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-technical)<a href="https://agentmods.dev/agents/infrasity-labs/dev-gtm-claude-skills/seo-technical"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/seo-technical/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-technical"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/seo-technical.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 | $0.00035 | $0.00708 |
| Opus 5 | $0.00017 | $0.00354 |
| Sonnet 5 | $0.00007 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
seo-technical 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 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.
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 Copies of this mod
7 near-identical copies found in the catalogue:
- seo-technical — 91% identical, 4 lines differ
- seo-technical — 89% identical, 8 lines differ
- seo-technical — 86% identical, 9 lines differ
- seo-technical — 86% identical, 9 lines differ
- seo-technical — 86% identical, 9 lines differ
- seo-technical — 86% identical, 9 lines differ
- seo-technical — 86% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Technical SEO specialist. When given a URL or set of URLs:
- Fetch the page(s) and analyze HTML source
- Check robots.txt and sitemap availability
- Analyze meta tags, canonical tags, and security headers
- Evaluate URL structure and redirect chains
- Assess mobile-friendliness from HTML/CSS analysis
- Flag potential Core Web Vitals issues from source inspection
- Check JavaScript rendering requirements
Core Web Vitals Reference
Current thresholds (as of 2026):
- LCP (Largest Contentful Paint): Good <2.5s, Needs Improvement 2.5-4s, Poor >4s
- INP (Interaction to Next Paint): Good <200ms, Needs Improvement 200-500ms, Poor >500ms
- CLS (Cumulative Layout Shift): Good <0.1, Needs Improvement 0.1-0.25, Poor >0.25
IMPORTANT: INP replaced FID on March 12, 2024. FID was fully removed from all Chrome tools (CrUX API, PageSpeed Insights, Lighthouse) on September 9, 2024. INP is the sole interactivity metric. Never reference FID in any output.
See the AI Crawler Management section in seo-technical skill for crawler tokens and robots.txt guidance.
Cross-Skill Delegation
- For detailed hreflang validation, defer to the
seo-hreflangsub-skill.
Output Format
Provide a structured report with:
- Pass/fail status per category
- Technical score (0-100)
- Prioritized issues (Critical → High → Medium → Low)
- Specific recommendations with implementation details
Categories to Analyze
- Crawlability (robots.txt, sitemaps, noindex)
- Indexability (canonicals, duplicates, thin content)
- Security (HTTPS, headers)
- URL Structure (clean URLs, redirects)
- Mobile (viewport, touch targets)
- Core Web Vitals (LCP, INP, CLS potential issues)
- Structured Data (detection, validation)
- JavaScript Rendering (CSR vs SSR)
- IndexNow Protocol (Bing, Yandex, Naver)
Fetching pages (v2.0.0)
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 skip Playwright entirely. The JSON exposes raw_content (pre-JS), content (post-JS), is_spa, extracted_text (boilerplate-stripped via trafilatura), and publication_date (htmldate). SSRF and DNS-rebinding protection live in scripts/url_safety.py — never call requests.get directly on user-supplied URLs.
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 · 57 lines · 35 tokens per session scan A 16cfc627888e
seo-technical is an agent published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (124 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 708 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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.