| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 20 | | adverbTagCount | 1 | | adverbTags | | 0 | "Aurora leaned back [back]" |
| | dialogueSentences | 63 | | tagDensity | 0.317 | | leniency | 0.635 | | rawRatio | 0.05 | | effectiveRatio | 0.032 | |
| 87.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1246 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "very" | | 1 | "carefully" | | 2 | "really" |
| |
| 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) | |
| 91.97% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1246 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 54 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 54 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 78 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1251 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 19 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 42 | | wordCount | 641 | | uniqueNames | 13 | | maxNameDensity | 2.65 | | worstName | "Eva" | | maxWindowNameDensity | 5.5 | | worstWindowName | "Eva" | | discoveredNames | | Aurora | 12 | | Frith | 1 | | Street | 1 | | London | 1 | | Nest | 1 | | Golden | 1 | | Empress | 1 | | Eva | 17 | | Silas | 3 | | Bekele | 1 | | Splott | 1 | | Nina | 1 | | Simone | 1 |
| | persons | | 0 | "Aurora" | | 1 | "Nest" | | 2 | "Eva" | | 3 | "Silas" | | 4 | "Bekele" | | 5 | "Nina" | | 6 | "Simone" |
| | places | | 0 | "Frith" | | 1 | "Street" | | 2 | "London" | | 3 | "Golden" | | 4 | "Splott" |
| | globalScore | 0.174 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 32 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1251 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 21.57 | | std | 23.39 | | cv | 1.084 | | sampleLengths | | 0 | 63 | | 1 | 25 | | 2 | 1 | | 3 | 11 | | 4 | 53 | | 5 | 53 | | 6 | 14 | | 7 | 6 | | 8 | 19 | | 9 | 45 | | 10 | 6 | | 11 | 37 | | 12 | 1 | | 13 | 25 | | 14 | 9 | | 15 | 2 | | 16 | 8 | | 17 | 30 | | 18 | 2 | | 19 | 17 | | 20 | 28 | | 21 | 81 | | 22 | 4 | | 23 | 1 | | 24 | 1 | | 25 | 16 | | 26 | 6 | | 27 | 28 | | 28 | 4 | | 29 | 13 | | 30 | 4 | | 31 | 39 | | 32 | 3 | | 33 | 3 | | 34 | 23 | | 35 | 47 | | 36 | 3 | | 37 | 28 | | 38 | 27 | | 39 | 8 | | 40 | 31 | | 41 | 1 | | 42 | 4 | | 43 | 67 | | 44 | 15 | | 45 | 2 | | 46 | 74 | | 47 | 19 | | 48 | 20 | | 49 | 1 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 54 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 104 | | matches | (empty) | |
| 23.81% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 1 | | flaggedSentences | 4 | | totalSentences | 96 | | ratio | 0.042 | | matches | | 0 | "There was a half-second where nothing happened in her face at all, and then it came apart — the mouth first, then the eyes, and she pressed the back of her hand to her lips as though she'd tasted something." | | 1 | "\"Don't.\" Eva laughed, and it was wrong — pitched too high, cut off too fast." | | 2 | "Eva Bekele, who at nineteen had climbed the scaffolding on the science block at three in the morning to hang a bedsheet that said SEND HELP; who'd once talked a bouncer in Splott into letting them both in free by convincing him she was a food safety inspector." | | 3 | "Really looked — at the scraped-back hair and the coat still buttoned and the shoes that cost more than a month of deliveries." |
| |
| 95.48% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 642 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.045171339563862926 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.00778816199376947 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 13.03 | | std | 14.46 | | cv | 1.11 | | sampleLengths | | 0 | 29 | | 1 | 34 | | 2 | 25 | | 3 | 1 | | 4 | 11 | | 5 | 27 | | 6 | 26 | | 7 | 17 | | 8 | 5 | | 9 | 10 | | 10 | 2 | | 11 | 19 | | 12 | 14 | | 13 | 6 | | 14 | 12 | | 15 | 7 | | 16 | 5 | | 17 | 40 | | 18 | 3 | | 19 | 3 | | 20 | 4 | | 21 | 33 | | 22 | 1 | | 23 | 15 | | 24 | 10 | | 25 | 9 | | 26 | 2 | | 27 | 8 | | 28 | 17 | | 29 | 13 | | 30 | 2 | | 31 | 10 | | 32 | 7 | | 33 | 2 | | 34 | 26 | | 35 | 10 | | 36 | 4 | | 37 | 48 | | 38 | 7 | | 39 | 12 | | 40 | 4 | | 41 | 1 | | 42 | 1 | | 43 | 9 | | 44 | 7 | | 45 | 6 | | 46 | 8 | | 47 | 20 | | 48 | 4 | | 49 | 7 |
| |
| 62.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.4583333333333333 | | totalSentences | 96 | | uniqueOpeners | 44 | |
| 75.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 44 | | matches | | 0 | "Really looked — at the" |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 8 | | totalSentences | 44 | | matches | | 0 | "She shouldered the door of" | | 1 | "His eyes went past her," | | 2 | "Her wet trainers squeaked on" | | 3 | "She had laughed with her" | | 4 | "She had never once in" | | 5 | "She said it flatly, the" | | 6 | "She hadn't planned to put" | | 7 | "Her voice did something she" |
| | ratio | 0.182 | |
| 28.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 38 | | totalSentences | 44 | | matches | | 0 | "The rain had followed Aurora" | | 1 | "She shouldered the door of" | | 2 | "Something in the way he" | | 3 | "Silas stood behind the bar" | | 4 | "His eyes went past her," | | 5 | "A woman sat alone at" | | 6 | "Hair scraped back so tight" | | 7 | "Aurora put the bags on" | | 8 | "Silas set the glass down" | | 9 | "The woman looked up then." | | 10 | "Aurora crossed the room." | | 11 | "Her wet trainers squeaked on" | | 12 | "Eva laughed, and it was" | | 13 | "Eva pushed the wine glass" | | 14 | "Eva gestured at the chair" | | 15 | "Eva had been the loudest" | | 16 | "That was the thing." | | 17 | "Eva Bekele, who at nineteen" | | 18 | "She had laughed with her" | | 19 | "She had never once in" |
| | ratio | 0.864 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 44 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 2 | | matches | | 0 | "There was a half-second where nothing happened in her face at all, and then it came apart — the mouth first, then the eyes, and she pressed the back of her hand…" | | 1 | "Eva Bekele, who at nineteen had climbed the scaffolding on the science block at three in the morning to hang a bedsheet that said SEND HELP; who'd once talked a…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 20 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 8 | | fancyCount | 2 | | fancyTags | | 0 | "Eva laughed (laugh)" | | 1 | "Eva pressed (press)" |
| | dialogueSentences | 63 | | tagDensity | 0.127 | | leniency | 0.254 | | rawRatio | 0.25 | | effectiveRatio | 0.063 | |