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.
npx skills add seovendorco/sv-mcp-skills --skill sv-core-analysisgit clone --depth 1 https://github.com/seovendorco/sv-mcp-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/skills/seovendorco/sv-mcp-skills/sv-core-analysis)<a href="https://agentmods.dev/skills/seovendorco/sv-mcp-skills/sv-core-analysis"><img src="https://agentmods.dev/badge/skills/seovendorco/sv-mcp-skills/sv-core-analysis/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/skills/seovendorco/sv-mcp-skills/sv-core-analysis"><img src="https://agentmods.dev/badge/skills/seovendorco/sv-mcp-skills/sv-core-analysis.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 | $0.00059 | $0.00393 |
| Opus 5.5 | $0.00024 | $0.00157 |
| Sonnet 5.5 | $0.00012 | $0.00079 |
| Haiku 4.5 | $0.00006 | $0.00039 |
Grade A, and why
sv-core-analysis 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 today.
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.
What it actually says
SV CORE Analysis: core-analysis
MCP tool: core-analysis · Mode: synchronous
API reference: CORE Analysis
When to use this
Use after content is drafted or published, to benchmark it against the current top-ranked pages for a keyword. For a single overall health score use sv-preliminary-audit instead.
Parameters
| Parameter | Required | Notes |
|---|---|---|
url |
no | Website URL, max 300 characters. |
keyword |
no | Max 100 characters. |
Neither is marked required in the schema, but a useful analysis needs both.
Example call
{ "url": "https://example.com/white-label-seo", "keyword": "white label seo" }
Result
A dense, nested report: HTTP status and load speed, URL and SSL checks, a keyword uniqueness score, the title, meta description and heading structure with counts and term frequency, indexing, sitemap and robots.txt status, and the top-ranked sites for the keyword.
Chaining
Pairs with sv-top-competitors (who to benchmark against) and usually runs after sv-prose to check a fresh draft before publishing.
Gotchas
- This is the deepest result in the set. When summarizing for a person, pull out the scores and the top-ranked sites that drive a decision, and do not dump the raw structure.
- An empty call returns an error or a near-empty report.
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.
- today First seen · 42 lines · 59 tokens per session scan A 0ef8d5f461d5
sv-core-analysis is a skill published in the GitHub repository seovendorco/sv-mcp-skills (2 stars, last pushed yesterday), licensed MIT. It adds 59 tokens to every session and 393 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-16.
Other skills, from other repositories
linkedin-post
Draft a daily LinkedIn post for S M Moshiur Rahman about business development, grounded in a real problem he hit that day and real numbers from OmniRank. Use when he says "linkedin post", "today's post", "draft a post", or describes friction he wants written up.
audit
Use when asked to audit a site's SEO, check AEO or answer-engine readiness, diagnose why a page is not ranking or not being cited by AI, verify structured data, or run pre-deploy discoverability checks on built HTML.
geo-artifacts
Use when asked to generate or fix llms.txt, llms-full.txt or facts.json, make a site citable or ingestible by ChatGPT, Claude, Perplexity or Gemini, or publish machine-readable ground truth for AI crawlers.
extract-source-sample
Given the path to a finished content-goose ad-run folder, extract everything that defines that ad — recipe shot list, VO script, characters, voices, world, atom-skills, master mp4 — and emit a source-sample.json in the exact shape the upload-ad-sample skill writes to the Goose Ads library. Also links every character…
create-video-seedance-2-fal
Generate a single 4-15s vertical video clip with ByteDance Seedance 2.0 reference-to-video via fal.ai. Multi-image reference (avatar + product + setting), native lip-synced VO + ambient audio (generate-audio on by default), internal multi-cut handling within one render. Routes through the GooseWorks FAL proxy (bills…
comprehensive-enrichment
Enrich any person or company from any identifier — email, name, LinkedIn URL, domain, company name, Twitter/X handle. Use when asked to enrich, look up, or research a lead, contact, person, or company.