Eleven free Claude skills that run a LinkedIn account: posts off 21 hook formulas, comments, replies, profile score, weekly plan, and a humanizer that strips the AI fingerprint and scores the draft before it goes out.
# Add to your Claude Code skills
git clone https://github.com/Jakeschincariol/linkedin-agent-skillGuides for using ai agents skills like linkedin-agent-skill.
Last scanned: 9/10/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-09-10T08:40:50.357Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}See how linkedin-agent-skill compares with popular alternatives.
linkedin-agent-skill is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by Jakeschincariol. Eleven free Claude skills that run a LinkedIn account: posts off 21 hook formulas, comments, replies, profile score, weekly plan, and a humanizer that strips the AI fingerprint and scores the draft before it goes out. It has 1,279 GitHub stars.
Yes. linkedin-agent-skill passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/Jakeschincariol/linkedin-agent-skill" and add it to your Claude Code skills directory (see the Installation section above).
linkedin-agent-skill is primarily written in Python. It is open-source under Jakeschincariol on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh linkedin-agent-skill against similar tools.
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Eleven Claude skills that run a LinkedIn account. Free, MIT, no signup, no API key, nothing to connect.
One of them writes your posts off 21 hook formulas. One comments on other people's posts. One handles the replies under yours. One scores your profile out of 100 and rewrites what lost points. One plans the week: what to post, when, and who to engage with.
And one is the humanizer, which is the reason the rest are usable. It strips the em dashes, the slop vocabulary and the invisible watermark characters out of a draft, then scores what is left against a five-check detection panel before you ever see it.
Nothing gets posted until you say yes. These skills write. You post.
Paste this into Claude:
https://github.com/Jakeschincariol/linkedin-agent-skill
Install this skill, then confirm /li-post works.
Or do it yourself, in Claude Code:
git clone https://github.com/Jakeschincariol/linkedin-agent-skill.git
cp -r linkedin-agent-skill/skills/li-* ~/.claude/skills/
Or as a plugin:
/plugin marketplace add Jakeschincariol/linkedin-agent-skill
/plugin install linkedin-agent
Project-local instead of global: copy the same folders into your repo's
.claude/skills/. No Claude Code at all? Paste any single SKILL.md at the
top of a chat and it runs as a mode - you lose the two Python tools, which is
most of the point of /li-human, but the rest works.
Then spend ten minutes on templates/voice.md. Copy it to
~/.claude/linkedin/voice.md and fill it in, or paste three of your own posts
into Claude and say "write my voice.md from these". Every skill reads that
file. Skip it and everything comes out sounding like everyone else.
| command | what it does |
|---|---|
/li-post |
One idea into a post. Three hook options from 21 formulas, one full draft, humanized before you see it. |
/li-comment |
Comments on other people's posts. Nine types, picked by what the post actually is. Never "Great post!". |
/li-reply |
The thread under your own post. Sorts every comment into lead / substance / peer / support / noise, then writes in that order. |
/li-profile |
Scores your profile against a 12-part rubric out of 100, then rewrites in fix-first order. |
/li-plan |
The week. What to post, when to post it, and the 10 people to engage with. Writes ~/.claude/linkedin/plan.md. |
/li-human |
The humanizer. Two scripts that actually run. See below. |
/li-carousel |
Document posts. Slide-by-slide copy, the cover that earns the swipe, and the PDF to upload. |
/li-repurpose |
One video, newsletter or transcript into a week of posts that each stand alone. |
/li-dm |
The 200-character invite note, the first message, and the two follow-ups. Two. |
/li-inbox |
Triages the inbox into lead / recruiter / peer / ask / spam, and tells you which tell gave the sequence away. |
/li-audit |
Post-mortem on what you have already published. Ranks by engagement rate and reach multiple, not impressions. |
/li-human ships two Python scripts with no dependencies. They run on your
machine, on your text, and nothing is uploaded.
python3 humanize.py draft.txt --report # clean it, show every change
python3 detect.py draft.txt # score it, five checks
python3 detect.py before.txt after.txt # prove the delta
What comes out automatically:
slop.json and it is meant to be edited.What gets flagged instead of fixed: "It's not just X, it's Y", rule-of-three triads, one-word rhetorical questions, hashtag walls, reflex engagement bait, uniform sentence length. Changing the shape of a sentence needs judgement, so those are handed back for a rewrite rather than mangled by a regex.
The five checks, scored 0-100, higher is more human:
| check | what it measures |
|---|---|
| BURSTINESS | sentence-length variation. Models write even. |
| SPECIFICITY | numbers, names and concrete markers per 100 words |
| SLOP DENSITY | lexicon hits per 100 words |
| FINGERPRINT | invisible characters, em dashes, curly quotes per 1,000 |
| VOICE | contractions, person, structural tells |
The verdict weights the mean at 60% and the weakest single check at 40%, because a detector only needs one signal to fire.
Run against a deliberately terrible draft:
BURSTINESS ##################...... 73.0
SPECIFICITY ######################## 100.0
SLOP DENSITY ........................ 0.0 19 stock terms, 24.1 per 100 words
FINGERPRINT ........................ 0.0 1 invisible, 1 em dash, 3 curly quote
VOICE ########................ 33.3 3 structural tells
------------------------------------------------------------
HUMAN SCORE ######.................. 24.8 FLAGGED
After humanize.py, with the flagged structures still unrewritten:
HUMAN SCORE #################....... 69.7 REVIEW (+44.9)
The last stretch to PASS is the part the script deliberately leaves to you.
These skills do not post to LinkedIn, and they should not. There is no official API for posting to a personal profile without an approved partner app, and automating the site with a browser or a third-party tool violates LinkedIn's User Agreement and gets accounts restricted. So every skill here ends the same way: a copy-ready block, and you paste it. That is not a limitation bolted on afterwards, it is the design. It is also why the approval gate is real rather than a setting.
The five checks are local heuristics, not detector APIs. They are modelled on the signals public detectors key on, and they run entirely on your machine. They are not GPTZero, Originality, Copyleaks, Winston or Turnitin, they do not call those services, and they cannot promise those verdicts. Fixing what they measure tends to move those numbers, because they are measuring the same underlying things. That is the whole claim. Nobody can honestly sell you "undetectable", and anybody who does is selling you something.
The invisible-character pass is real and it is narrow. It removes the zero-width and format characters that end up in generated text and survive a copy-paste. That is a genuine, checkable fingerprint. It is not a claim about defeating a cryptographic watermarking scheme, and this repo does not make one.
Nothing here fabricates. No invented metrics, clients or outcomes go under
your name. If a draft needs a number you have not given, it comes back with
{{your number}} in it and a flag, every time.
skills/li-post/hooks.json 21 hook formulas: template, example, what it is for, how it gets ruined
skills/li-human/slop.json the lexicon: 113 terms, 17 invisible classes, 11 structural tells
skills/li-human/humanize.py the three cleaning passes
skills/li-human/detect.py the five-check panel
skills/li-profile/rubric.json the 100-point profile score
templates/voice.md your voice profile. Fill this in first.
Made by Jake Schincariol, opusjake.ai. The full write-up is at opusjake.ai/r/linkedin-agent.
MIT. Take it, change it, ship it.