npx skills add ...
npx skills add jakeschincariol/instagram-agent-skill --skill ig-human
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", "this sounds like ChatGPT", or before any caption, script, comment, reply or DM is shown to the user.
npx skills add jakeschincariol/instagram-agent-skill --skill ig-human
Two tools live in this folder and they both actually run. Use them. Do not eyeball this.
Both read slop.json: 154 stock words and phrases with plain-English
replacements, 18 invisible character classes, 11 typographic substitutions and
16 structural tells. The last block of each list is Instagram-specific, the
vocabulary that only shows up in captions and voiceovers. It is meant to be
edited. If the user has a word they always use that the lexicon strips, take it
out of the file.
Captions are short and scripts get said out loud. A written-sounding line in a 600-character caption is a larger share of the text than the same line in an essay, and a voiceover that nobody could say naturally is obvious in the first take. The tell here is not a detector flagging the post. The tell is a person scrolling past something that reads like a brand, or a creator stumbling over their own script.
1. Invisible characters. Zero-width spaces and joiners, word joiners, soft
hyphens, byte-order marks, Unicode tag characters, invisible separators,
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 most
mechanical thing 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 the dash to a comma and then cleans up the double punctuation and orphaned periods that leaves behind.
3. The slop lexicon. delve, leverage, robust, seamless, crucial, testament to, "in today's fast-paced world", plus the Instagram block: "stop scrolling", "in today's video", "follow for more", "tag someone who needs this", "the algorithm loves", "run don't walk". Each swapped for a plain word or deleted, with capitalisation preserved and URLs left untouched.
Structural tells get flagged, not rewritten, because changing the shape of a sentence needs judgement:
That list is your job. Rewrite each flagged line by hand, keeping the meaning,
then re-run detect.py. This is the part that moves the score from REVIEW to
PASS, and it is the part a script cannot do.
detect.py scores five signals 0-100, higher is more human:
| check | what it measures | machine looks like |
|---|---|---|
| BURSTINESS | sentence-length variation | every sentence the same length |
| SPECIFICITY | numbers, names, concrete markers per 100 words | abstract nouns, no figures |
| SLOP DENSITY | lexicon hits per 100 words | stock vocabulary |
| FINGERPRINT | invisible chars, em dashes, curly quotes per 1k chars | typographically perfect |
| VOICE | contractions, person, structural tells | no contractions, staged reveals |
The verdict weights the mean at 60% and the weakest single check at 40%, because one signal is enough. PASS needs an overall of 70+ with no check below 55.
These are five local heuristics modelled on the signals public detectors key on. They run entirely on the user's machine and nothing is uploaded. They are not GPTZero, Originality, Copyleaks, Winston or Turnitin, they do not call those APIs, and they cannot promise those verdicts. Fixing what they measure does tend to move those numbers, because they are measuring the same underlying things. That is the claim. Do not make a bigger one on the user's behalf, and do not tell a user their text is undetectable.
humanize.py draft.txt -o clean.txt --reportdetect.py draft.txt clean.txt to show the before and after.