You can spot AI-generated prose within three sentences.
It rarely stems from grammatical errors. Large language models understand syntax and subject-verb agreement better than most human writers. The tell is subtler: an unearned earnestness, a predictable rhythm, and an insistence on explaining why everything it mentions is historic, essential, or transformative.
When readers encounter this tone, they stop reading. Search engines downrank it. Communities reject it.
Over the past two years, Wikipedia editors cataloged thousands of AI-generated article submissions to isolate exactly what gives synthetic prose away. They compiled their findings into a guide called Signs of AI Writing.
To turn those observations into an automated, actionable workflow for developers and writers, I built writ—an open-source AI self-audit skill that systematically strips synthetic habits out of machine drafts.
Here is an analysis of why language models write this way, the specific patterns that give them away, and how to audit your text before publishing.
The single biggest tell in AI text is not a specific word. It is the impulse to explain the importance of a statement instead of simply stating it.
Human writers trust their audience. If you report that an engineering team migrated from PostgreSQL to ClickHouse and cut query latency by 80%, the reader grasps the impact immediately.
A standard language model cannot resist adding a participatory tail:
"...slashing query latency by 80%, highlighting the team's commitment to efficiency and underscoring the transformative shift toward real-time analytics."
Notice what happened. The sentence stopped delivering information and started narrating its own importance. If you delete that final clause, the factual value remains unchanged.
Language models behave this way because reinforcement learning from human feedback (RLHF) rewards outputs that sound complete, polite, and authoritative. In practice, that training creates prose that sounds like an anxious corporate press release.
The writ skill codifies Wikipedia's catalog into a targeted checklist. When auditing drafts, these six patterns appear most frequently:
Sentences that end with trailing -ing clauses designed to manufacture weight:
Certain words are fine in moderation, but language models cluster them with statistical regularity:
Language models frequently reach for rhetorical negative parallelism to fake depth:
AI models lean heavily on em dashes (—) to splice thoughts together where human writers naturally use commas, colons, parentheses, or separate sentences.
Models often invent controversy around mundane facts to appear objective:
AI prose tends to settle into a metronomic cadence: sentence of twelve words, followed by sentence of fourteen words, followed by sentence of thirteen words.
writ Skill?
writ is an open-source, prompt-based self-audit skill designed for AI coding agents, developers, and technical writers.
Instead of generating text from scratch, writ acts as an editorial second pass. It ingests an existing draft, scans the text against the codified Wikipedia heuristics, identifies synthetic patterns, and rewrites the passage while preserving the author's technical intent and data points.
┌──────────────────────┐
│ Raw Draft / AI Output│
└──────────┬───────────┘
│
▼
┌──────────────────────┐
│ writ Audit Engine │
│ • Kill tacked clauses│
│ • Strip stock terms │
│ • Break cadence lock│
└──────────┬───────────┘
│
▼
┌──────────────────────┐
│ Natural Human Prose │
└──────────────────────┘
To understand how writ works, examine this comparison taken from a technical system overview:
"In today's fast-paced technological landscape, caching plays a vital role in backend architecture. By seamlessly storing frequently accessed records in memory, Redis acts as a game-changer for distributed systems, underscoring the critical need for low-latency operations and highlighting the shift toward real-time responsiveness."
Notice the symptoms:
"Redis stores hot keys in memory to keep read latencies under two milliseconds. For high-throughput services, that eliminates repetitive queries against the primary database and prevents connection exhaustion during traffic spikes."
The edited version is shorter, contains specific technical metrics, and provides concrete engineering rationale without posturing.
The repository is hosted on GitHub at github.com/Avinashricky211/writ. You can integrate it in three ways:
Clone or add the writ directory directly into your agent skills path:
git clone https://github.com/Avinashricky211/writ.git
When prompt-instructing your agent, invoke the skill:
Review this article draft using /writ. Strip all synthetic AI tells, remove dangling significance clauses, and ensure the tone reads as a technical peer speaking to another engineer.
You can copy SKILL.md from the repository and paste it into your system prompt when generating or revising blog posts, release notes, documentation, or newsletters.
Add writ as an automated review step in your CI documentation pipelines to flag PR descriptions and markdown docs that lean too heavily on marketing adjectives.
You can inspect the rules, contribute heuristics, or download the full skill at github.com/Avinashricky211/writ.
Written by Yadlapalli Avinash Ricky, AI Engineer, Author of The Art of AI Prompts, and creator of AviGPT-250M.