A score for the visible sample
Every workflow starts with the posts already on screen, whether you are checking one post or auditing the full feed.
LinkedIn workflows
Pick the LinkedIn check you actually need.
SlopScore is a LinkedIn post analyzer and feed audit. Use this page when you want the shortest path from a specific LinkedIn question to the right workflow inside the product.
What is SlopScore?
SlopScore scores the visible posts on screen, shows the repeated patterns behind the result, and lets you save or share what you found. The workflow stays tied to real examples instead of broad claims.
What you will see
The output stays simple across every workflow.
Reasons behind the result
SlopScore shows the repeated patterns behind the read so the output is easier to trust and explain.
A saved example or report link
You can keep the capture as context, compare it against history, or share a clean report instead of a screenshot.
Workflows
Start with the job you are actually trying to do.
Each page targets a different LinkedIn question, but the product boundary stays the same: visible posts, explainable reasons, and output you can keep.
LinkedIn post analyzer
LinkedIn Post Analyzer
The fastest way to review one post without turning the conversation into a screenshot debate.
- A score for the visible post
- Reasons behind the result
- A saved example or report link
LinkedIn Feed Audit
A simple timeline-level workflow for checking what LinkedIn is rewarding in the feed right now.
- A score for the visible feed
- Reasons behind the result
- A saved example or report link
AI LinkedIn Post Detector
A practical AI-signal workflow for people who want visible reasons instead of a fake yes-or-no answer.
- A score for the visible post
- Reasons behind the result
- A saved example or report link
LinkedIn Engagement Bait Detector
A detector for comment bait, reaction-hunting prompts, and other LinkedIn structures that are built to game engagement.
- A score for the visible sample
- Reasons behind the result
- A saved example or report link
LinkedIn Personal Brand Audit
A history-based workflow for reviewing how a LinkedIn posting style changes across repeated captures.
- A score across saved captures
- Reasons behind the drift
- A saved example or report link
Browse by signal
Already know what feels off? Start with the signal guide.
These pages explain the repeatable patterns behind templated LinkedIn writing and connect them back to the right SlopScore workflow.
AI vocabulary on LinkedIn
AI Vocabulary on LinkedIn
A guide to the polished, slightly generic wording that often makes LinkedIn posts feel AI-assisted before the structure gives it away.
- AI-flavored vocabulary
- Copula avoidance
- Filler phrase stacking
Template Hooks on LinkedIn
A guide to “here’s what works,” “I gave ChatGPT…,” and other reusable hook structures that make LinkedIn posts feel templated before the detail arrives.
- Template hook structure
- Instructional tease
- Negative parallelism
Engagement-Bait CTA on LinkedIn
A guide to comment prompts, DM requests, and code-word CTAs that make a LinkedIn post feel engineered for visible reactions.
- Engagement bait CTA
- Instructional tease
- Gated resource offer
Stacked Formatting on LinkedIn
A guide to short-line, em-dash, and bold-heavy formatting that can make LinkedIn posts feel engineered for effect before the point is earned.
- Stacked short-line formatting
- Em-dash overuse
- Boldface overuse
Browse by format
Sometimes the format mix tells the story before the wording does.
These pages are built around real format buckets already stored by SlopScore, so you can compare what the feed looks like before you argue about the wording.
LinkedIn image posts
LinkedIn Image Posts
A format page for understanding when image-heavy LinkedIn posting starts feeling repetitive, over-packaged, or optimized for reaction.
LinkedIn video postsLinkedIn Video Posts
A format page for understanding how video-led posting changes the score, signal mix, and feel of a visible LinkedIn sample.
LinkedIn text postsLinkedIn Text Posts
A format page for understanding how text-first LinkedIn writing picks up repeated language, structure, and bait patterns.
LinkedIn article postsLinkedIn Article Posts
A format page for understanding how article-style LinkedIn posting changes the score, timeline mix, and pattern clusters.
Trust and objections
The useful promise stays narrow and inspectable.
Not a people score
The score stays tied to a post or a visible feed sample, not to a universal label about a person.
Not a magic AI detector
The product highlights pattern clusters that feel synthetic or repetitive. It does not promise a forensic verdict.
Tied to what is on screen
That visible-post boundary is what makes the output easier to inspect, discuss, and share.
Start now
Open the app, score the feed you are seeing, and keep the result.
The right page gets you to the right workflow faster, but the product stays the same underneath: score the visible sample, read the reasons, and save or share the output.