| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 2 | | adverbTags | | 0 | "She sat back [back]" | | 1 | "she said aloud [aloud]" |
| | dialogueSentences | 43 | | tagDensity | 0.163 | | leniency | 0.326 | | rawRatio | 0.286 | | effectiveRatio | 0.093 | |
| 85.44% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1030 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "gently" | | 1 | "slowly" | | 2 | "slightly" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 95.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1030 | | totalAiIsms | 1 | | 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 | 65 | | matches | (empty) | |
| 98.90% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 65 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 39 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1038 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 16 | | wordCount | 703 | | uniqueNames | 5 | | maxNameDensity | 1 | | worstName | "Patel" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Aldwych | 1 | | Station | 1 | | Patel | 7 | | Tube | 1 | | Quinn | 6 |
| | persons | | | places | | | globalScore | 1 | | windowScore | 1 | |
| 90.48% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 42 | | glossingSentenceCount | 1 | | matches | | 0 | "Cold, obviously, but more than cold" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1038 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 50 | | mean | 20.76 | | std | 23.11 | | cv | 1.113 | | sampleLengths | | 0 | 49 | | 1 | 6 | | 2 | 37 | | 3 | 3 | | 4 | 39 | | 5 | 46 | | 6 | 3 | | 7 | 45 | | 8 | 2 | | 9 | 64 | | 10 | 7 | | 11 | 5 | | 12 | 7 | | 13 | 6 | | 14 | 3 | | 15 | 51 | | 16 | 4 | | 17 | 6 | | 18 | 2 | | 19 | 80 | | 20 | 6 | | 21 | 12 | | 22 | 18 | | 23 | 5 | | 24 | 6 | | 25 | 72 | | 26 | 4 | | 27 | 7 | | 28 | 24 | | 29 | 24 | | 30 | 3 | | 31 | 86 | | 32 | 7 | | 33 | 11 | | 34 | 22 | | 35 | 5 | | 36 | 74 | | 37 | 26 | | 38 | 1 | | 39 | 1 | | 40 | 38 | | 41 | 8 | | 42 | 15 | | 43 | 22 | | 44 | 40 | | 45 | 3 | | 46 | 18 | | 47 | 9 | | 48 | 3 | | 49 | 3 |
| |
| 89.07% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 65 | | matches | | 0 | "were blown" | | 1 | "been walked" | | 2 | "was pushed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 111 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 2 | | flaggedSentences | 8 | | totalSentences | 101 | | ratio | 0.079 | | matches | | 0 | "The rain had stopped an hour ago, but the platform still smelled of it — wet concrete, wet iron, something mineral and old underneath." | | 1 | "The mark at the centre was a fingerprint — a thumb, she thought, pressed flat, the whorl set into a residue that glittered faintly when the torch caught it at an angle." | | 2 | "The parchment in his eyes wasn't a trick of the light; the sclera had taken on the grain of dried paper." | | 3 | "Cold, obviously, but more than cold — dry in a way skin shouldn't be." | | 4 | "The trouser cuffs were filthy from the tiles, but the shoes — brown brogues, expensive — were clean on the soles." | | 5 | "Her torch beam found an old tiled sign — TO TRAINS — and beneath it, the scuff and shuffle of modern footprints where SOCO had come through." | | 6 | "The tiles here were white; the dust here was white." | | 7 | "Patel gave her the look he reserved for her worse hunches — polite, patient, quietly unconvinced." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 695 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.033093525179856115 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.011510791366906475 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 10.28 | | std | 8.6 | | cv | 0.836 | | sampleLengths | | 0 | 24 | | 1 | 25 | | 2 | 6 | | 3 | 18 | | 4 | 11 | | 5 | 8 | | 6 | 3 | | 7 | 30 | | 8 | 9 | | 9 | 2 | | 10 | 26 | | 11 | 5 | | 12 | 13 | | 13 | 3 | | 14 | 30 | | 15 | 15 | | 16 | 2 | | 17 | 9 | | 18 | 23 | | 19 | 20 | | 20 | 3 | | 21 | 9 | | 22 | 7 | | 23 | 4 | | 24 | 1 | | 25 | 7 | | 26 | 2 | | 27 | 4 | | 28 | 3 | | 29 | 12 | | 30 | 32 | | 31 | 2 | | 32 | 2 | | 33 | 3 | | 34 | 4 | | 35 | 6 | | 36 | 2 | | 37 | 21 | | 38 | 21 | | 39 | 12 | | 40 | 10 | | 41 | 14 | | 42 | 2 | | 43 | 6 | | 44 | 12 | | 45 | 15 | | 46 | 3 | | 47 | 5 | | 48 | 6 | | 49 | 7 |
| |
| 80.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.5148514851485149 | | totalSentences | 101 | | uniqueOpeners | 52 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 54 | | matches | | 0 | "Pale grey, almost chalky." |
| | ratio | 0.019 | |
| 79.26% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 54 | | matches | | 0 | "He was young enough to" | | 1 | "His eyes were open and" | | 2 | "She ran her torch across" | | 3 | "He followed her beam." | | 4 | "She pulled her own from" | | 5 | "His pupils were blown wide" | | 6 | "She touched the back of" | | 7 | "She sat back on her" | | 8 | "She swept the torch down" | | 9 | "She turned one gently with" | | 10 | "Her torch beam found an" | | 11 | "She walked the other way," | | 12 | "she said aloud" | | 13 | "She didn't answer yet." | | 14 | "She came back to the" | | 15 | "It was pushed outward, radially," | | 16 | "She thought about the mud." | | 17 | "Her watch ticked against her" | | 18 | "She rolled the cuff down" |
| | ratio | 0.352 | |
| 34.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 46 | | totalSentences | 54 | | matches | | 0 | "The rain had stopped an" | | 1 | "Quinn ducked beneath the tape" | | 2 | "DC Patel was leaning against" | | 3 | "He was young enough to" | | 4 | "Quinn hadn't been young for" | | 5 | "Patel nodded toward the corpse" | | 6 | "The dead man lay on" | | 7 | "His eyes were open and" | | 8 | "Patel clicked the biro" | | 9 | "She ran her torch across" | | 10 | "The paint had gone to" | | 11 | "Dust wiped clean." | | 12 | "A single smudge of something" | | 13 | "He followed her beam." | | 14 | "Quinn straightened and stepped closer," | | 15 | "The mark at the centre" | | 16 | "Something in between." | | 17 | "She pulled her own from" | | 18 | "The parchment in his eyes" | | 19 | "His pupils were blown wide" |
| | ratio | 0.852 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 71.43% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 3 | | matches | | 0 | "The dead man lay on his back, one arm flung wide, the other folded across his chest as though he'd been trying to hold something in." | | 1 | "The mark at the centre was a fingerprint — a thumb, she thought, pressed flat, the whorl set into a residue that glittered faintly when the torch caught it at a…" | | 2 | "A fine pattern of it lay on the platform below, a crescent of grey powder that matched the mud crusted on the dead man's shoes." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 43 | | tagDensity | 0.047 | | leniency | 0.093 | | rawRatio | 0 | | effectiveRatio | 0 | |