linkedin-agent-skill

The LinkedIn agent skill

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.

Install

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.

The eleven

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.

The humanizer

/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:

• Invisible characters. Zero-width spaces and joiners, word joiners, soft hyphens, byte-order marks, Unicode tag characters, non-breaking and narrow spaces. Your keyboard does not make these. They survive copy-paste and they are invisible in every editor you own.
• Typography. Em dash to comma, en dash to hyphen, curly quotes to straight, ellipsis to three dots.
• The lexicon. 113 stock words and phrases with plain-English replacements - delve, leverage, robust, seamless, crucial, testament to, "in today's fast-paced world", "let that sink in" - with capitalisation preserved and URLs untouched. It lives in 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.

The fine print, which is the honest part

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.

Files

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.

Credit

Made by Jake Schincariol, opusjake.ai. The full write-up is at opusjake.ai/r/linkedin-agent.

License

MIT. Take it, change it, ship it.