| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 27 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 113 | | tagDensity | 0.239 | | leniency | 0.478 | | rawRatio | 0.037 | | effectiveRatio | 0.018 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2280 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 89.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2280 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "methodical" | | 1 | "perfect" | | 2 | "traced" | | 3 | "measured" | | 4 | "jaw clenched" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 117 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 117 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 203 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 58 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2276 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 27 | | unquotedAttributions | 0 | | matches | (empty) | |
| 0.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 88 | | wordCount | 1192 | | uniqueNames | 15 | | maxNameDensity | 2.85 | | worstName | "Eva" | | maxWindowNameDensity | 6.5 | | worstWindowName | "Eva" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Aurora | 30 | | Silas | 10 | | Golden | 2 | | Empress | 2 | | Bloomsbury | 1 | | Rory | 1 | | Eva | 34 | | Posy | 1 | | Simmonds | 1 | | Alan | 1 | | Moore | 1 | | British | 1 | | Partner | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Aurora" | | 3 | "Silas" | | 4 | "Empress" | | 5 | "Rory" | | 6 | "Eva" | | 7 | "Alan" | | 8 | "Moore" |
| | places | | | globalScore | 0.074 | | windowScore | 0 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 1 | | matches | | 0 | "quite match the woman who used to laugh so hard she'd snort" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2276 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 203 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 110 | | mean | 20.69 | | std | 20.71 | | cv | 1.001 | | sampleLengths | | 0 | 76 | | 1 | 36 | | 2 | 3 | | 3 | 12 | | 4 | 6 | | 5 | 59 | | 6 | 69 | | 7 | 8 | | 8 | 31 | | 9 | 1 | | 10 | 18 | | 11 | 36 | | 12 | 64 | | 13 | 21 | | 14 | 65 | | 15 | 14 | | 16 | 13 | | 17 | 1 | | 18 | 27 | | 19 | 83 | | 20 | 5 | | 21 | 20 | | 22 | 3 | | 23 | 6 | | 24 | 5 | | 25 | 23 | | 26 | 2 | | 27 | 4 | | 28 | 1 | | 29 | 4 | | 30 | 5 | | 31 | 31 | | 32 | 11 | | 33 | 14 | | 34 | 2 | | 35 | 7 | | 36 | 31 | | 37 | 7 | | 38 | 16 | | 39 | 28 | | 40 | 2 | | 41 | 46 | | 42 | 22 | | 43 | 6 | | 44 | 44 | | 45 | 46 | | 46 | 27 | | 47 | 4 | | 48 | 3 | | 49 | 43 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 117 | | matches | | |
| 77.11% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 217 | | matches | | 0 | "was polishing" | | 1 | "were going" | | 2 | "was coming" | | 3 | "was letting" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 203 | | ratio | 0.01 | | matches | | 0 | "Her eyes—grey, wide, unmistakable even after all the years—locked onto Aurora's face." | | 1 | "On the back, in handwriting she recognised even now—small, precise, with a habit of crossing the wrong letters—Eva had written a mobile number." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1198 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 40 | | adverbRatio | 0.0333889816360601 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.001669449081803005 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 203 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 203 | | mean | 11.21 | | std | 10.63 | | cv | 0.949 | | sampleLengths | | 0 | 23 | | 1 | 12 | | 2 | 41 | | 3 | 14 | | 4 | 18 | | 5 | 2 | | 6 | 2 | | 7 | 3 | | 8 | 10 | | 9 | 2 | | 10 | 6 | | 11 | 30 | | 12 | 2 | | 13 | 13 | | 14 | 9 | | 15 | 5 | | 16 | 4 | | 17 | 21 | | 18 | 17 | | 19 | 27 | | 20 | 6 | | 21 | 2 | | 22 | 19 | | 23 | 12 | | 24 | 1 | | 25 | 3 | | 26 | 14 | | 27 | 1 | | 28 | 20 | | 29 | 16 | | 30 | 10 | | 31 | 18 | | 32 | 4 | | 33 | 32 | | 34 | 5 | | 35 | 16 | | 36 | 22 | | 37 | 37 | | 38 | 6 | | 39 | 4 | | 40 | 10 | | 41 | 12 | | 42 | 1 | | 43 | 1 | | 44 | 21 | | 45 | 6 | | 46 | 43 | | 47 | 12 | | 48 | 4 | | 49 | 10 |
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| 43.60% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.29064039408866993 | | totalSentences | 203 | | uniqueOpeners | 59 | |
| 68.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 97 | | matches | | 0 | "Somewhere in the corner, the" | | 1 | "Then she turned and walked" |
| | ratio | 0.021 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 97 | | matches | | 0 | "She wiped her boots on" | | 1 | "She could still smell the" | | 2 | "She tucked the delivery bag" | | 3 | "She deleted them without listening." | | 4 | "She moved through the room" | | 5 | "Her eyes—grey, wide, unmistakable even" | | 6 | "She set the phone down" | | 7 | "She flagged down Silas" | | 8 | "He poured without comment and" | | 9 | "He'd always been good at" | | 10 | "He poured and set it" | | 11 | "They sat with that for" | | 12 | "She spread her hands" | | 13 | "She was twenty-six." | | 14 | "She was twenty-six and she" | | 15 | "She watched the door until" | | 16 | "He'd been in intelligence long" | | 17 | "He picked up the empty" | | 18 | "He limped back toward the" |
| | ratio | 0.196 | |
| 1.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 97 | | matches | | 0 | "The green neon glow of" | | 1 | "Rain had been threatening all" | | 2 | "She wiped her boots on" | | 3 | "She could still smell the" | | 4 | "The last delivery had been" | | 5 | "Silas set the glass down" | | 6 | "Aurora slipped onto her usual" | | 7 | "She tucked the delivery bag" | | 8 | "She deleted them without listening." | | 9 | "The door swung open." | | 10 | "A woman walked in, shaking" | | 11 | "She moved through the room" | | 12 | "Aurora looked up from her" | | 13 | "The woman stopped two feet" | | 14 | "Her eyes—grey, wide, unmistakable even" | | 15 | "Aurora's throat tightened." | | 16 | "She set the phone down" | | 17 | "Eva laughed, a short, sharp" | | 18 | "Aurora's left hand went to" | | 19 | "The crescent scar sat there," |
| | ratio | 0.918 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 97 | | matches | (empty) | | ratio | 0 | |
| 47.62% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 45 | | technicalSentenceCount | 6 | | matches | | 0 | "She wiped her boots on the mat and nodded at Silas behind the bar, who was polishing a glass with the methodical patience of a man who had all the time in the w…" | | 1 | "The last delivery had been to a bloke in Bloomsbury who'd complained about the prawn crackers being stale." | | 2 | "Dark hair cropped close to the skull in a way that highlighted cheekbones sharp enough to cut." | | 3 | "She moved through the room with the kind of confidence that came from knowing exactly where you were going and not caring who watched you get there." | | 4 | "Eva looked at her for one more moment, grey eyes searching blue ones, searching for something that might have been there eight years ago or might have never exi…" | | 5 | "He'd been in intelligence long enough to recognise the shape of a conversation that mattered, even if he hadn't heard the words." |
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| 69.44% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 27 | | uselessAdditionCount | 3 | | matches | | 0 | "Eva pulled out, not waiting for an invitation" | | 1 | "Eva held up, the gesture too polished, too measured" | | 2 | "He limped back, his silver ring catching the low light" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 113 | | tagDensity | 0.044 | | leniency | 0.088 | | rawRatio | 0 | | effectiveRatio | 0 | |