Use when analyzing published content quality — E-E-A-T scoring, anti-cannibalization, keyword distribution, AI disclosure.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
<objective>
Scores existing content against the E-E-A-T pillars (Experience, Expertise, Authoritativeness, Trustworthiness), runs local keyword-density analysis (`scripts/analyze-keywords.ts`) for distribution and stuffing signals (multi-signal detection — never a fixed >3% density threshold), checks anti-cannibalization (one primary keyword and intent per URL), verifies meta title/H1/heading rules and local semantic distribution, applies 2026 citation-eligible copywriting checks (answer capsule opening every H2, one hyperlinked statistic every 150-200 words, named entities instead of pronouns), and flags AI-content-disclosure requirements. Covers quality of already-written content — for planning a brief before writing, use seo-content-brief; for entity/knowledge-graph optimization, use seo-entity.
</objective>
# Content Quality (E-E-A-T 2026)
## Content Intelligence Workflow
Before content recommendations, run `scripts/analyze-keywords.ts` (local-first, no API key). Use it as first-pass evidence for keyword distribution, semantic breadth, local modifier placement, heading coverage, and stuffing risk.
```bash
…
The line at the top of this page pins the label git:20260729.3b91eed, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
A 17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
pass: Frontmatter block present
pass: Frontmatter declares a name
pass: Frontmatter declares a description
pass: Size between 200 bytes and 200 KB (6392 bytes)
pass: No zero-width or bidi control characters
pass: No instruction hidden inside an HTML comment
pass: No link to an exfiltration or paste host
pass: No credential-shaped string
pass: No instruction to send local credentials anywhere
pass: No text hidden with inline styles
pass: No prompt-injection phrasing
pass: No curl or wget piped into a shell
pass: No recursive delete of root, home or parent
pass: No instruction to read or print local credentials
pass: No base64 blob over 200 characters
pass: No link to a raw IP address
pass: No script tag
Source
GitHub
fusengine/agents · 36 stars · license MIT · pushed 2026-10-30 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ykyg3qbtkzoinnva
GET https://markdownregistry.com/api/v1/resolve?ref=fusengine/agents/seo-content
GET https://markdownregistry.com/api/v1/blob/72cc3d7484b2661df45f39bca659d006a11b28006be151cc584f61c7b279f78f
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
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