Comprehensive 80-item content quality audit across CORE-EEAT dimensions with prioritized improvement plan.
Works with
Evaluates content against 8 dimensions: Contextual Clarity, Organization, Referenceability, Exclusivity (CORE, GEO-focused) plus Experience, Expertise, Authoritativeness, Trust (EEAT, SEO-focused)
Produces GEO Score, SEO Score, content-type weighted total, per-item pass/partial/fail ratings, and veto item checks for critical trust violations
Generates Top 5 prioritized improveme
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncontent-quality-auditorExecute the skills CLI command in your project's root directory to begin installation:
Fetches content-quality-auditor from aaron-he-zhu/seo-geo-claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate content-quality-auditor. Access via /content-quality-auditor in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Based on CORE-EEAT Content Benchmark. Full benchmark reference: references/core-eeat-benchmark.md
SEO & GEO Skills Library · 20 skills for SEO + GEO · ClawHub · skills.sh System Mode: This cross-cutting skill is part of the protocol layer and follows the shared Skill Contract and State Model.
This skill evaluates content quality across 80 standardized criteria organized in 8 dimensions. It produces a comprehensive audit report with per-item scoring, dimension and system scores, weighted totals by content type, and a prioritized action plan.
System role: Publish Readiness Gate. It decides whether content is ready to ship, what blocks publication, and what should be promoted into durable project memory.
Use this when content needs a quality check before publishing — even if the user doesn't use audit terminology:
Start with one of these prompts. Finish with a publish verdict and a handoff summary using the repository format in Skill Contract.
Audit this content against CORE-EEAT: [content text or URL]
Run a content quality audit on [URL] as a [content type]
CORE-EEAT audit for this product review: [content]
Score this how-to guide against the 80-item benchmark: [content]
Audit my content vs competitor: [your content] vs [competitor content]
Gate verdict: SHIP (no veto items, dimension scores above threshold) / FIX (issues found but no veto) / BLOCK (veto item T04, C01, or R10 failed). Always state the verdict prominently at the top of the report.
Expected output: a CORE-EEAT audit report, a publish-readiness verdict, and a short handoff summary ready for memory/audits/content/.
memory/audits/content/.memory/hot-cache.md (auto-saved, no user confirmation needed). Top improvement priorities to memory/open-loops.md.Next Best Skill below once the verdict is clear.See CONNECTORS.md for tool category placeholders.
With ~~web crawler + ~~SEO tool connected: Automatically fetch page content, extract HTML structure, check schema markup, verify internal/external links, and pull competitor content for comparison.
With manual data only: Ask the user to provide:
Proceed with the full 80-item audit using provided data. Note in the output which items could not be fully evaluated due to missing access (e.g., backlink data, schema markup, site-level signals).
When stopping to ask, always: (1) state the specific value and threshold, (2) offer numbered options with outcomes.
Stop and ask the user when:
Continue silently (never stop for):
When a user requests a content quality audit:
### Audit Setup
**Content**: [title or URL]
**Content Type**: [auto-detected or user-specified]
**Dimension Weights**: [loaded from content-type weight table]
#### Veto Check (Emergency Brake)
| Veto Item | Status | Action |
|-----------|--------|--------|
| T04: Disclosure Statements | ✅ Pass / ⚠️ VETO | [If VETO: "Add disclosure banner at page top immediately"] |
| C01: Intent Alignment | ✅ Pass / ⚠️ VETO | [If VETO: "Rewrite title and first paragraph"] |
| R10: Content Consistency | ✅ Pass / ⚠️ VETO | [If VETO: "Verify all data before publishing"] |
If any veto item triggers, flag it prominently at the top of the report and recommend immediate action before continuing the full audit.
Evaluate each item against the criteria in references/core-eeat-benchmark.md.
Score each item:
### C — Contextual Clarity
| ID | Check Item | Score | Notes |
|----|-----------|-------|-------|
| C01 | Intent Alignment | Pass/Partial/Fail | [specific observation] |
| C02 | Direct Answer | Pass/Partial/Fail | [specific observation] |
| ... | ... | ... | ... |
| C10 | Semantic Closure | Pass/Partial/Fail | [specific observation] |
**C Score**: [X]/100
Repeat the same table format for O (Organization), R (Referenceability), and E (Exclusivity), scoring all 10 items per dimension.
### Exp — Experience
| ID | Check Item | Score | Notes |
|----|-----------|-------|-------|
| Exp01 | First-Person Narrative | Pass/Partial/Fail | [specific observation] |
| ... | ... | ... | ... |
**Exp Score**: [X]/100
Repeat the same table format for Ept (Expertise), A (Authority), and T (Trust), scoring all 10 items per dimension.
See references/item-reference.md for the complete 80-item ID lookup table and site-level item handling notes.
Calculate scores and generate the final report:
## CORE-EEAT Audit Report
### Overview
- **Content**: [title]
- **Content Type**: [type]
- **Audit Date**: [date]
- **Total Score**: [score]/100 ([rating])
- **GEO Score**: [score]/100 | **SEO Score**: [score]/100
- **Veto Status**: ✅ No triggers / ⚠️ [item] triggered
### Dimension Scores
| Dimension | Score | Rating | Weight | Weighted |
|-----------|-------|--------|--------|----------|
| C — Contextual Clarity | [X]/100 Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Marketingsame categoryReviews
4.7★★★★★55 reviews- NNoah Jackson★★★★★Dec 20, 2024
I recommend content-quality-auditor for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- AAma Park★★★★★Dec 16, 2024
content-quality-auditor reduced setup friction for our internal harness; good balance of opinion and flexibility.
- XXiao Torres★★★★★Dec 16, 2024
Keeps context tight: content-quality-auditor is the kind of skill you can hand to a new teammate without a long onboarding doc.
- SSoo Farah★★★★★Dec 4, 2024
Useful defaults in content-quality-auditor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- AAditi Patel★★★★★Nov 23, 2024
I recommend content-quality-auditor for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- VValentina Chen★★★★★Nov 11, 2024
Useful defaults in content-quality-auditor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- SSoo Nasser★★★★★Nov 7, 2024
content-quality-auditor has been reliable in day-to-day use. Documentation quality is above average for community skills.
- JJin Abebe★★★★★Nov 7, 2024
Registry listing for content-quality-auditor matched our evaluation — installs cleanly and behaves as described in the markdown.
- AAditi Sethi★★★★★Oct 26, 2024
content-quality-auditor fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- MMateo Malhotra★★★★★Oct 26, 2024
Useful defaults in content-quality-auditor — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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