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-ranklensgit 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-ranklens)<a href="https://agentmods.dev/skills/seovendorco/sv-mcp-skills/sv-ranklens"><img src="https://agentmods.dev/badge/skills/seovendorco/sv-mcp-skills/sv-ranklens/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-ranklens"><img src="https://agentmods.dev/badge/skills/seovendorco/sv-mcp-skills/sv-ranklens.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 | $0.00048 | $0.00701 |
| Opus 5.5 | $0.00019 | $0.00280 |
| Sonnet 5.5 | $0.00010 | $0.00140 |
| Haiku 4.5 | $0.00005 | $0.00070 |
Grade A, and why
sv-ranklens 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.
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.
SV RankLens: ranklens
MCP tool: ranklens · Mode: synchronous
API reference: RankLens
When to use this
Use when the question is about brand-level AI visibility, and (with the competitors action) how it compares to the competitors that show up in those AI answers. For a deep domain audit use sv-geo-audit.
Parameters
| Parameter | Required | Notes |
|---|---|---|
entity |
yes | The search term, 4 to 80 characters. Not keyword. |
web |
yes | Website URL, max 300 characters. url is accepted as a fallback. |
action |
no | rank (default) or competitors. |
mgptid |
for competitors |
The mgptid from a prior rank result. Not used for rank. |
brand |
no | Max 60 characters. Pass it when the brand cannot be worked out from the domain. |
engine |
no | AI engine ID, 0 to 26. Several have a "Latest" alias. |
samplesize |
no | 0 = default, 1 to 4 = 2x, 4x, 8x, 16x sampling. More samples cost more and give more confidence. |
brandmatch |
no | Comparison angle, 0 to 182. |
location |
no | Max 100 characters. |
language |
no | 0 to 39. 0 = US English. |
Example calls
Rank:
{ "entity": "white label seo", "web": "https://example.com", "brand": "Acme SEO" }
Competitors, using the mgptid from the rank result:
{ "action": "competitors", "entity": "white label seo", "web": "https://example.com", "mgptid": "<mgptid from the rank result>" }
Result
rank returns one ranking data point per sample, including average, low and high rank, confidence, probability and a rank score, plus an mgptid. competitors returns the competitors that appeared in those samples. Rows use the field name entity.
Chaining
Often paired with sv-geo-audit. The two-step competitor flow is rank, take mgptid from the result, then competitors with that mgptid.
Gotchas
- The search term goes in
entity, notkeyword. competitorsfails without a validmgptidfrom an earlierrankcall. No raw call is needed, it is an ordinary parameter.brandmatchhas 183 options. If the user's request does not clearly match one, ask which they want instead of guessing an ID.
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 · 57 lines · 48 tokens per session scan A 78e46ff92c21
sv-ranklens is a skill published in the GitHub repository seovendorco/sv-mcp-skills (2 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 701 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.
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