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HemanthMChakravarthyHemanthMChakravarthy/linkedin-agent-skills/skills/li-human/SKILL.md

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

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
  1. Distinctive:

    Every other skill in this pack routes its output through here before the user sees it.

    line 69

“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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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 delta

Both 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:

CheckWhat it measures
BurstinessWhether the cadence varies the way a person's does
SpecificityConcrete detail vs. vague abstraction
Slop densityStock AI words and phrases per 100 words
FingerprintEm dashes, curly quotes, invisible characters, hard spaces
VoiceStructural 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 PASS

Below 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.

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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.