tell

name

tell

description

Check whether a passage reads as AI-generated and point at the specific tells to rewrite — overused words (delve, tapestry, crucial), cliché phrases ("it's not just X, it's Y", "in conclusion"), em-dash overuse, emoji, rule-of-three lists. Use when you want a diagnosis of why prose sounds like a model wrote it, not a rewrite. Pairs with deadpan: deadpan prevents tells at generation; tell detects them after.

tell

every AI has a tell.

A model leaves fingerprints. This skill is for spotting them — reading a passage and naming the exact words and structures that scream "generated", so they can be rewritten by hand.

What counts as a tell

1. Overused words. delve, tapestry, testament, realm, navigate, underscore, leverage, robust, seamless, crucial, pivotal, multifaceted, nuanced, intricate, bustling, vibrant, foster, harness, elevate, unlock, embark, landscape, beacon, treasure trove.
2. Cliché phrases. "it's not just X, it's Y", "not only … but also", "in conclusion", "in summary", "it's worth noting", "in today's fast-paced world", "when it comes to", "a testament to", "plays a crucial role", "at the end of the day", "the world of", "dive into", "rich tapestry", "ever-evolving", "game-changer".
3. Structural tells. em-dash overuse, emoji, rule-of-three comma lists ("fast, cheap, and reliable"), excessive bold runs.

A single tell is noise; a cluster is a confession. Weigh density, not just presence.

Example

Tell-ridden: Let's delve into the rich tapestry of modern computing — a testament to human ingenuity. It's not just a tool, it's a revolution that plays a crucial role in our ever-evolving world.

Diagnosis: delve, tapestry, testament, "it's not just X, it's Y", "plays a crucial role", "ever-evolving", one em-dash — score ~99/100. Rewrite from scratch; nothing here survives.

Output

Deliver the diagnosis — the tells and the score — not a narration of running tell.py. Don't explain the tool; list what was found and stop. No preamble, no closing "consider rewriting" unless the user asked what to do about it.

Companion tool

tell.py in this folder scores text mechanically (0-100) and lists every tell with counts. Use it to diagnose, gate prose in CI (tell --max 30 draft.md), or spot-check your own drafts. It pairs with deadpan: deadpan stops the tells at generation time, tell catches the ones that slipped through.

Two modes and a knob:

• --llm — instead of the keyword lexicon, hand the passage to a model that reads it the way a human would, catching paraphrased slop and structural blandness the word list misses. Same {score, hits, total} output, so --score/--json/--max still work. --provider and --model pick the backend (defaults from the environment); failure exits 2, no silent fallback.
• --patterns FILE (repeatable) — extend the offline lexicon with your own tells from a JSON file {"words": [...], "phrases": [[label, regex], ...]}. Honors $TELL_PATTERNS (os.pathsep-separated) as a fallback. Your entries extend the built-ins; they do not affect --llm.