LinkedIn Humanizer V2 Agent Skill

Scrub AI tells from any text draft OR audit a finished post against the 2026 algorithm heuristic checklist.

Context cost
36.3k tokensestimated from the bundle, loaded when it triggers
Bundle
18 of 19 fileshanded out here; the rest is at its source
Licence
MITthe author’s own terms
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

Remove AI tells from LinkedIn posts/comments: 2026 vocabulary density, reveal bridges, staccato fragments, stacked triads, performed sincerity. Tiered rewriter plus `--mode audit` LinkedIn post auditor and `--mode profile` voice builder. Not for drafting from scratch (use linkedin-post-writer) or beating AI detectors. Keywords: humanize, de-AI, post audit, post auditor, audit before posting.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

social media
Filed under

Sales & CRM

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md19.9 kB · preview
--- name: linkedin-humanizer ---
6# LinkedIn Humanizer V3
7
8Rewrites any text to remove the AI tells that human readers notice and that LinkedIn's "AI slop" filter reacts to. Based on Wikipedia's "Signs of AI writing" taxonomy, the 2025-2026 stylometry literature, and our own length-controlled corpus. **V3 (2026-09):** recalibrated on 2026 evidence. Vocabulary is scored by density, em dashes are capped instead of banned, forced rhythm is now a tell instead of a fix, and there is an over-correction guard.
9
10**What this skill does not do:** it does not make text "pass" GPTZero, Pangram, Turnitin or Originality. Those are trained classifiers keyed on the instruction-tuning style signature; prompt-style "sound like a real person" rewrites are caught 92-95% of the time (VUB IJEI 2026, Russell 2025), and light mechanical rewriting raises detectability (arXiv 2603.17522). No post-hoc edit reliably beats a Pangram-class detector, and detector scores on LinkedIn-length text (100-300 words) are noise. The real value is elsewhere: expert human readers cite vocabulary (53%) and sentence structure (36%) as what gives AI text away, and LinkedIn's July 2026 slop-report button costs a flagged post roughly 40% of its views. This skill removes what those readers and that filter react to.
11
12## What changed in V3
13
14Evidence tier in brackets: [strong] = replicated across 2+ independent 2025-2026 studies or our own length-controlled corpus; [vendor] = single platform or vendor dataset; [weak] = one study or expert-panel report.
15
16- **Vocabulary moved from a delete-list to density scoring.** The 2023-24 words (delve, tapestry, realm, journey) are decaying as humans avoid them [strong: Geng & Trotta 2025]. The durable 2026 markers are common words (significant, crucial, notably, comprehensive, insights, robust, leverage, foster, landscape, nuanced, streamline, elevate) plus grammar: nominalisations and "-ing" clause openers at 5.3x human rate [strong: Kobak Sci Adv 2025; Wu et al 2026; PNAS 2025]. AI vocabulary is also the one marker consistently reach-negative on LinkedIn in our own corpus (0.74-0.84 author-relative) [strong]. One marker in a paragraph is not a verdict. Three or more is.
17- **Em dash is no longer a tell.** GPT-5.4 emits 1.43 per 1,000 words, below the 3.23 human baseline; 29% of human captions and 23% of top-creator LinkedIn posts in our corpus use one (author-relative ratio 1.09) [strong]. Zero em dashes is now its own tell (the writer is trying to look human). New rule: cap at about 1 per 100 words, replace excess with comma, colon, parentheses or a rewrite. Never a period.
18- **Forced burstiness is the #1 2026 tell, not the fix.** LLM sentence-length variance is half of human [strong], but detectors do not score it, mechanical long/short alternation is a learnable humanizer fingerprint [weak: DAMAGE 2025], and on LinkedIn sentence-length variance is not an engagement lever in either direction (our corpus, n=397, within-creator: null to slightly negative) [strong]. "Short. Punchy. Done.", "No X. No Y. Just Z.", one-word paragraphs and "The result?" reveals are the current top tells. Pass 2 is now RHYTHM, not BREAK: fix machine-flat rhythm, never manufacture variance.
19- **Rule of three is still a tell, at density.** Tricolon runs at 2x expert-human rate across 2026 frontier models [strong: arXiv 2604.19768]. Stacked, perfectly parallel triads and 3+ per post get scrubbed. One natural triple stays (26% of top human tweets have one).
20- **Fingerprint injection was half wrong.** Named entities and concreteness are supported [strong: lower entity density in LLM text across 3 studies]; an odd-precision number with a referent in line 1 lifts likes 34% [vendor]. Bare numbers are not a discriminator, and inserted hedges and confessions backfire: performed hesitancy is 2x more common in LLM text than expert human text, and sincerity announcements ("let me be honest") are a named 2026 tell [strong: tropes.fyi false vulnerability; Schilke & Reimann 2025]. Pass 3 now asks for a flat, dated, uncomfortable fact instead.
21- **Over-correction guard.** Humanizer output has its own fingerprint; "writing slightly worse on purpose" now reads as a tell [weak: DAMAGE 2025; slopotron]. Pass 4 checks whether Passes 1-3 introduced the very patterns they were meant to remove. Edits are proportional to real problems. When in doubt, leave it.
22
23See sub-skills/rules-explainer.md for per-rule justification, defenses, and citations, and references/tier-rationale.md §V3 for the evidence.
24
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md2,971 words
Files18
LicenceMIT
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈110
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
36,165
on trigger
The instruction body and 17 supporting files, read only when the skill fires.
18.1%
of a 200k window
Ten skills this size would not fit in the window together.
050k100k150k200k context window

36.3k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

The download carries 18 of the 19 files in its source folder; the list here names 18 files, 158.4 kB on disk. Mostly text — the instructions the model reads — with 1 script in it that your client would run only if the instructions tell it to. The other one is not in the download — listed after these, with why.

  • SKILL.md19.9 kB
  • references/audit-ai-tells.md11.9 kB
  • references/audit-checklist.md4.2 kB
  • references/audit-examples.md0.5 kB
  • references/detector-list.md6.3 kB
  • references/emoji-patterns.md3.8 kB
  • references/examples.md1.4 kB
  • references/rules-explainer.md15.1 kB
  • references/scrub-rules.md27.7 kB
  • references/tier-rationale.md17.7 kB
  • references/voice-fingerprint.md5.4 kB
  • scripts/test_detectors.py13.3 kB
  • sub-skills/detector-tester.md6.1 kB
  • sub-skills/emoji-detector.md4.0 kB
  • sub-skills/illustration.md7.6 kB
  • sub-skills/post-audit.md5.0 kB
  • sub-skills/rules-explainer.md5.4 kB
  • sub-skills/voice-profile.md3.1 kB

Not in the download

Not carried is a type a skill download does not hold. Marked read: its SKILL.md or scripts name the file, so without it the skill works only in part. Take it from its source.

  • scripts/detectors.env.exampleread · not carried
What is not in it

A skill installs nothing and depends on nothing: it is a folder your client reads. This one carries 1 script beside the text, so the download is 18 files — not the whole of its source: 1 file is left out, and the skill reads it. It is listed under the files, with why. The MIT licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# LinkedIn Humanizer V2 · 36.3k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/linkedin-humanizer-v2/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/linkedin-humanizer-v2" ] && { [ ! -e .claude/skills/linkedin-humanizer-v2 ] || { mv .claude/skills/linkedin-humanizer-v2 "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/linkedin-humanizer-v2" .claude/skills/ && echo "Installed .claude/skills/linkedin-humanizer-v2"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add sergebulaev/linkedin-humanizer-v2

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/linkedin-humanizer-v2/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/linkedin-humanizer-v2/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/linkedin-humanizer-v2" ] && { [ ! -e ~/.claude/skills/linkedin-humanizer-v2 ] || { mv ~/.claude/skills/linkedin-humanizer-v2 "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/linkedin-humanizer-v2" ~/.claude/skills/ && echo "Installed ~/.claude/skills/linkedin-humanizer-v2" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/linkedin-humanizer-v2/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where. It appears as linkedin-humanizer in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, linkedin-humanizer-v2. Not in this download: 1 file of its source, which its SKILL.md or scripts read (which one, and why). Take it from its source.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Versionnot versioned
Publishedno release date on file
PriceFree
Referencesergebulaev/linkedin-humanizer-v2

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

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