| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 17 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.459 | | leniency | 0.919 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.58% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1460 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1460 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 1 | | narrationSentences | 65 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 65 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 84 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 92 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1472 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 24 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 927 | | uniqueNames | 8 | | maxNameDensity | 0.76 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Lucien" | | discoveredNames | | Lucien | 7 | | Rory | 7 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Eva | 4 | | Urdu | 1 | | Four | 3 |
| | persons | | | places | | 0 | "Charing" | | 1 | "Cross" | | 2 | "Road" | | 3 | "Urdu" | | 4 | "Four" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | 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 | 1472 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 84 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 35.05 | | std | 39.29 | | cv | 1.121 | | sampleLengths | | 0 | 37 | | 1 | 5 | | 2 | 55 | | 3 | 39 | | 4 | 17 | | 5 | 87 | | 6 | 3 | | 7 | 37 | | 8 | 135 | | 9 | 4 | | 10 | 17 | | 11 | 20 | | 12 | 109 | | 13 | 37 | | 14 | 7 | | 15 | 15 | | 16 | 87 | | 17 | 43 | | 18 | 5 | | 19 | 21 | | 20 | 9 | | 21 | 2 | | 22 | 37 | | 23 | 7 | | 24 | 104 | | 25 | 19 | | 26 | 1 | | 27 | 7 | | 28 | 5 | | 29 | 3 | | 30 | 179 | | 31 | 35 | | 32 | 20 | | 33 | 83 | | 34 | 29 | | 35 | 21 | | 36 | 51 | | 37 | 10 | | 38 | 13 | | 39 | 9 | | 40 | 34 | | 41 | 14 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 65 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 155 | | matches | | 0 | "was digging" | | 1 | "was holding" |
| |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 84 | | ratio | 0.071 | | matches | | 0 | "Four months of discipline — of not looking up from a delivery run every time a tall blond man crossed Charing Cross Road, of not drafting messages and deleting them, of teaching herself the specific dull ache of getting what she’d asked for — and a cat undid it in a single gesture." | | 1 | "Not physically — he was barely taller than her — but he arrived in a room the way weather does." | | 2 | "His cane was hooked over his forearm — the ivory handle catching the hallway bulb, the thing that was not entirely a cane." | | 3 | "Of course he sat properly, in the hard-backed chair by the window, so that the whole time she was digging through Eva’s cabinets for the kit he could watch her with that mismatched stare — amber on the left, black on the right, and the black one always the one that made her want to look away and never did — and she would have to feel it between her shoulder blades." | | 4 | "“He’s dead. He’s been dead since March, which is not the warning.” He rotated his forearm into the lamplight, and she saw the cut was clean and shallow and deliberate — the kind you take reaching for something that isn’t yours." | | 5 | "And she laughed — one short, undignified sound she couldn’t take back — and did not let go of his arm." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 920 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 30 | | adverbRatio | 0.03260869565217391 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.013043478260869565 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 84 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 84 | | mean | 17.52 | | std | 18.49 | | cv | 1.055 | | sampleLengths | | 0 | 37 | | 1 | 5 | | 2 | 26 | | 3 | 29 | | 4 | 3 | | 5 | 4 | | 6 | 32 | | 7 | 17 | | 8 | 6 | | 9 | 28 | | 10 | 53 | | 11 | 3 | | 12 | 37 | | 13 | 8 | | 14 | 20 | | 15 | 31 | | 16 | 36 | | 17 | 23 | | 18 | 17 | | 19 | 4 | | 20 | 17 | | 21 | 20 | | 22 | 2 | | 23 | 72 | | 24 | 35 | | 25 | 2 | | 26 | 35 | | 27 | 7 | | 28 | 10 | | 29 | 5 | | 30 | 70 | | 31 | 17 | | 32 | 14 | | 33 | 5 | | 34 | 4 | | 35 | 20 | | 36 | 5 | | 37 | 5 | | 38 | 16 | | 39 | 9 | | 40 | 2 | | 41 | 30 | | 42 | 7 | | 43 | 7 | | 44 | 11 | | 45 | 93 | | 46 | 11 | | 47 | 8 | | 48 | 1 | | 49 | 7 |
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| 71.83% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4880952380952381 | | totalSentences | 84 | | uniqueOpeners | 41 | |
| 65.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 51 | | matches | | 0 | "Of course he sat properly," |
| | ratio | 0.02 | |
| 78.82% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 51 | | matches | | 0 | "She worked the three deadbolts," | | 1 | "She’d forgotten how much space" | | 2 | "His cane was hooked over" | | 3 | "She found the kit under" | | 4 | "He flexed and unflexed the" | | 5 | "She didn’t cross the room." | | 6 | "He turned his head a" | | 7 | "He was not smiling" | | 8 | "She unwound a length of" | | 9 | "She looked up." | | 10 | "He rotated his forearm into" | | 11 | "He set the paper bag" | | 12 | "she said, and her voice" | | 13 | "She set the kit aside" | | 14 | "He was quiet for a" | | 15 | "Her thumb, without her deciding" | | 16 | "He looked at her hand" | | 17 | "he said, quietly, and only" |
| | ratio | 0.353 | |
| 58.04% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 51 | | matches | | 0 | "The chain caught at three" | | 1 | "Lucien’s voice came unhurried, as" | | 2 | "Rory didn’t move." | | 3 | "Ptolemy had other ideas." | | 4 | "The tabby came off the" | | 5 | "Traitor, she thought, and heard" | | 6 | "Lucien looked down at the" | | 7 | "Something in his face loosened" | | 8 | "She worked the three deadbolts," | | 9 | "She’d forgotten how much space" | | 10 | "Rain beaded on the shoulders" | | 11 | "The suit was charcoal, tailored," | | 12 | "His cane was hooked over" | | 13 | "The flat smelled of cumin" | | 14 | "Scrolls furled with rubber bands," | | 15 | "She found the kit under" | | 16 | "He flexed and unflexed the" | | 17 | "Rory sat down on the" | | 18 | "She didn’t cross the room." | | 19 | "That was on purpose." |
| | ratio | 0.804 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 60.44% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 3 | | matches | | 0 | "Rain beaded on the shoulders of the suit and in his slicked-back platinum hair, which had come loose at the temples in a way that suggested he’d been walking a …" | | 1 | "The suit was charcoal, tailored, and it had seen better evenings: a dark bloom on the left cuff that wasn’t rain, and the sleeve above it shoved back and refast…" | | 2 | "His cane was hooked over his forearm — the ivory handle catching the hallway bulb, the thing that was not entirely a cane." |
| |
| 95.59% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 17 | | uselessAdditionCount | 1 | | matches | | 0 | "Lucien’s voice came, as if the intervening months had been a corridor he’d simply walked down at his own pace" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.297 | | leniency | 0.595 | | rawRatio | 0 | | effectiveRatio | 0 | |