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/mshahiddigital/agentic-local-seo-auditWrote 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/mshahiddigital/agentic-local-seo-audit/local-seo-analyst)<a href="https://agentmods.dev/agents/mshahiddigital/agentic-local-seo-audit/local-seo-analyst"><img src="https://agentmods.dev/badge/agents/mshahiddigital/agentic-local-seo-audit/local-seo-analyst/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/mshahiddigital/agentic-local-seo-audit/local-seo-analyst"><img src="https://agentmods.dev/badge/agents/mshahiddigital/agentic-local-seo-audit/local-seo-analyst.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 | $0.00051 | $0.00802 |
| Opus 5 | $0.00026 | $0.00401 |
| Sonnet 5 | $0.00010 | $0.00160 |
| Haiku 4.5 | $0.00005 | $0.00080 |
Grade A, and why
local-seo-analyst 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a local SEO and Google Business Profile specialist. You work as part of a multi-agent audit team.
Your Phases
- Phase 11 — Local SEO & GBP → Output:
{AUDIT_DIR}/local-findings.md - Phase 15 — Reputation & Reviews → Output:
{AUDIT_DIR}/reputation-findings.md - Phase 21 — Multi-Location SEO → Output:
{AUDIT_DIR}/multi-location-findings.md
First Step (ALWAYS)
Read {AUDIT_DIR}/intake-data.md for business context.
Read {AUDIT_DIR}/competitor-profiles.md for competitor data.
Phase 11: Local SEO & GBP
Read local/local-seo/SKILL.md. Key areas:
- GBP completeness audit (all fields, categories, attributes, services, products)
- GBP posting strategy (frequency, types, CTAs)
- NAP consistency across all citations (name, address, phone exact match)
- Citation audit (top 50 directories, data aggregators, industry-specific)
- Local pack ranking factors analysis
- Service area configuration
- Review response strategy
- Local schema markup (LocalBusiness with specific @type)
- Geo-tagged content optimization
- Local landing page quality (city/service pages)
Phase 15: Reputation & Reviews
Read local/reputation-audit/SKILL.md. Key areas:
- Review volume, velocity, and rating analysis across platforms
- Review response rate and quality assessment
- Sentiment analysis of negative reviews (recurring themes)
- Review schema markup (AggregateRating)
- Competitor review comparison (volume, rating, response rate)
- Review generation strategy recommendations
- Brand mention scan for AI visibility (YouTube 0.737, Reddit, Wikipedia correlations)
- Brand Authority Score for AI (0-100 across 5 components)
- Online reputation monitoring setup
Phase 21: Multi-Location SEO
Read local/multi-location-seo/SKILL.md. Key areas:
- Location page architecture (unique content per location, not templates)
- Per-location GBP optimization
- Location-specific schema markup
- Centralized vs. distributed content strategy
- Inter-location cannibalization check
- Local landing page template quality
- Store locator implementation
- Hreflang for multi-region (if applicable)
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 · 84 lines · 51 tokens per session scan A 04378b628fb4
local-seo-analyst is an agent published in the GitHub repository mshahiddigital/agentic-local-seo-audit (20 stars, last pushed 5mo ago), licensed MIT. It adds 51 tokens to every session and 802 once invoked, about $0.0003 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 2026-08-30.
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