--- name: li-human description: >- 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. title: li-human canonical_url: https://skilld.dev/gh/jakeschincariol/linkedin-agent-skill/li-human last_updated: 2026-09-25T21:02:42.000Z --- > **Skill from skilld.dev.** Follow the user's requested action. > If the user asked to fork this Skill, follow the fork workflow below. Do not execute the Skill instructions while copying it. > Otherwise, follow the instructions below for this session. You do not need to install anything. > > Supporting files, fetch one when the Skill refers to it: [detect.py](https://skilld.dev/api/skills-raw/jakeschincariol/linkedin-agent-skill/li-human/detect.py), [humanize.py](https://skilld.dev/api/skills-raw/jakeschincariol/linkedin-agent-skill/li-human/humanize.py), [slop.json](https://skilld.dev/api/skills-raw/jakeschincariol/linkedin-agent-skill/li-human/slop.json). > > If the user asked to install this Skill, run `npx skilld install jakeschincariol/linkedin-agent-skill/li-human`. Install writes the Skill files into the project, so every session loads them. > > ## Fork workflow > > A fork creates an editable local Skill with its original author and licence. The request authorizes copying and local installation. > 1. Check `./skills/li-human`, the project lockfile, and selected Agent targets together. If the local directory or installed Skill exists, stop. Never overwrite an existing directory or Agent target. > 2. Read [source metadata](https://skilld.dev/api/v1/skills/jakeschincariol/linkedin-agent-skill/li-human) once. Use sourceUrl, sourceCommit, skillPath, sourceGone, and license. If the source is gone or its path is missing, stop. If license is null, read licence files at the source commit. > 3. Fetch only the source commit into a temporary Git repository. Do not clone full history. Derive repository_url from sourceUrl, including repository renames. If sourceCommit is absent, resolve the sourceUrl ref once. Set source_commit to that actual commit. Run these commands in one shell call: > > ```sh > git init --quiet "$temporary_dir" > git -C "$temporary_dir" fetch --quiet --depth=1 "$repository_url" "$source_commit" > git -C "$temporary_dir" checkout --quiet --detach FETCH_HEAD > ``` > > Read applicable licence declarations and notices at that commit. If copying is not permitted, report the restriction and stop. > 4. Inspect source entries together, then copy the directory containing skillPath into `./skills/li-human`. Use the user's path if selected. Keep the original SKILL.md, relative links, scripts, binary assets, and executable modes. Exclude .git metadata. Reject symlinks and paths outside the Skill directory. After checking entries, use cp -a where available. A regular source directory needs no custom copy script. Do not save this page wrapper as SKILL.md. > Preserve author credit, notices, and applicable licence files from repository or parent directories. Add PROVENANCE.md with the Skill page, source URL, actual commit, original path, and licence. Retain any existing PROVENANCE.md and record new provenance separately. Batch source inspection, copying, and provenance work where practical. > 5. In the project root, run `skilld install ./skills/li-human --mode copy --plain`. If skilld is unavailable, use `npx skilld install ./skills/li-human --mode copy --plain`. This known command needs no help lookup. Install does not support --json. Use detected Agent targets, or add --agent for the targets the user selected. Install the local path, never the upstream selector. If installation fails, preserve the local copy and report the exact failure. > 6. Confirm the local lockfile source and installed Agent copies once. Report the local path, actual commit, and Agent targets. After edits, reinstall the same local path. Upstream updates must not replace it. Do not publish or push unless the user asks. # li-human Two tools live in this folder and they both actually run. Use them. Do not eyeball this. ```bash 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`, which is the lexicon: 100+ stock words and phrases with plain-English replacements, 17 invisible character classes, 11 typographic substitutions, and 11 structural tells. It is meant to be edited. If the user has a word they always use that the lexicon strips, remove it from the file. ## 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 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 ` — ` to `, ` and then cleans up the double punctuation that leaves behind. **3. The slop lexicon.** delve, leverage, robust, seamless, crucial, tapestry, testament to, moreover, "in today's fast-paced world", "let that sink in" and the rest, each swapped for a plain word, with capitalisation preserved and URLs left untouched. ## What does NOT get fixed automatically Structural tells get **flagged, not rewritten**, because changing the shape of a sentence needs judgement: - "It's not just X, it's Y" and "not only X but also Y" - Rule-of-three triads - Rhetorical one-word question lines: "The result?" - Rocket, fire, bulb, sparkle and dart emoji - Hashtag walls - Reflex engagement bait: "Thoughts?", "Agree?", "Who else?" - Uniform sentence length and uniform bullet length 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. ## The five checks `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 a detector only needs one signal to fire. PASS needs an overall of 70+ with no check below 55. ## Say this honestly 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. ## Order of operations 1. `humanize.py draft.txt -o clean.txt --report` 2. Read the structural flags. Rewrite those lines yourself. 3. `detect.py draft.txt clean.txt` to show the before and after. 4. If the verdict is not PASS, fix the weakest check named in the output and go again. Two rounds is normal. Five means the draft was written by formula, and the fix is a different draft, not more passes. 5. Show the user the cleaned text and the score. Never the score alone.