| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "Rory gestured vaguely [vaguely]" |
| | dialogueSentences | 64 | | tagDensity | 0.219 | | leniency | 0.438 | | rawRatio | 0.071 | | effectiveRatio | 0.031 | |
| 90.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2160 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slightly" | | 1 | "very" | | 2 | "quickly" |
| |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 83.80% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2160 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "flickered" | | 1 | "stomach" | | 2 | "flicker" | | 3 | "calculate" | | 4 | "silence" | | 5 | "measured" | | 6 | "weight" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 138 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 138 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 188 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 78 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 13 | | totalWords | 2164 | | ratio | 0.006 | | matches | | 0 | "u alive?" | | 1 | "Sorry for the Irish exit lol." | | 2 | "Sorry for the Irish exit." |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 83 | | wordCount | 1597 | | uniqueNames | 21 | | maxNameDensity | 1.75 | | worstName | "Niamh" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Niamh" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Yu-Fei | 1 | | Jamie | 5 | | Guinness | 1 | | Wednesday | 2 | | Indian | 1 | | Ocean | 1 | | Silas | 3 | | Eva | 2 | | Niamh | 28 | | Sullivan | 1 | | Rory | 27 | | Cardiff | 1 | | Cathays | 1 | | Brussels | 2 | | Irish | 1 | | Greek | 1 | | Street | 1 | | Soho | 1 | | Silence | 1 |
| | persons | | 0 | "Raven" | | 1 | "Yu-Fei" | | 2 | "Jamie" | | 3 | "Silas" | | 4 | "Eva" | | 5 | "Niamh" | | 6 | "Sullivan" | | 7 | "Rory" | | 8 | "Irish" |
| | places | | 0 | "Indian" | | 1 | "Ocean" | | 2 | "Cardiff" | | 3 | "Cathays" | | 4 | "Brussels" | | 5 | "Greek" | | 6 | "Street" | | 7 | "Soho" |
| | globalScore | 0.623 | | windowScore | 0.5 | |
| 36.36% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 4 | | matches | | 0 | "quite read" | | 1 | "Jamie, who had apparently discovered how to s" | | 2 | "quite a laugh" | | 3 | "as if seeing it for the first time" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.462 | | wordCount | 2164 | | matches | | 0 | "not much, but enough to notice" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 188 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 85 | | mean | 25.46 | | std | 26.19 | | cv | 1.029 | | sampleLengths | | 0 | 60 | | 1 | 19 | | 2 | 37 | | 3 | 70 | | 4 | 93 | | 5 | 70 | | 6 | 7 | | 7 | 47 | | 8 | 3 | | 9 | 97 | | 10 | 7 | | 11 | 36 | | 12 | 82 | | 13 | 71 | | 14 | 13 | | 15 | 65 | | 16 | 3 | | 17 | 82 | | 18 | 10 | | 19 | 3 | | 20 | 26 | | 21 | 6 | | 22 | 3 | | 23 | 2 | | 24 | 3 | | 25 | 30 | | 26 | 4 | | 27 | 5 | | 28 | 9 | | 29 | 22 | | 30 | 2 | | 31 | 29 | | 32 | 10 | | 33 | 6 | | 34 | 35 | | 35 | 45 | | 36 | 3 | | 37 | 3 | | 38 | 10 | | 39 | 32 | | 40 | 3 | | 41 | 6 | | 42 | 3 | | 43 | 63 | | 44 | 54 | | 45 | 6 | | 46 | 2 | | 47 | 26 | | 48 | 7 | | 49 | 10 |
| |
| 97.64% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 138 | | matches | | 0 | "were heeled" | | 1 | "were gone" | | 2 | "were painted" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 265 | | matches | | 0 | "was talking" | | 1 | "was tasting" | | 2 | "was happening" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 0 | | flaggedSentences | 11 | | totalSentences | 188 | | ratio | 0.059 | | matches | | 0 | "The bartender — a narrow-faced kid named Jamie who'd started three weeks ago and still couldn't pour a Guinness without panicking — jerked his chin toward the back." | | 1 | "She pulled a pint glass from the rack behind the bar — Jamie had given up protesting this weeks ago — and tilted it under the tap." | | 2 | "She took her pint to a corner booth and sat with her back against the wall — a habit she'd picked up without noticing, one that Silas had pointed out to her once with a look she couldn't quite read." | | 3 | "She knew that laugh the way she knew the crescent scar on her left wrist — by feel, without looking, the shape of it pressed into some deep layer of muscle memory." | | 4 | "Auburn hair — no, darker, closer to chestnut now, cropped short where it used to fall past her shoulder blades." | | 5 | "She could finish her drink in the back room — she had the code for the bookshelf panel — and wait until Niamh settled her tab and walked back into whatever chrome-and-glass life had brought her to Soho on a Wednesday evening." | | 6 | "Niamh's face opened like a door flung wide, and then just as quickly something shuttered behind it — a flicker of calculation, of adjustment, that Rory had never seen before." | | 7 | "Her face was thinner, the cheekbones more pronounced, and she wore makeup now — not much, but enough to notice." | | 8 | "Not judgmentally — or if it was judgment, she'd gotten skilled enough to bury it beneath three or four layers of something warmer." | | 9 | "Silas called it her barrister gene — the inherited ability to answer a question without answering it." | | 10 | "\"I'm sorry I left the way I did. I'm sorry I never called. I'm sorry the version of me you're looking at isn't the version you lost, and I'm sorry I can't undo the parts of the last seven years that made me into this.\" She gestured at herself — the blazer, the short hair, the painted nails." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1598 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.02252816020025031 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.006257822277847309 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 188 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 188 | | mean | 11.51 | | std | 11.24 | | cv | 0.976 | | sampleLengths | | 0 | 23 | | 1 | 37 | | 2 | 11 | | 3 | 8 | | 4 | 28 | | 5 | 9 | | 6 | 12 | | 7 | 5 | | 8 | 3 | | 9 | 23 | | 10 | 27 | | 11 | 7 | | 12 | 9 | | 13 | 11 | | 14 | 29 | | 15 | 9 | | 16 | 28 | | 17 | 40 | | 18 | 5 | | 19 | 6 | | 20 | 19 | | 21 | 7 | | 22 | 2 | | 23 | 2 | | 24 | 11 | | 25 | 32 | | 26 | 3 | | 27 | 9 | | 28 | 20 | | 29 | 22 | | 30 | 19 | | 31 | 11 | | 32 | 16 | | 33 | 3 | | 34 | 4 | | 35 | 12 | | 36 | 24 | | 37 | 2 | | 38 | 9 | | 39 | 41 | | 40 | 8 | | 41 | 10 | | 42 | 12 | | 43 | 3 | | 44 | 5 | | 45 | 21 | | 46 | 42 | | 47 | 13 | | 48 | 4 | | 49 | 4 |
| |
| 40.43% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.2872340425531915 | | totalSentences | 188 | | uniqueOpeners | 54 | |
| 28.01% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 119 | | matches | | 0 | "Just the raw, uncomfortable thing" |
| | ratio | 0.008 | |
| 98.99% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 119 | | matches | | 0 | "she called out, sliding the" | | 1 | "Her calves ached." | | 2 | "She'd earned a drink and" | | 3 | "She pulled a pint glass" | | 4 | "She could close her eyes" | | 5 | "She took her pint to" | | 6 | "She pulled out her phone." | | 7 | "She knew that laugh the" | | 8 | "She wore a charcoal blazer" | | 9 | "She sat very still." | | 10 | "She said something Rory couldn't" | | 11 | "She'd sent a text to" | | 12 | "She could leave." | | 13 | "Her jacket was on the" | | 14 | "She could finish her drink" | | 15 | "She was already off the" | | 16 | "Her shoes were heeled and" | | 17 | "Her face was thinner, the" | | 18 | "She set her wine on" | | 19 | "It came out harder than" |
| | ratio | 0.303 | |
| 18.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 105 | | totalSentences | 119 | | matches | | 0 | "The green neon sign buzzed" | | 1 | "Rory pushed through the door" | | 2 | "she called out, sliding the" | | 3 | "The bartender — a narrow-faced" | | 4 | "Rory peeled off her rain" | | 5 | "The delivery shift was done." | | 6 | "Her calves ached." | | 7 | "She'd earned a drink and" | | 8 | "She pulled a pint glass" | | 9 | "The bar was half-full for" | | 10 | "A couple argued in low" | | 11 | "The old maps on the" | | 12 | "Rory had memorized most of" | | 13 | "She could close her eyes" | | 14 | "She took her pint to" | | 15 | "She pulled out her phone." | | 16 | "A text from Eva: *u" | | 17 | "That was when she heard" | | 18 | "A single note of genuine" | | 19 | "She knew that laugh the" |
| | ratio | 0.882 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 119 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 8 | | matches | | 0 | "The green neon sign buzzed and flickered against the wet pavement, throwing sickly light across puddles that hadn't been there an hour ago." | | 1 | "The bartender — a narrow-faced kid named Jamie who'd started three weeks ago and still couldn't pour a Guinness without panicking — jerked his chin toward the b…" | | 2 | "She wore a charcoal blazer that fit her like money, and she held her wine glass by the stem, which was new." | | 3 | "She set her wine on the table with exaggerated care, as if it were evidence of something." | | 4 | "She thought about one of those tight, clever deflections she was good at, the ones that made people laugh and moved the conversation sideways." | | 5 | "The bookshelf against the far wall that was, if you knew where to push, not a bookshelf at all." | | 6 | "She looked at Rory with an expression that had no calculation in it, no layers, no Brussels polish." | | 7 | "He glanced at Rory, then at Niamh, then back at Rory with an expression that communicated, in the economical way of someone who'd spent decades reading rooms fo…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 2 | | fancyTags | | 0 | "she called out (call out)" | | 1 | "Niamh repeated (repeat)" |
| | dialogueSentences | 64 | | tagDensity | 0.156 | | leniency | 0.313 | | rawRatio | 0.2 | | effectiveRatio | 0.063 | |