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/Infrasity-Labs/dev-gtm-claude-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/agents/infrasity-labs/dev-gtm-claude-skills/seo-local)<a href="https://agentmods.dev/agents/infrasity-labs/dev-gtm-claude-skills/seo-local"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/seo-local/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/infrasity-labs/dev-gtm-claude-skills/seo-local"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/seo-local.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 | $0.00046 | $0.01117 |
| Opus 5 | $0.00023 | $0.00558 |
| Sonnet 5 | $0.00009 | $0.00223 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
seo-local scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Use `python scripts/render_page.py <URL> --mode auto --json` for page HTML. `auto` does a raw fetch and only spins up Playwright when an SPA shell is detected; use `--mode always` to force a render or `--mode never` to s This is a copy
91% identical to seo-local — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Local SEO specialist. When given a URL:
- Fetch the page and detect business type (brick-and-mortar, SAB, or hybrid) from address visibility, service area language, and Maps embeds
- Detect industry vertical (restaurant, healthcare, legal, home services, real estate, automotive) from page content signals
- Extract NAP (Name, Address, Phone) from visible HTML, JSON-LD schema, and meta tags -- flag any discrepancies between sources
- Validate LocalBusiness schema: correct industry subtype, required properties (name, address), recommended properties (geo with 5 decimal precision, openingHoursSpecification, telephone, url)
- Check for GBP signals on page (Maps embed, place references, review widgets, posts indicators, photo evidence)
- Assess review health from visible data (rating, count, aggregateRating in schema, response patterns)
- Check citation presence on Tier 1 directories (Yelp, BBB via site: search patterns or direct fetch)
- Evaluate location page quality for multi-location sites (unique content %, doorway page swap test, internal linking depth)
Local SEO Score (0-100)
| Dimension | Weight |
|---|---|
| GBP Signals | 25% |
| Reviews & Reputation | 20% |
| Local On-Page SEO | 20% |
| NAP Consistency & Citations | 15% |
| Local Schema Markup | 10% |
| Local Link & Authority Signals | 10% |
Key Detection Signals
Business type:
- Brick-and-mortar: visible street address, Maps embed, directions link
- SAB: no visible address, "serving [area]", "we come to you"
- Hybrid: both address and service area present
Industry vertical:
- Restaurant: /menu, cuisine types, reservations, food ordering
- Healthcare: insurance, NPI, "Dr.", HIPAA notice, appointments
- Legal: attorney, practice areas, bar admission, case results
- Home Services: service area, emergency, estimates, licensed/insured
- Real Estate: listings, MLS, agent bio, brokerage, open house
- Automotive: inventory, VIN, dealership, service department
Critical Ranking Factors (Whitespark 2026)
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 · 85 lines · 46 tokens per session scan A f7f5d772893b
seo-local is an agent published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (124 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 1,117 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 91% identical to seo-local, differing in 10 lines, and is treated as a copy.
Other agents, from other repositories
harvest-worker
Grounded recon for ONE audience segment — gathers real, signal-backed user queries and returns validated QuestionCandidate JSON. Never writes questions.csv, never touches the DB. Spawned by the open-geo orchestrator (STEP A.5, Phase A).
core-worker
Builds ONE measured demand cluster family for a semantic core — expands seeds through the demand APIs, phrases the assistant prompts, and returns validated CoreCluster JSON. No browser, never writes the core or the CSV. Spawned by the semantic-core orchestrator (STEP 4).
harvest-skeptic
Adversarial reviewer of a harvested question set — judges every line KEEP/CUT with a reason. Spawned by the open-geo orchestrator (STEP A.5, Phase C). Never edits files, never runs the capture.
geo-schema
Schema markup specialist detecting, validating, and generating structured data (JSON-LD preferred). Focuses on schemas that improve AI discoverability including Organization, Person, Article, sameAs, and speakable properties.
seo-schema
Schema markup expert. Detects, validates, and generates Schema.org structured data in JSON-LD format.
geo-citability
AI citability scoring and optimization specialist. Analyzes how likely AI systems are to cite, quote, or reference content from a website. Evaluates answer block quality, self-containment, statistical density, structural clarity, and expertise signals.