| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 18 | | tagDensity | 0.111 | | leniency | 0.222 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2035 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 58.23% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2035 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "almost alive" | | 1 | "warmth" | | 2 | "pulse" | | 3 | "pulsed" | | 4 | "throb" | | 5 | "vibrated" | | 6 | "echo" | | 7 | "quickened" | | 8 | "silence" | | 9 | "raced" | | 10 | "whisper" | | 11 | "familiar" | | 12 | "trembled" |
| |
| 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 | 261 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 261 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 279 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 31 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2033 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 88.81% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1961 | | uniqueNames | 13 | | maxNameDensity | 1.22 | | worstName | "Rory" | | maxWindowNameDensity | 2 | | worstWindowName | "Rory" | | discoveredNames | | Aurora | 1 | | Richmond | 2 | | Park | 1 | | Heathrow | 1 | | Heartstone | 5 | | Cardiff | 2 | | Golden | 2 | | Empress | 2 | | Yu-Fei | 1 | | Rory | 24 | | London | 2 | | University | 1 | | Evan | 1 |
| | persons | | 0 | "Heartstone" | | 1 | "Yu-Fei" | | 2 | "Rory" | | 3 | "Evan" |
| | places | | 0 | "Richmond" | | 1 | "Park" | | 2 | "Heathrow" | | 3 | "Cardiff" | | 4 | "London" |
| | globalScore | 0.888 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 153 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared behind a rectangle of glass" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.492 | | wordCount | 2033 | | matches | | 0 | "not swinging but straining, its chain stretched at an angle as if caught by a" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 279 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 195 | | mean | 10.43 | | std | 11.45 | | cv | 1.098 | | sampleLengths | | 0 | 14 | | 1 | 50 | | 2 | 5 | | 3 | 2 | | 4 | 22 | | 5 | 11 | | 6 | 10 | | 7 | 31 | | 8 | 8 | | 9 | 7 | | 10 | 47 | | 11 | 28 | | 12 | 1 | | 13 | 1 | | 14 | 1 | | 15 | 10 | | 16 | 53 | | 17 | 8 | | 18 | 30 | | 19 | 4 | | 20 | 1 | | 21 | 6 | | 22 | 8 | | 23 | 38 | | 24 | 2 | | 25 | 47 | | 26 | 6 | | 27 | 7 | | 28 | 11 | | 29 | 6 | | 30 | 4 | | 31 | 21 | | 32 | 2 | | 33 | 2 | | 34 | 6 | | 35 | 9 | | 36 | 6 | | 37 | 1 | | 38 | 3 | | 39 | 30 | | 40 | 3 | | 41 | 3 | | 42 | 31 | | 43 | 12 | | 44 | 3 | | 45 | 24 | | 46 | 4 | | 47 | 2 | | 48 | 3 | | 49 | 13 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 261 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 320 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 279 | | ratio | 0.004 | | matches | | 0 | "London should have filled the gap—tyres on wet tarmac, aircraft grinding towards Heathrow, a siren chasing itself across the borough—but the night swallowed each sound before it reached her." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 438 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.02054794520547945 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.00228310502283105 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 279 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 279 | | mean | 7.29 | | std | 5.04 | | cv | 0.692 | | sampleLengths | | 0 | 14 | | 1 | 15 | | 2 | 6 | | 3 | 29 | | 4 | 5 | | 5 | 3 | | 6 | 4 | | 7 | 4 | | 8 | 13 | | 9 | 2 | | 10 | 2 | | 11 | 7 | | 12 | 10 | | 13 | 11 | | 14 | 13 | | 15 | 7 | | 16 | 8 | | 17 | 7 | | 18 | 6 | | 19 | 22 | | 20 | 5 | | 21 | 14 | | 22 | 4 | | 23 | 11 | | 24 | 3 | | 25 | 10 | | 26 | 1 | | 27 | 1 | | 28 | 1 | | 29 | 10 | | 30 | 5 | | 31 | 7 | | 32 | 14 | | 33 | 15 | | 34 | 12 | | 35 | 8 | | 36 | 11 | | 37 | 13 | | 38 | 4 | | 39 | 2 | | 40 | 4 | | 41 | 1 | | 42 | 6 | | 43 | 8 | | 44 | 9 | | 45 | 14 | | 46 | 15 | | 47 | 2 | | 48 | 15 | | 49 | 14 |
| |
| 44.91% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.29454545454545455 | | totalSentences | 275 | | uniqueOpeners | 81 | |
| 28.86% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 231 | | matches | | 0 | "Then the next." | | 1 | "Bright, delicate, familiar." |
| | ratio | 0.009 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 54 | | totalSentences | 231 | | matches | | 0 | "Its red tail-lights shrank along" | | 1 | "She pulled her coat closed" | | 2 | "Her voice fell flat at" | | 3 | "It had warmed during the" | | 4 | "She took it out." | | 5 | "She slipped through the narrow" | | 6 | "She found the first one" | | 7 | "It grew from a crack" | | 8 | "They formed a trail." | | 9 | "She could send the unknown" | | 10 | "She could hand the pendant" | | 11 | "Its beam found moss, roots" | | 12 | "Her hand closed around the" | | 13 | "Their fronds bowed, sprang upright," | | 14 | "She wiped it away and" | | 15 | "She swung round." | | 16 | "Her light reached twenty metres" | | 17 | "Her phone vibrated." | | 18 | "She had entered the park" | | 19 | "She opened the taxi app." |
| | ratio | 0.234 | |
| 72.55% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 179 | | totalSentences | 231 | | matches | | 0 | "The taxi left Aurora beside" | | 1 | "Its red tail-lights shrank along" | | 2 | "The engine noise went with" | | 3 | "London should have filled the" | | 4 | "Rory checked the message again." | | 5 | "The time at the top" | | 6 | "She pulled her coat closed" | | 7 | "A gravel path ran between" | | 8 | "Her voice fell flat at" | | 9 | "The Heartstone rested beneath her" | | 10 | "It had warmed during the" | | 11 | "Heat travelled through the silver" | | 12 | "She took it out." | | 13 | "The thumbnail-sized crimson stone held" | | 14 | "The glow tightened and loosened" | | 15 | "She slipped through the narrow" | | 16 | "The smell changed at once." | | 17 | "Leaves lay slick across the" | | 18 | "The park map on her" | | 19 | "The message had included no" |
| | ratio | 0.775 | |
| 21.65% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 231 | | matches | | 0 | "Now it felt almost alive." |
| | ratio | 0.004 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 74 | | technicalSentenceCount | 4 | | matches | | 0 | "It had warmed during the taxi ride, a soft beat against her breastbone that had grown stronger as they crossed into Richmond." | | 1 | "The trees themselves had become standing stones, grey bark ridged and hardened, each one bearing shapes that might have been knots or faces." | | 2 | "The pendant pulled towards the centre of the grove, not swinging but straining, its chain stretched at an angle as if caught by an unseen magnet." | | 3 | "GOLDEN EMPRESS The letters faced the wrong way, as if Rory viewed them from inside the restaurant." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |