caveman
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy.…
De-slop pass for any text. Scans for the statistical fingerprints of AI writing (negative parallelism / "not X but Y", em-dash density, rule-of-three, false ranges, puffery vocabulary, uniform cadence, hedged both-sidesing), rewrites by meaning, then re-scans until the text is
$ npx -y skills add juliusbrussee/skills --skill deslopify --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/deslopifyContext preview
The summary Claude sees to decide when to auto-load this skill.
De-slop pass for any text. Scans for the statistical fingerprints of AI writing (negative parallelism / "not X but Y", em-dash density, rule-of-three, false ranges, puffery vocabulary, uniform cadence, hedged both-sidesing), rewrites by meaning, then re-scans until the text is
name: deslopify description: > De-slop pass for any text. Scans for the statistical fingerprints of AI writing (negative parallelism / "not X but Y", em-dash density, rule-of-three, false ranges, puffery vocabulary, uniform cadence, hedged both-sidesing), rewrites by meaning, then re-scans until the text is clean and sits in the right register for its genre: academic article, tweet, reddit post, email, blog, docs, marketing. Use when the user says "deslopify", "deslop", "de-slop this", "remove the AI tells", "humanize this", "make this not sound like AI", or invokes /deslopify. Also use before publishing any agent-drafted prose.
Strip the AI fingerprints out of a text and make it good in its genre. The target is prose that reads like one person wrote it for one audience about something they actually know. Detector scores are beside the point; text can score human and still be unreadable.
The "not X but Y" family and its relatives are generative habits. Preference tuning rewards balanced, contrastive, comprehensive-sounding framing, so the contrast move sits deep in the model's priors and surfaces about once a paragraph. Two consequences shape this skill:
1. **You cannot see your own slop.** The priors that produce the pattern also make it invisible on re-read. So detection runs mechanically, as regex against a fixed catalog. "Does this look AI to me?" is not a detection method. 2. **Rewriting reintroduces slop.** Ask a model to remove "it's not just X, it's Y" and out comes "this is less about X than Y", the same move in a wig. Every rewrite therefore gets re-scanned, and the loop runs until a scan comes back clean.
Workflow: **Scan → Diagnose → Rewrite by meaning → Re-scan → (repeat) → Register check.**
Before touching the text, establish:
Run the detection patterns from [references/tells.md](references/tells.md) against the text. If the text is in a file, or you can write it to a temp file, run the grep commands in that reference literally: the catalog is written as runnable `grep -Ein` patterns. Otherwise apply each pattern by hand, line by line.
Produce a finding list: line or sentence, matched pattern, tell category. Then run the two structural checks regex can't catch:
Report the findings to the user as a short table before rewriting: category, count, worst example. The user should see the diagnosis.
Go finding by finding. The cardinal rule: never fix a pattern by paraphrasing the pattern. Decide what the sentence asserts, then assert that.
Every negative parallelism gets exactly one of these treatments:
1. **Strawman negation** (nobody believes X). Delete the X half and assert Y directly, with whatever evidence the text has.
2. **Real contrast** (people genuinely hold X). Earn it: name who holds X, say concretely why Y beats it. A real contrast survives being made specific; slop doesn't. 3. **Empty claim** (the contrast decorates a sentence that asserts nothing). Delete the sentence. Most cases are this one.
Banned escape hatches, all the same move, all counted as new findings: "less about X than Y", "X matters, but Y matters more", "the real X is Y", "the question isn't X, it's Y", "X? Y." (the rhetorical-question variant), and the em-dash form "— not X, but Y".
Eight personal agent skills for now: Caveman base, Interface Kit, Grill Me, Loop Factory, Junior to Senior, Deslopify, Context Canary, and The Last 20%. This repo is shaped by eight things: Caveman - 70k-star token compression without technical loss.
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