li-human
Skill2 starsChanged 43 days agoMITView on GitHub
Strip the machine fingerprint out of any draft - em dashes, AI slop words, invisible watermark characters - and score it against a five-check detection panel before it goes out. Use whenever text needs to sound human, when the user says humanize, "does this sound like AI", "remove the em dashes", "de-slop this", "will this get flagged", or before any LinkedIn post, comment, reply, or DM is shown to the user.
8 other projects use a version of this, 8 of them edited.
Adopted by
Most recently taken up by
- furkanbilalakyuz/linkedin-agent-skill-tredited, first seen Nov 8, 2026
- mkmlabs-ai/linkedin-agentedited, first seen Nov 8, 2026
- mubaid/dsh-linkedin-agentedited, first seen Oct 25, 2026
- robbiehaynes/job-searchedited, first seen Oct 20, 2026
- postzen-dev/linkedin-agent-skilledited, first seen Oct 15, 2026
- avi691/linkedin-agent-skilledited, first seen Oct 8, 2026
Other versions8 projects use a version of this.
Word for word · 0
None found.
Edited · 8
- Jakeschincariol/linkedin-agent-skill★ 51438% same
- avi691/linkedin-agent-skill★ 538% same
- mkmlabs-ai/linkedin-agent★ 437% same
- nessalazne/linkedin-agent-skill★ 338% same
- postzen-dev/linkedin-agent-skill★ 338% same
- furkanbilalakyuz/linkedin-agent-skill-tr★ 032% same
- mubaid/dsh-linkedin-agent★ 038% same
- robbiehaynes/job-search★ 038% same
Wrote their own version · 0
None found.
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.
- AGENTS.mdAGENTS.md
- li-auditSkill
- li-carouselSkill
- li-commentSkill
- li-dmSkill
- li-inboxSkill
- li-planSkill
- li-postSkill
- li-profileSkill
- li-replySkill
- li-repurposeSkill
- li-voiceSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.
0
Common
Most of the 8 edited versions we read keep these.
1
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 69
Every other skill in this pack routes its output through here before the user sees it.
“Common” is measured: kept by at least 6 in 10 of the 8 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
li-human
Two tools live in this folder and they both actually run. Use them. Do not eyeball this.
python3 humanize.py draft.txt --report # clean it, show what changed
python3 detect.py draft.txt # score it, five checks
python3 detect.py before.txt after.txt # prove the deltaBoth read slop.json, the lexicon: 100+ stock words and phrases with
plain-English replacements, invisible character classes, typographic
substitutions, and structural tells. It is meant to be edited. If the user
has a word they genuinely use that the lexicon strips, remove it from the
file rather than fighting the tool.
What gets fixed automatically
1. Invisible characters. Zero-width spaces and joiners, word joiners,
soft hyphens, byte-order marks, Unicode tag characters, non-breaking and
narrow spaces. A keyboard does not produce these. They survive copy-paste,
they are invisible in every editor, and they are the single most mechanical
tell in generated text. humanize.py deletes every one, including any
remaining Unicode format character it does not have a name for.
2. Typography. Em dash to comma, en dash to hyphen, curly quotes to
straight, ellipsis to three dots, bullet character to hyphen. The em dash
pass is the one that matters: it collapses — to , and then cleans up
the doubled punctuation that leaves behind.
3. The lexicon. 100+ 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". Capitalisation is
preserved and URLs are never touched. Edit slop.json; it is yours.
What gets flagged instead of fixed
"It's not just X, it's Y" constructions, 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 with a rewrite suggestion rather than mangled by a regex.
The five checks
detect.py scores the draft 0-100 on each check; higher is more human:
| Check | What it measures |
| Burstiness | Whether the cadence varies the way a person's does |
| Specificity | Concrete detail vs. vague abstraction |
| Slop density | Stock AI words and phrases per 100 words |
| Fingerprint | Em dashes, curly quotes, invisible characters, hard spaces |
| Voice | Structural tells: triads, rhetorical questions, engagement bait |
The gate
Every other skill in this pack routes its output through here before the user sees it. The receipt line reports the result:
humanizer: 6 artefacts stripped, human score 84 PASSBelow 60 is a FAIL: rewrite the flagged sentences by hand (the tool shows you which) and re-run. Do not show a FAIL draft to the user as finished. Between 60 and 75 is a warning: show the draft, but name the weakest check. 75 and up ships.
When the agent cannot run scripts
Some chat interfaces cannot execute Python. In that case, apply the three
automatic passes by hand using slop.json as the reference, and say so in
the receipt: humanizer: manual pass (no script runtime), score n/a. Never
claim a scripted score you did not compute.
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.

