What does this skill do, and when should you use it?
This is the content-analysis skill inside the claude-seo repository, focused on content quality, E-E-A-T, and AI citation readiness. It applies Google’s “Who / How / Why” heuristic and examines authorship, process disclosure, expertise, authority, trust, and people-first value. It also reviews topical coverage, readability, keywords, structure, links, multimedia, freshness, and signals of low-quality AI-generated content. It fits SEO consultants, content teams, and site operators who need page-level content audits, but it should not be treated as the repository’s full technical SEO, schema, or local SEO suite.
Analyzes a page URL for bylines and author credentials, process disclosure, first-hand evidence, citations, contact information, HTTPS, dates, corrections, and other E-E-A-T signals; evaluates topical word-count floors, Flesch readability, sentence and paragraph length, keyword placement, heading hierarchy, image alt text, internal links, and external citations; flags markers of low-quality AI content and assesses whether the page contains clear, structured, attributable material that AI search systems can quote; produces a Content Quality Score, E-E-A-T subscores, an AI Citation Readiness score, issues, and recommendations. Optional DataForSEO MCP tools can provide keyword volume, difficulty, intent, and content-analysis data.
- An SEO consultant audits a client’s blog or service page before an engagement and needs to find thin content, E-E-A-T gaps, and structural issues.
- A content lead refreshes an aging article and needs to review readability, keyword coverage, citations, authorship, and freshness.
- A YMYL publisher reviews author credentials, first-hand evidence, transparency, and trust signals.
- A marketing team wants stronger visibility in ChatGPT, Perplexity, and Google AI search and needs to assess answer structure, data, attribution, and schema signals.
How do you install this skill?
- Treat word-count, keyword-density, and Flesch thresholds as supporting heuristics, not direct Google ranking criteria.
- The skill contains time-sensitive 2025/2026 search and AI-platform claims; verify them against the relevant official sources and referenced files before use.
- Before enabling DataForSEO or other external extensions, confirm credential scope, whether data leaves the machine, service availability, and cost.
- The publisher is not verified by the FollowSkills enterprise registry; treat identity as unknown and independently review provenance and maintenance status.
- Network access
- Local filesystem
- MCP Server
The skill is distributed as part of the AgriciDaniel/claude-seo collection. In Claude Code, install the plugin with /plugin marketplace add AgriciDaniel/claude-seo, followed by /plugin install claude-seo@agricidaniel-claude-seo. For a manual installation, run git clone --depth 1 https://github.com/AgriciDaniel/claude-seo.git, then bash claude-seo/install.sh. The source does not document a separate installation process for skills/seo-content/SKILL.md.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/AgriciDaniel/claude-seo.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/seo-content" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
In Claude Code, run /seo content https://example.com/article, or use a trigger such as “content quality,” “E-E-A-T,” “content analysis,” “readability check,” “thin content,” or “content audit.” For the optional FLOW workflow, the documented commands are /seo flow optimize <url> and /seo flow win <url>.
What are this skill's strengths and limitations?
- Covers all four E-E-A-T dimensions: Experience, Expertise, Authoritativeness, and Trustworthiness.
- Combines content coverage, readability, structure, linking, multimedia, AI-content assessment, and AI citation readiness.
- Explicitly distinguishes content-quality indicators from direct Google ranking factors, including word count and Flesch scores.
- Defines handling for unreachable URLs, paywalls, login walls, and pages with fewer than 100 retrievable words.
- It requires access to the target page; blocked URLs, gated content, or interaction-dependent pages can limit the analysis.
- The full E-E-A-T criteria reference `skills/seo/references/eeat-framework.md`, so copying only the single file may omit supporting material.
- DataForSEO enrichment is optional and requires the corresponding MCP tools; the skill does not itself provide live keyword data.
- The source provides no independent test coverage or client-specific validation for this individual skill.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| SEO Content Quality Audit this page | 52 · Use with care | ★ 19k | 23d ago | MIT |
| On-Page SEO Checker | 54 · Use with care | ★ 3k | 13d ago | Apache-2.0 |
| Blog GEO Citation Audit | 55 · Use with care | ★ 2.4k | 13d ago | MIT |
| Blog Cluster Engine | 53 · Use with care | ★ 2.4k | 13d ago | MIT |
| SEO Content Brief Generator | 52 · Use with care | ★ 19k | 23d ago | MIT |
How did FollowSkills review this skill?
The skill is primarily a URL content-audit workflow and defines handling for unreachable URLs, paywalls, and thin content; repository context also shows SSRF controls, credential file protection, isolation, and a security policy. Points are deducted because the skill itself does not clearly disclose data flows, network scope, user confirmation, external side effects, or rollback, and optional DataForSEO-style integrations lack detailed permission boundaries.
The skill provides a reasonably coherent audit flow, output structure, and abnormal-input handling, while repository CI and content-quality tests offer partial static support. Points are deducted because the target skill path is not end-to-end reproduced, referenced shared materials and sub-skills were not verified here, and several 2025/2026 claims and thresholds may become stale.
Invocation triggers are clear, with a defined audience for content quality, E-E-A-T, readability, and AI-citation analysis, and the expected report sections are specified. Points are deducted because non-fit cases, input prerequisites, language support, and regional network reachability are underdocumented; some AI-search and optional data capabilities depend on external services without mainland-China availability guidance.
The file includes name, description, version, MIT licensing, argument hint, layered sections, an output template, and an error-handling table; repository context adds versioning, CI, security policy, and maintenance signals. Points are deducted because the file lacks a changelog, explicit maintenance owner and update path, dependency installation notes, FAQ, and a clear guarantee that referenced materials are available.
The skill covers Who/How/Why, E-E-A-T, structure, linking, AI-citation readiness, freshness, and directly usable report sections for scores, issues, and recommendations. Points are deducted because static material does not verify a complete content-audit result, some rules such as word count, keyword density, and readability thresholds require judgment, and outputs may need substantial human review.
The skill cites Google's official Helpful Content documentation and distinguishes several heuristics from direct ranking factors; repository context includes related content-quality tests and CI. Points are deducted because key judgments lack itemized sourcing, representative input/output evidence, and independent corroboration, while static review cannot establish actual correctness.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →