seo-article
Skill4 starsChanged 7 months agoMITView on GitHub
Write an SEO-optimized technical blog post or article. Invoke with topic, target keywords, and optional word count. Auto-invoked when user says "write article", "blog post about", "write content about".
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 9
Write an SEO-optimized article right now:
- Distinctive: line 15
Write the full article (1500-2500 words) with this structure:
- Distinctive: line 22
Follow SEO rules: keyword density 1-1.5%, short paragraphs, bullet points, internal/external link suggestions
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Write an SEO-optimized article right now:
Topic and keywords: $ARGUMENTS
Steps:
- Define primary keyword and 3-5 secondary keywords
- Write the full article (1500-2500 words) with this structure:
- Title (H1): Include primary keyword, under 60 chars
- Meta description: 150-155 chars with keyword and benefit
- Intro: Hook with surprising stat or contrarian take
- H2/H3 sections: Logical flow, keyword-rich headings
- Code examples where relevant
- Conclusion: Key takeaways + CTA
- Follow SEO rules: keyword density 1-1.5%, short paragraphs, bullet points, internal/external link suggestions
Output the full article in markdown, followed by:
- Meta title
- Meta description
- URL slug
- Target keywords
- Word count
- Heading count
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- AI-XLabs-Innovation/launchranked-agent★ 0same name
- yori44/claude-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository31 other files it gives its agents.
- .claude/CLAUDE.mdCLAUDE.md
- CLAUDE.mdCLAUDE.md
- global/CLAUDE.mdCLAUDE.md
- auditSkill
- case-studySkill
- ci-commentSkill
- cold-outreachSkill
- daily-briefingSkill
- dep-checkSkill
- github-readmeSkill
- hub-statusSkill
- invoice-genSkill
- kronus-helpSkill
- kronusSkill
- kronus-troubleshootSkill
- lead-qualifySkill
- linkedin-postSkill
- manifest-validateSkill
- meeting-notesSkill
- planSkill
- profile-optimizeSkill
- project-summarySkill
- quick-reviewSkill
- reviewSkill
- secret-scanSkill
- standup-updateSkill
- telegram-projectSkill
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- test-runSkill
- testSkill
- upwork-proposalSkill
Does it work?
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

