| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 1 | | adverbTags | | 0 | "Rory said finally [finally]" |
| | dialogueSentences | 48 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0.067 | | effectiveRatio | 0.042 | |
| 85.84% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1059 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 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) | |
| 81.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1059 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "familiar" | | 1 | "flicked" | | 2 | "eyebrow" | | 3 | "silence" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 91.67% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 2 | | narrationSentences | 50 | | matches | | 0 | "was surprised" | | 1 | "was ashamed" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 50 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 51 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 4 | | markdownWords | 37 | | totalWords | 1066 | | ratio | 0.035 | | matches | | 0 | "That" | | 1 | "I thought about you every week" | | 2 | "I was ashamed" | | 3 | "I walked past a bookshop window in Bloomsbury last autumn and saw a book you'd edited and stood there for twenty minutes pretending to read the back" |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 772 | | uniqueNames | 10 | | maxNameDensity | 2.2 | | worstName | "Rory" | | maxWindowNameDensity | 5 | | worstWindowName | "Meg" | | discoveredNames | | Rory | 17 | | Raven | 1 | | Nest | 1 | | Silas | 5 | | Redbreast | 1 | | London | 2 | | Meg | 15 | | Evan | 1 | | Cathays | 1 | | Bloomsbury | 1 |
| | persons | | 0 | "Rory" | | 1 | "Raven" | | 2 | "Nest" | | 3 | "Silas" | | 4 | "Meg" | | 5 | "Evan" |
| | places | | | globalScore | 0.399 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 36 | | 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 | 1066 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 20.11 | | std | 22.85 | | cv | 1.136 | | sampleLengths | | 0 | 96 | | 1 | 46 | | 2 | 26 | | 3 | 1 | | 4 | 45 | | 5 | 2 | | 6 | 59 | | 7 | 15 | | 8 | 22 | | 9 | 1 | | 10 | 3 | | 11 | 49 | | 12 | 7 | | 13 | 3 | | 14 | 3 | | 15 | 6 | | 16 | 45 | | 17 | 9 | | 18 | 47 | | 19 | 3 | | 20 | 51 | | 21 | 5 | | 22 | 6 | | 23 | 3 | | 24 | 6 | | 25 | 27 | | 26 | 13 | | 27 | 2 | | 28 | 4 | | 29 | 2 | | 30 | 42 | | 31 | 2 | | 32 | 11 | | 33 | 50 | | 34 | 10 | | 35 | 53 | | 36 | 6 | | 37 | 3 | | 38 | 2 | | 39 | 31 | | 40 | 7 | | 41 | 5 | | 42 | 4 | | 43 | 40 | | 44 | 4 | | 45 | 22 | | 46 | 5 | | 47 | 7 | | 48 | 92 | | 49 | 10 |
| |
| 77.19% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 50 | | matches | | 0 | "was surprised" | | 1 | "been clenched" | | 2 | "being told" | | 3 | "was ashamed" |
| |
| 44.96% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 129 | | matches | | 0 | "were identifying" | | 1 | "was moving" | | 2 | "was loosening" |
| |
| 5.16% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 83 | | ratio | 0.048 | | matches | | 0 | "It was the full name that did it — no one in London used the full name." | | 1 | "But the eyes — the eyes were Meg's." | | 2 | "Meg's eyebrows flicked up — a small, automatic gesture Rory had seen her make a thousand times over college essays, over bad dates, over the news that Rory was moving in with Evan." | | 3 | "Rory looked at her then, properly — at the blonde hair that didn't suit her, at the tattoo that did, at the mouth she had once known the exact weather of." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 606 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.02145214521452145 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0033003300330033004 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 12.84 | | std | 11.91 | | cv | 0.927 | | sampleLengths | | 0 | 30 | | 1 | 5 | | 2 | 29 | | 3 | 32 | | 4 | 19 | | 5 | 18 | | 6 | 6 | | 7 | 3 | | 8 | 26 | | 9 | 1 | | 10 | 2 | | 11 | 17 | | 12 | 14 | | 13 | 12 | | 14 | 2 | | 15 | 40 | | 16 | 8 | | 17 | 11 | | 18 | 3 | | 19 | 12 | | 20 | 15 | | 21 | 7 | | 22 | 1 | | 23 | 3 | | 24 | 12 | | 25 | 37 | | 26 | 7 | | 27 | 3 | | 28 | 3 | | 29 | 6 | | 30 | 33 | | 31 | 2 | | 32 | 10 | | 33 | 9 | | 34 | 13 | | 35 | 34 | | 36 | 3 | | 37 | 51 | | 38 | 5 | | 39 | 6 | | 40 | 3 | | 41 | 4 | | 42 | 2 | | 43 | 27 | | 44 | 7 | | 45 | 6 | | 46 | 2 | | 47 | 4 | | 48 | 2 | | 49 | 19 |
| |
| 58.63% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.40963855421686746 | | totalSentences | 83 | | uniqueOpeners | 34 | |
| 79.37% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 42 | | matches | | 0 | "Somewhere in the back of" |
| | ratio | 0.024 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 12 | | totalSentences | 42 | | matches | | 0 | "She hadn't meant to stop." | | 1 | "She had meant to climb" | | 2 | "She hung her jacket on" | | 3 | "He nodded once, the signet" | | 4 | "He never asked." | | 5 | "It was the full name" | | 6 | "It had always been the" | | 7 | "She hadn't said his name" | | 8 | "They sat with that." | | 9 | "She wanted to say *I" | | 10 | "She wanted to say *I" | | 11 | "She wanted to say *I" |
| | ratio | 0.286 | |
| 31.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 42 | | matches | | 0 | "The green neon sign hummed" | | 1 | "She hadn't meant to stop." | | 2 | "She had meant to climb" | | 3 | "She hung her jacket on" | | 4 | "He nodded once, the signet" | | 5 | "That was the thing about" | | 6 | "He never asked." | | 7 | "It was the full name" | | 8 | "Aurora belonged to a different" | | 9 | "The woman at the bar" | | 10 | "Hazel, too wide, always a" | | 11 | "The word came out flat," | | 12 | "Meg's smile was careful, a" | | 13 | "Rory slid onto the stool" | | 14 | "Silas set down the whisky" | | 15 | "Meg's eyebrows flicked up —" | | 16 | "It had always been the" | | 17 | "Rory said, before the silence" | | 18 | "Meg turned the stem of" | | 19 | "Meg laughed, and for a" |
| | ratio | 0.857 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 42 | | matches | (empty) | | ratio | 0 | |
| 28.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 25 | | technicalSentenceCount | 4 | | matches | | 0 | "Silas set down the whisky without looking at either of them, and moved off to the other end of the bar with the instinctive tact of a man who had spent thirty y…" | | 1 | "Meg nodded, slowly, as if she had rehearsed receiving this answer and was surprised to find it still cut." | | 2 | "She wanted to say *I walked past a bookshop window in Bloomsbury last autumn and saw a book you'd edited and stood there for twenty minutes pretending to read t…" | | 3 | "Rory raised two fingers again, and Silas, from the far end of the bar, nodded without turning, as if he had been listening all along and knew exactly what slowl…" |
| |
| 91.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 1 | | matches | | 0 | "Meg smiled, and the smile held a long history of not being told things" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 10 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 48 | | tagDensity | 0.208 | | leniency | 0.417 | | rawRatio | 0.1 | | effectiveRatio | 0.042 | |