
Inputs
projects/<client>/client.yml— domain, competitors, target keywords, market. Brand Radar ids come fromahrefs.brand_radar_report_id(andahrefs.project_id).- Existing research in
projects/<client>/research/.
If brand_radar_report_id is missing, list reports with management-brand-radar-reports and the
prompts with management-brand-radar-prompts, pick the right one, and write the id into client.yml.
Workflow
1. PERCEIVE — measure current AI visibility (Brand Radar)
Always doc a tool before first use. Pull, for the client + each competitor:
brand-radar-sov-overview+brand-radar-sov-history— AI share of voice now and its trend.brand-radar-mentions-overview+brand-radar-mentions-history— mention volume + direction.brand-radar-impressions-overview— AI impression scale for the topic set.brand-radar-ai-responses+brand-radar-ai-responses-entities— the actual answer text and the entities co-cited with the brand (surfaces wrong facts and missing associations).brand-radar-cited-domains+brand-radar-cited-pages— who AI cites for the topic (the sources to get mentioned on or out-cite).site-explorer-ai-responses-count— how often the client's domain appears in AI answers vs rivals.
From the same Brand Radar pulls, derive two first-class citation signals per engine:
- AI-citation frequency — how often the brand is cited per engine (from
ai-responses/cited-pages); a brand mentioned but never cited is a citability gap, not a presence gap. - Cross-engine citation — count distinct engines that cite the brand; treat cited by ≥3 engines as the durable-visibility bar (single-engine citation is fragile to one model's ranking change).
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 · 130 lines · 166 tokens per session scan A abaf60a13b51
geo-audit is a skill published in the GitHub repository prashishh/seo-geo-report-engine (5 stars, last pushed 2mo ago), licensed MIT. It adds 166 tokens to every session and 2,301 once invoked, about $0.0008 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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