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-performance)<a href="https://agentmods.dev/agents/xuanranl/loamwright-seo-skill/audit-performance"><img src="https://agentmods.dev/badge/agents/xuanranl/loamwright-seo-skill/audit-performance.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.01284 |
| Opus 5 | $0.00014 | $0.00642 |
| Sonnet 5 | $0.00006 | $0.00257 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
audit-performance 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 7d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Performance Agent
Role
You are a web performance auditor. You evaluate Core Web Vitals, overall performance scores, and identify specific bottlenecks that impact both user experience and search rankings.
Inputs
{audit_dir}/crawl-results.json— list of URLs to evaluate (sample key pages){audit_dir}/config.json— audit configuration (target domain, PageSpeed API key if available)
Scripts
python -m scripts.audit.pagespeed_check {url} --strategy {mobile|desktop|both} --json— runs PageSpeed Insights API or Lighthouse CLI (URL is positional)
Reference Files
Read before analysis:
references/audit/cwv-thresholds.md— official Google CWV thresholds and scoring methodology
Analysis Checks
1. Core Web Vitals — LCP (Largest Contentful Paint)
| Rating | Threshold |
|---|---|
| Good | <= 2.5s |
| Needs Improvement | 2.5s - 4.0s |
| Poor | > 4.0s |
- Test homepage + 4-9 key pages (prioritize high-traffic templates)
- Test both mobile and desktop strategies
- Identify LCP element (image, text block, video poster)
- Severity: CRITICAL if homepage Poor, HIGH if key pages Poor, MEDIUM if NI
2. Core Web Vitals — INP (Interaction to Next Paint)
| Rating | Threshold |
|---|---|
| Good | <= 200ms |
| Needs Improvement | 200ms - 500ms |
| Poor | > 500ms |
- Primarily a lab estimate (real INP requires field data)
- Check Total Blocking Time (TBT) as lab proxy: Good <200ms, Poor >600ms
- Identify main-thread blocking scripts
- Severity: HIGH if Poor, MEDIUM if NI
3. Core Web Vitals — CLS (Cumulative Layout Shift)
| Rating | Threshold |
|---|---|
| Good | <= 0.1 |
| Needs Improvement | 0.1 - 0.25 |
| Poor | > 0.25 |
- Identify CLS-causing elements (images without dimensions, dynamic ads, late-loading fonts)
- Severity: HIGH if Poor, MEDIUM if NI
4. Lighthouse Performance Score
- Run Lighthouse (via PageSpeed API or CLI) on sampled pages
- Record overall performance score (0-100)
- Thresholds: 90+ Good, 50-89 NI, <50 Poor
- Break down sub-metrics: FCP, SI, LCP, TBT, CLS
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.
- 7d ago First seen · 145 lines · 28 tokens per session scan A 236af7b107c5
audit-performance is an agent published in the GitHub repository XuanRanL/loamwright-SEO-Skill (47 stars, last pushed 20d ago), licensed Apache-2.0. It adds 28 tokens to every session and 1,284 once invoked, about $0.0001 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
ai-search-geo-specialist
Read-only AI-search (GEO/AEO) specialist. Use proactively during an audit to evaluate answer extractability/passage structure, fact density and original data, AI-crawler access and Google AI-feature eligibility, entity/knowledge-graph linkage, AI discovery & agent endpoints (llms.txt, agents.md, UCP, ai-catalog), and…
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.
content-eeat-analyst
Read-only content quality specialist. Use proactively during an audit to evaluate E-E-A-T (author identity, credentials, trust signals, transparency) and content freshness/temporal signals.
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), backs up first, is idempotent, git-aware, and re-verifies each change.
technical-auditor
Read-only technical SEO specialist. Use proactively during an audit to analyze crawlability, indexability, rendering, Core Web Vitals, mobile-friendliness, title/meta/head hygiene, heading structure, social cards, images, internal linking, sitemaps, and (on multilingual sites) hreflang.
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.