content-ops
Skill1 starsChanged 6 months agoMITView on GitHub
Content quality assurance system that combines expert panel evaluation with automated readability analysis, originality detection, content decay monitoring, and auto-refresh scheduling. Use when: score this content, run readability check on [url/text], check for content decay, generate refresh calendar, extract atoms from [content], run expert panel on [draft].
3 other projects use a version of this.
Adopted by
Most recently taken up by
- LazyIsEfficient/agentic-ostheir own version, first seen Oct 22, 2026
- Th3Circle-app/agent-skillstheir own version, first seen Oct 20, 2026
- matteotitta/claude-marketing-contenttheir own version, first seen Oct 20, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- LazyIsEfficient/agentic-os★ 17same name
- matteotitta/claude-marketing-content★ 0same name
- Th3Circle-app/agent-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 repository12 other files it gives its agents.
- conversion-opsSkill
- creative-opsSkill
- finance-opsSkill
- growth-engineSkill
- outbound-engineSkill
- podcast-opsSkill
- revenue-intelligenceSkill
- sales-pipelineSkill
- sales-playbookSkill
- seo-opsSkill
- team-opsSkill
- voice-guardSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
4
Distinctive
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 12
Run readability check on [URL/text]
- Distinctive: line 13
Check for content decay
- Distinctive: line 16
Run expert panel on [draft]
- Distinctive: line 17
Check brand voice consistency
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Content Ops
Content quality assurance system that combines expert panel evaluation with automated readability analysis, originality detection, content decay monitoring, and auto-refresh scheduling. Ensures every piece of content meets publishing standards before going live and stays fresh after publishing.
When to Use
- Score this content
- Run readability check on [URL/text]
- Check for content decay
- Generate refresh calendar
- Extract atoms from [content]
- Run expert panel on [draft]
- Check brand voice consistency
Workflow
- Ingest Content -- accept draft from any format (Google Doc, Markdown, Notion, raw text)
- Readability Scan -- calculate Flesch-Kincaid + Dale-Chall scores, flag structural issues
- Originality Check -- compare against published content library for overlap
- AI Detection Pass -- scan for AI-generated content patterns, flag for humanization
- Brand Voice Check -- score against voice fingerprint, highlight deviations
- Expert Panel Review -- route to relevant evaluators based on content type and channel
- Iterative Scoring -- panel scores, identifies top 3 weaknesses, content is revised, re-scored
- Publishing Gate -- requires readability pass + originality pass + voice pass + panel score >= 90
- Post-Publish Monitoring -- track performance weekly, flag decay signals monthly
- Refresh Queue -- prioritized backlog of content needing updates, sorted by traffic impact
Reference
Full methodology lives in README.md in this folder: configuration, scoring formulas, output formats, integration points, and worked examples. Read it before producing scored output.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
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.