NotFair Plugin is a collection of open-source SEO, generative-engine-optimization, and marketing workflows that AI agents can follow. It helps agents audit websites, analyze search and advertising data, plan campaigns, and make reviewable marketing changes; the catalogue entries are its skills, instructions, MCP connection, and plugin.
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 agentmods add skills/nowork-studio/notfair-plugin/landingnpx skills add nowork-studio/notfair-plugin --skill landinggit clone --depth 1 https://github.com/nowork-studio/notfair-pluginWrote 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/nowork-studio/notfair-plugin/landing)<a href="https://agentmods.dev/skills/nowork-studio/notfair-plugin/landing"><img src="https://agentmods.dev/badge/skills/nowork-studio/notfair-plugin/landing.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 | $0.00121 | $0.02602 |
| Opus 5 | $0.00060 | $0.01301 |
| Sonnet 5 | $0.00024 | $0.00520 |
| Haiku 4.5 | $0.00012 | $0.00260 |
Grade A, and why
google-ads-landing 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 6d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setup
Read and follow ../shared/preamble.md (MCP detection, account selection) and ../shared/analysis-principles.md (evidence requirement, guardrails). Both apply throughout this skill — every dimension below is a measurement, not an opinion.
Landing Page Scoring + Diagnostic
Google Ads campaigns fail on the landing page more often than in the auction. A great RSA that sends traffic to a slow, unfocused, or mismatched page burns budget twice — once on the click, once on the lost conversion. This skill scores landing pages on 5 weighted dimensions and emits concrete fixes.
Only score pages that actually run ad traffic. Don't score random marketing pages. Run this on direct request, on auto-handoff from /google-ads-audit (high-CTR / low-CVR ad groups), when QS diagnosis flags "Landing Page Experience: Below Average", or as a preflight before /google-ads-copy writes new copy for a page nobody's validated.
When the question is about ad-to-page fit, high CTR / low CVR, LPX, or testing ads and landing pages together, read references/message-chain-testing.md before scoring. It keeps the diagnosis focused on the paid-search message chain instead of drifting into a generic web-design audit.
Reference
references/scoring-rubric.md— the 5-dimension weighted rubric, thresholds, and evidence fields. Read before scoring.references/message-chain-testing.md— query → ad → page message-chain diagnosis and ad+LP test design.../manage/references/quality-score-framework.md— only when the user's explicit goal is QS improvement.
Phase 1: Resolve the target pages
Figure out which URLs to score. In priority order:
- User supplied a URL — score that page, skip discovery.
- User supplied an ad group or campaign name —
runScripta GAQL query againstad_group_adfiltered to that ad group; extract uniquefinal_urls. Normalize (strip tracking params, preserve path + query that affects routing). - Auto-handoff from
/google-ads-audit— the handoff passes the specific ad groups flagged. Pull their final URLs the same way. - No arguments —
runScriptanad_group_adquery across the account ranking final URLs by last-30-day spend, propose the top 3, ask the user to confirm.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 159 lines · 121 tokens per session scan A d0cea7b2cc6a
google-ads-landing is a skill published in the GitHub repository nowork-studio/notfair-plugin (3,508 stars, last pushed today), licensed MIT. It adds 121 tokens to every session and 2,602 once invoked, about $0.0006 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.
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