--- name: anti-ai-prose description: > ยท Audit prose for AI tells in docs, PRs, emails, slides, docstrings. Triggers: 'ai writing', 'sounds like chatgpt', 'ai slop prose', 'llm voice', 'sound human'. Not for code (use anti-slop). license: MIT compatibility: "None - works on any prose or text input" metadata: source: iuliandita/skills date_added: "2026-04-09" effort: medium argument_hint: "" --- # Anti-AI-Prose: Audit Writing for Machine-Generated Patterns Detect and fix the linguistic tells that make written English read as machine-generated. The goal is prose that sounds like a specific, thoughtful human wrote it. This skill applies to any text: **documentation**, **READMEs**, **wikis** (Confluence, Notion, internal), **pull request descriptions**, **commit messages**, **release notes**, **blog posts**, **emails**, **slide copy**, **creative writing**, and **code comments / docstrings**. The vocabulary, syntax, tone, and formatting checks are language-domain, not platform-domain. Based in part on [Wikipedia: Signs of AI writing](https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing) - a field guide compiled by editors who have read enormous volumes of LLM-generated text and know what it actually looks like. ## When to use - Auditing a README, doc page, or wiki article that feels machine-written - Reviewing a PR body, commit message, or release note draft before publishing - Polishing a blog post, email, or presentation script you wrote with LLM help - Checking creative writing (fiction, essays) for AI tells after an LLM-assisted pass - Reviewing docstrings and code comments for the same prose patterns - Any time someone says "this sounds like ChatGPT wrote it" - Self-check after a heavy LLM-drafting session ## When NOT to use - Code quality, over-abstraction, dependency creep, stale idioms - use **anti-slop** - Doc drift after a feature change, API rename, or config update - use **update-docs** - Generating or restructuring a prompt from rough notes - use **prompt-generator** - Correctness bugs, logic errors, edge cases - use **code-review** - Security review of auth, secrets, or attack surface - use **security-audit** - Full multi-dimensional repo audit - use **full-review** --- ## AI Self-Check Before returning any audit, verify: - [ ] **Findings are patterns, not taste**: the issue is a demonstrable AI tell (from the Wikipedia guide or observed LLM output), not personal writing preference - [ ] **Context respected**: academic and technical prose can look formal without being AI-generated. Journalism, marketing, and tourism writing have legitimate conventions that overlap with AI tells. Do not flag genre conventions as AI tells - [ ] **Direct quotes preserved**: do not edit quoted material from other authors, even if it contains banned vocabulary - [ ] **Domain terms kept**: `pivotal` in hinge hardware specs, `landscape` in horticulture, `foster` as a verb in child welfare, `realm` in fantasy fiction - these are not tells in context - [ ] **Code blocks untouched**: do not flag identifiers, strings, or code comments that contain banned words as part of functional code - [ ] **Rewrites are real improvements**: every "after" is shorter, clearer, or more specific than the "before". No lateral rewrites that just swap synonyms - [ ] **Severity is honest**: do not inflate Low findings to Medium to pad the report - [ ] **Short text rule applied**: under 100 words, 2+ tells in one paragraph = High severity - [ ] **Audit output itself uses no AI-prose tells** (apply these rules to your own output) - [ ] **Density threshold applied** before assigning severity level --- - [ ] **Current source checked**: dated versions, CLI flags, API names, and support windows are verified against primary docs before repeating them - [ ] **Hidden state identified**: local config, credentials, caches, contexts, branches, cluster targets, or previous runs are made explicit before acting - [ ] **Verification is real**: final checks exercise the actual runtime, parser, service, or integration point instead of only linting prose or happy paths - [ ] **Overflagging avoided**: plain but valid technical prose is not labeled AI-written without concrete evidence - [ ] **Audience preserved**: edits keep the author's domain vocabulary, intent, and required formality --- ## Performance - Review a representative sample first, then expand only if the same pattern repeats across the document. - Group repeated prose issues by pattern instead of leaving near-duplicate comments on every paragraph. - Prioritize high-visibility text: titles, summaries, intros, conclusions, and user-facing docs. --- ## Best Practices - Flag exact phrases and structural patterns, not vibes. - Offer replacement copy when the fix is obvious; otherwise describe the problem and let the author decide. - Do not erase necessary caveats, compliance language, or domain-specific precision to make prose sound casual. ## Workflow ### Step 1: Scope the audit Default scope based on context: - If invoked on a specific file or paste - audit that text - If invoked with no target and there are uncommitted changes to `.md` / `.txt` / doc files - audit those - If invoked in a code repo with recent commits - audit the docstrings and comments in changed files - Otherwise - ask the user for a target Available scopes: - **Single file** - one doc, README, draft, or source file - **Directory** - all `.md` / `.rst` / `.txt` under a path - **Pasted text** - inline block the user supplies - **Recent changes** - git diff against a base - **Comments and docstrings only** - scan code files but audit only prose regions ### Step 2: Detect text kind Different text types have different conventions. Before flagging, identify which applies: - **Technical docs** - formal is OK, but vocabulary bans still apply - **README / PR / commit** - concise is expected, significance padding is especially jarring - **Marketing / product copy** - tonal tells (`boast`, `showcase`) may be intentional but still weaken the writing - **Creative fiction** - many tells (tricolons, elegant variation) are legitimate devices; flag only when they read as mechanical - **Wiki article** - neutral voice required, promotional language is always a finding - **Email** - conversational is expected, formality inflation is a tell - **Slides / presentation** - fragments are fine, but vocabulary and tonal tells still apply ### Step 3: Scan for patterns Apply the four categories (see below). For each match, read the surrounding context - a single instance of an AI word in a 5000-word document is probably noise, but three instances in three paragraphs is a pattern. **Density heuristic** (rough guide, not a hard rule): - **Under 1 flagged item per 500 words** - noise, usually do not flag - **2-3 per 500 words** - a pattern, flag the cluster with Medium severity - **4+ per 500 words** - dominant voice, High severity, recommend structural rewrite **Short text scaling:** for text under 100 words, any 2+ tells in a single paragraph is High severity regardless of the per-500-words threshold. A single sentence crammed with AI vocabulary is worse than a long doc with scattered instances. Density only applies to vocabulary and syntax tells. A single travel-guide paragraph is enough to flag on its own. One fabricated citation is always High. Classify each finding by category, action, and severity: **Action:** - **Fix** - clearly a tell, should change - **Consider** - judgment call, present it and let the user decide - **Fine** - matches the pattern but is justified (note why, move on) **Severity:** - **High** - cluster of tells that makes the piece sound unmistakably AI-written; vague attribution passing opinion as fact; fabricated citations or broken references - **Medium** - vocabulary or syntax tells that dull the voice without breaking trust; formulaic structures ("Despite its X, faces challenges..."); travel-guide voice in non-travel writing - **Low** - single instances of banned vocabulary; formatting nits (em-dash usage, unnecessary bold); tricolon overuse ### Step 4: Report and fix Present findings grouped by category. For each Fix-level item, show the concrete rewrite. Rewrites should be **shorter** or **more specific** - never longer. --- ## The Four Categories of AI Prose Slop ### 1. Vocabulary Tells (Noise) Specific words that LLMs overuse far beyond their natural English frequency. **Flagged vocabulary** (context-sensitive - see exceptions below): | AI word | Natural alternatives | |---|---| | delve | look at, examine, dig into, cover | | tapestry | mix, range, variety (or just drop the metaphor) | | testament | proof, evidence, example | | pivotal | key, important, central (or drop if padding) | | crucial | important, needed (or drop if padding) | | realm | area, field, world | | landscape | scene, field, mix | | showcase | show, display, feature | | empower | help, enable, let (or rewrite with a specific claim) | | foster | build, grow, support, encourage | | navigate | handle, work through, manage | | nestled | set, located, built | | vibrant | lively, active, busy (or drop) | | underscore | show, highlight, confirm | | garner | get, earn, attract | | enduring | lasting, long-running | | boast | have (just "has") | | leverage | use | | utilize | use | | facilitate | help, enable | | seamless | smooth (or drop) | | robust | reliable, solid (or drop if padding) | | commitment to | cares about, focuses on | | dive deep into | look at, cover | | embark on | start, begin | | nuanced | subtle, careful, specific (or drop - almost always padding) | | multifaceted | has many sides, covers a lot (or drop) | | holistic | whole, end-to-end, full (or drop) | | synergy | fit, overlap, how X and Y work together (or drop) | | innovative | new, novel (or name what is new) | | commence | start, begin | | journey toward | work toward, move toward, aim for (or drop) | | moving forward | from now on, next, going forward (or drop) | **Detect:** - Multiple flagged words in the same paragraph - Flagged word used metaphorically (`tapestry of experiences`, `realm of possibility`) - Flagged word in a context where a plain verb would work (`showcase the features` -> `show the features`) **Fix:** Replace with the plain alternative. If the sentence gets weaker after replacement, the original was padding - cut the whole phrase. See "What NOT to Flag" below for domain exceptions (horticulture `landscape`, child welfare `foster`, networking `realm`, etc.). ### 2. Syntax Tells (Noise + Soul) Sentence structures LLMs reach for to sound balanced or significant. #### Negative parallelism LLMs overuse `not X but Y` and `not just X, but also Y` constructions to signal balance and sophistication. In moderation this is fine English. In quantity it is a clear tell. **Detect:** - Three or more `not X but Y` / `not just X, but Y` / `it's not about X, it's about Y` structures in a single piece - Used where a direct claim would work: `this isn't just a tool, it's a platform` -> `this is a platform` **Fix:** State the positive claim directly. If the contrast matters, keep one instance and rewrite the rest. #### Forced tricolons (rule of three) `X, Y, and Z` lists used for rhythm rather than enumeration. LLMs default to three items even when two or four would be more accurate. **Detect:** - Adjective triplets where one adjective would carry the meaning: `a fast, reliable, and scalable system` - Noun triplets that are really the same concept: `clarity, precision, and accuracy` - Three-item lists where the third item is obviously padded to hit the count **Fix:** Drop the weakest item. Use two items when the point is a contrast, four or more when it is an actual list. #### Copula avoidance LLMs avoid plain `is` / `are` / `has` / `have` in favor of elaborate constructions: `serves as`, `marks`, `represents`, `features`, `offers`, `boasts`, `stands as`. **Detect:** - `serves as` where `is` works: `it serves as a backup` -> `it is a backup` - `represents` used as a replacement for `is`: `this represents a shift` -> `this is a shift` - `boasts` used for `has`: `the app boasts 50 features` -> `the app has 50 features` - `marks` used to inflate: `this marks the first time` -> `this is the first time` **Fix:** Use the plain copula. Elaborate verbs should carry weight - do not spend them on simple identity claims. #### Elegant variation LLMs avoid repeating a noun within a paragraph, substituting increasingly strained synonyms. A character named Alice becomes `the protagonist`, `the main character`, `the young woman`, `the eponymous heroine` in four consecutive sentences. **Detect:** - The same entity referred to by 3+ different nouns in close proximity - Strained synonyms where a pronoun or name repetition would be natural - Different technical terms for the same concept within one document **Fix:** Use the name, or a pronoun. Repetition is fine. Forced variation is worse than repetition. ### 3. Tonal Tells (Soul) The voice of the text gives away the author even when the words are individually defensible. #### Travel-guide voice `Nestled between rolling hills, this vibrant city boasts a rich cultural heritage and a thriving arts scene`. LLMs default to this register for any geographic or cultural topic. **Detect:** `nestled`, `rolling hills`, `vibrant`, `thriving`, `rich heritage`, `bustling`, `charming`, `picturesque` **Fix:** State facts. `The city has 300,000 people, two universities, and a jazz festival in August.` #### Promotional tone `Our commitment to excellence ensures we foster innovation and empower our customers to succeed.` LLMs reach for press-release cadence when asked to describe any organization or product. **Detect:** `commitment to`, `empower`, `foster`, `ensure`, `strive`, `dedicated to`, `passionate about`, `industry-leading`, `cutting-edge`, `next-generation` **Fix:** Replace with specific claims. `We help X customers do Y` beats `We empower customers to succeed`. #### Vague attribution `Experts say`, `industry reports indicate`, `observers have noted`, `many believe`. LLMs use these when they want to assert something without a source. Real writers either cite or own the claim. **Detect:** - `experts say` / `experts agree` without naming experts - `industry reports` / `studies show` without a study - `observers have noted` / `critics argue` without names - Plural `sources say` pointing to at most one source **Fix:** Cite the source. Or own the claim. Or cut it - most of the time the surrounding sentence works without the attribution. #### Significance padding `This marks a pivotal moment, underscoring broader trends in the industry.` LLMs inflate the weight of routine events to pad word count. **Detect:** - `marks a pivotal moment` - `underscoring broader trends` - `highlighting the importance of` - `serves as a reminder that` - `in an era where` - `in today's fast-paced world` **Fix:** Delete the whole sentence. If what follows does not make sense without the padding, rewrite the surrounding paragraph. #### Hedging and qualifier stacking LLMs stack hedges and qualifiers to sound cautious or balanced. Each hedge by itself is fine English; stacking them makes every claim feel tentative. **Detect:** - Frequent `generally`, `typically`, `often`, `usually`, `in many cases`, `for the most part` - Weak modal stacking: `may`, `can`, `might`, `could potentially`, `arguably`, `relatively` - Two or more hedges in the same clause: `can generally be considered to be relatively reliable` - Hedges on claims that the author clearly knows are true: `this may help with performance` (when benchmarks are already in the paragraph) **Fix:** Delete the hedge and state the claim. If the claim really does need a caveat, state it concretely: `on Linux only`, `for connections over 1000 RPS` - not `generally speaking`. #### Scaffolding padding Phrases that wrap around the actual content without adding information. LLMs lean on these to sound organized or conversational. **Detect:** - `it's worth noting that`, `it's important to note`, `it's worth mentioning` - `in this article, we'll explore` / `in this guide, we'll cover` (meta-commentary about the piece itself) - `let's dive into` / `let's explore` / `let's take a look at` - `here's the thing:` / `the fact is:` / `the truth is:` - `at the end of the day` / `when all is said and done` - `as we've seen` / `as mentioned earlier` / `as previously discussed` (when the reader just read it) **Fix:** Cut the wrapper and keep the content. `It's worth noting that X` becomes `X`. `In this article, we'll explore Y` becomes a first sentence that is about Y. #### "Despite its X, faces challenges" LLMs reach for a formula when asked to describe any organization or project: positives first, then a "however" paragraph listing challenges, often ending with a "future outlook" paragraph. **Detect:** the shape of the article more than specific words. Three-paragraph structure where paragraph 1 is positive, paragraph 2 starts with `Despite` or `However`, and paragraph 3 starts with `Looking ahead` or `The future`. **Fix:** Reorganize around the actual story. If there is no story, the piece probably should not exist. ### 4. Formatting Tells (Noise) Layout and punctuation patterns that LLMs default to. **Detect:** - **Em dashes** (Unicode U+2014, or the `--` double-dash substitute) used as sentence breaks. LLMs overuse them to imitate journalistic cadence. Replace with single `-` or restructure the sentence. - **Title Case in section headings** (`Understanding the Core Concepts` vs `Understanding the core concepts`). AI defaults to title case even in sentence-case conventions. Match the project's style. - **Excessive bold** - every third noun bolded for no reason. Bold earns its use by signaling a term or path. - **Bullet salad** - prose turned into bullets when a paragraph would read better. Lists are for enumerations, not for every idea. - **Three-bullet-happy layouts** - suspicious when every list has exactly three items - **Curly quotes** (`"`, `'`) in technical writing that should use ASCII - **Emoji** in professional prose where decoration is the only purpose - **Decorative thematic breaks** - `---` before every `##`. Dividers that mark a real phase change are fine; decoration is not - **Markdown artifacts in rendered text** - `**bold**` appearing as literal characters because the paste lost its format - **LLM output bugs** - `turn0search0`, `contentReference`, `oaicite`, `+1`, `attached_file`, hallucinated wiki-style shortcuts **Fix:** Match the surrounding project's conventions. If there is no convention, default to plain ASCII, sentence case, minimal bold, paragraph prose. --- ## What NOT to Flag These look like AI tells but are not: - **Direct quotations** - do not edit words written by someone else, even if they contain banned vocabulary - **Genre conventions** - travel writing uses travel-guide voice because that is what travel writing sounds like. Marketing copy uses promotional tone. Journalism uses em-dashes. Fiction uses elegant variation and tricolons intentionally. Respect the genre. - **Technical terms of art** - `pivotal` in mechanical engineering, `realm` in networking or identity (Kerberos, OIDC), `foster` in child welfare, `landscape` in horticulture or graphic design, `crucial experiment` in philosophy of science - `landscape` in ML/AI contexts (optimization landscape, loss landscape, feature landscape) - `robust` in statistics/ML (robust estimation, robust optimization, robust regression) - **Deliberate register play** - satire, parody, pastiche, and stylistic experiments - **Direct speech / dialog** in fiction - characters can sound however they sound - **Lists that are actually lists** - a three-item list is only suspicious if the items are padded. An enumeration of three real things is fine - **Bold where it signals a term or path** - bolding a defined term on first use is standard - **Em dashes in publications that require them** - some style guides (Chicago, AP) allow or require em dashes. The rule applies to your project's conventions ### Counter-example (prose that looks AI but is fine) > Nestled in the loss landscape near a sharp minimum, the model's robust features fail to generalize. This underscores a pivotal result from Keskar et al. (2017): flat minima tend to foster better test accuracy than sharp ones. Looks flagged at a glance: `nestled`, `landscape`, `robust`, `underscores`, `pivotal`, `foster`. But every term is a term of art (ML optimization, statistics), `underscores` has a real referent, and the citation is real. Verdict: **Fine**. Do not flag. Domain context overrides vocabulary match. --- ## Output Format ````markdown ## Anti-AI-Prose Audit: [scope] ### Findings #### [Category Name] ([count] items) **[action]** ([severity]) `path/to/file:line` - [description] > before: [quoted text from the source] > after: [suggested rewrite] ### Summary - X findings across Y files / sections - [overall read: does the piece sound human?] - [top-level observation: e.g., "vocabulary is mostly fine but the structure is formulaic"] ```` Rules for the report itself: - **Omit empty categories.** If there are no formatting tells, do not write an empty "Formatting Tells (0 items)" heading - **Order within a category** High > Medium > Low - **Deletion fixes have no "after"** - write `> after: (cut)` or just state the delete in the description - **Apply these rules to your own audit.** Run the Self-Check on the report before returning it - an audit written in AI-slop voice is not credible Keep it concise. Show the before/after pair. Do not lecture about why AI writing is bad - the user already knows. ### Worked example (anchor the format) Input (README snippet, 48 words): > In today's fast-paced world, our platform empowers developers to seamlessly navigate the complex landscape of modern APIs. Built with a commitment to excellence, it boasts robust features and fosters innovation. Whether you're a beginner or expert, this tool serves as a pivotal resource for your journey toward better software. Report: ``` ## Anti-AI-Prose Audit: README snippet (48 words) ### Findings #### Vocabulary Tells (6 items) **Fix** (High) line 1 - cluster of 6 flagged words in 48 words: far above 4/500 threshold > before: empowers / seamlessly / navigate / landscape / commitment to / boasts / fosters / pivotal / journey toward > after: (rewrite, see below) #### Tonal Tells (2 items) **Fix** (High) line 1 - scaffolding padding and significance padding > before: "In today's fast-paced world" > after: (cut) **Fix** (Medium) line 1 - promotional tone > before: "Built with a commitment to excellence" > after: (cut) ### Summary - 8 findings, one paragraph, dominant AI voice - Rewrite: "An HTTP API client for Python. Handles auth, retries, and pagination. Works with any OpenAPI 3.x spec." - Down from 48 words to 22, with concrete claims instead of posture ``` --- ## Output Contract > **Severity migration:** The prior scale (`High | Medium | Low`) is replaced by `P0 | P1 | P2 | P3 | info`. Mapping: `High` -> `P1`, `Medium` -> `P2`, `Low` -> `P3`. Inline severity references elsewhere in this file should be updated in a follow-up pass -- out of scope for this contract retrofit. See `skills/_shared/output-contract.md` for the full contract. - **Skill name:** ANTI-AI-PROSE - **Deliverable bucket:** `audits` - **Mode:** always-on. Every invocation emits the full contract -- boxed inline header, body summary inline plus per-finding detail in the deliverable file, boxed conclusion, conclusion table. - **Deliverable path:** `docs/local/audits/anti-ai-prose/-.md` - **Severity scale:** `P0 | P1 | P2 | P3 | info` (see shared contract). ## Related Skills - **anti-slop** - code quality audit. When auditing a repo, run anti-slop for code and anti-ai-prose for docs. The two are deliberately complementary. - **update-docs** - keeps docs accurate and trimmed after feature changes. Anti-ai-prose focuses on voice; update-docs focuses on factual drift. - **prompt-generator** - structures a rough draft into an LLM prompt. If the user wants to generate cleaner prose next time, this helps shape the prompt. - **full-review** - orchestrates code-review, anti-slop, security-audit, and update-docs. Not wired into full-review by default - invoke anti-ai-prose separately when the repo has substantial prose worth auditing. - **code-review** - catches logic and correctness issues. Anti-ai-prose only touches prose; code-review handles the code itself. --- ## Rules 1. **Read the full piece before flagging.** A single `delve` in a 10,000-word book is not a pattern. Three in a paragraph is. Context determines severity. 2. **Never edit quoted material.** Original words from other authors stay as written. 3. **Respect genre conventions.** Travel writing, marketing, fiction, and academic prose have legitimate conventions that overlap with AI tells. Flag only when the writing is worse for the device, not because it matches a pattern. 4. **Every rewrite must be shorter or more specific.** Lateral synonym swaps are not improvements. If the rewrite is longer, the original was fine. 5. **Keep the voice of the author.** The goal is prose that sounds like a specific human, not a generic "good writing" rewrite. If you do not know the author's voice, leave stylistic calls alone and only flag the mechanical tells. 6. **Do not pad the report.** If there are three findings, list three. Not five. Not one inflated to three. 7. **Run the AI Self-Check** before returning any audit.