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/XuanRanL/loamwright-SEO-SkillWrote 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/xuanranl/loamwright-seo-skill/audit-local)<a href="https://agentmods.dev/agents/xuanranl/loamwright-seo-skill/audit-local"><img src="https://agentmods.dev/badge/agents/xuanranl/loamwright-seo-skill/audit-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/xuanranl/loamwright-seo-skill/audit-local"><img src="https://agentmods.dev/badge/agents/xuanranl/loamwright-seo-skill/audit-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 | $0.00032 | $0.01085 |
| Opus 5 | $0.00016 | $0.00543 |
| Sonnet 5 | $0.00006 | $0.00217 |
| Haiku 4.5 | $0.00003 | $0.00109 |
Grade A, and why
audit-local scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Local Agent
Spawn Condition
Only run when {audit_dir}/config.json :: business_type is local_brick_mortar, local_sab, or local_hybrid.
Inputs
{audit_dir}/crawl-results.json— crawled pages with HTML, schema blocks, internal links{audit_dir}/config.json— domain, business name, address, phone, service areas{audit_dir}/gbp-data.json— GBP profile (if pre-fetched)
Read references/audit/local-seo-signals.md before analysis.
Scripts
Use these concrete invocations to gather data:
# Parse homepage + contact page for NAP, schema, maps embed
python -m scripts.audit.parse_html {homepage_html} --url "$URL" --json
python -m scripts.audit.parse_html {contact_page_html} --url "$URL" --json
# Fetch robots.txt and key local pages
python -m scripts.audit.fetch_page "$URL/contact" --json
python -m scripts.audit.fetch_page "$URL/about" --json
# Check schema on location/service pages from crawl results
for page in $(cat {audit_dir}/crawl-results.json | python -c "import sys,json; [print(p['url']) for p in json.load(sys.stdin) if '/location' in p.get('url','') or '/service' in p.get('url','')]"); do
python -m scripts.audit.fetch_page "$page" --json
done
Extract NAP from parsed HTML: search for <address>, LocalBusiness schema, phone regex \+?\d[\d\s\-()]{7,}, and structured address fields.
Scoring Dimensions (weighted → overall)
GBP Signals (25%) — Primary category set correctly (single strongest controllable factor). Title = registered name (no keyword stuffing). Verified. Opening hours present + matching website. Description 750 chars with primary service + city. Photos >= 10, updated < 90 days. Posts < 7 days old. Q&A seeded. Deduct 25 if category wrong, 15 if unverified.
Reviews & Reputation (20%) — Rating 4.5+ for competitive markets. Review count in top-3 for local pack. Velocity: new reviews/month trending up. Recency: latest review < 18 days (the "18-day rule" — stale profiles regress). Owner response rate 80%+. Extract sentiment keywords. CRITICAL if rating < 3.5.
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.
- 9d ago First seen · 75 lines · 32 tokens per session scan A a348b986341d
audit-local is an agent published in the GitHub repository XuanRanL/loamwright-SEO-Skill (47 stars, last pushed 22d ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,085 once invoked, about $0.0002 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.
Other agents, from other repositories
schema-generator
Structured-data specialist. Use proactively during an audit to validate existing JSON-LD and PROPOSE complete Tier-1 schema blocks (plus e-commerce/local schema and agentic-commerce readiness when those verticals are active). It proposes diffs only and does NOT write files.
seo-fixer-writer
The ONLY agent allowed to write files. Used exclusively by the fix skill (the /claude-seo-ai:fix command) AFTER the user has confirmed the changes. Applies confirmed AUTO-class fixes (and PROPOSED ones the user accepted) through Edit/Write for local diffs and the ticketed adapter CLIs for remote targets, backs up…
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.
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.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.