| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 43 | | tagDensity | 0.233 | | leniency | 0.465 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 88.66% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1763 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "slowly" | | 1 | "softly" | | 2 | "very" |
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| 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) | |
| 82.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1763 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "glint" | | 1 | "echoed" | | 2 | "throbbed" | | 3 | "pulsed" | | 4 | "flicked" | | 5 | "silence" |
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| 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 | 198 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 198 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 231 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1763 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 54 | | wordCount | 1522 | | uniqueNames | 29 | | maxNameDensity | 0.66 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 3 | | Town | 2 | | World | 1 | | End | 1 | | Portuguese | 1 | | Met | 1 | | Inverness | 1 | | Street | 1 | | Silas | 2 | | Kentish | 1 | | Road | 1 | | Herrera | 9 | | Quinn | 10 | | Leslie | 1 | | Green | 1 | | November | 1 | | Armed | 1 | | Response | 1 | | Morris | 3 | | Deptford | 1 | | Underground | 1 | | Veil | 1 | | Market | 1 | | Raven | 1 | | Nest | 1 | | London | 1 | | Saint | 1 | | Christopher | 1 | | Old | 3 |
| | persons | | 0 | "Silas" | | 1 | "Herrera" | | 2 | "Quinn" | | 3 | "Leslie" | | 4 | "Green" | | 5 | "Morris" | | 6 | "Underground" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | 0 | "Camden" | | 1 | "Town" | | 2 | "World" | | 3 | "Portuguese" | | 4 | "Inverness" | | 5 | "Street" | | 6 | "Kentish" | | 7 | "Road" | | 8 | "Deptford" | | 9 | "Veil" | | 10 | "Raven" | | 11 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 81.82% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 110 | | glossingSentenceCount | 3 | | matches | | 0 | "spiral that seemed to turn if you looked at it too long" | | 1 | "felt like damp paper" | | 2 | "quite match the real one" |
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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 | 1763 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 231 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 95 | | mean | 18.56 | | std | 19.42 | | cv | 1.046 | | sampleLengths | | 0 | 18 | | 1 | 42 | | 2 | 34 | | 3 | 2 | | 4 | 13 | | 5 | 72 | | 6 | 3 | | 7 | 22 | | 8 | 8 | | 9 | 50 | | 10 | 34 | | 11 | 3 | | 12 | 44 | | 13 | 14 | | 14 | 2 | | 15 | 24 | | 16 | 7 | | 17 | 20 | | 18 | 2 | | 19 | 3 | | 20 | 37 | | 21 | 4 | | 22 | 1 | | 23 | 2 | | 24 | 32 | | 25 | 9 | | 26 | 26 | | 27 | 11 | | 28 | 8 | | 29 | 34 | | 30 | 10 | | 31 | 14 | | 32 | 18 | | 33 | 57 | | 34 | 1 | | 35 | 35 | | 36 | 9 | | 37 | 71 | | 38 | 15 | | 39 | 3 | | 40 | 8 | | 41 | 32 | | 42 | 11 | | 43 | 5 | | 44 | 3 | | 45 | 17 | | 46 | 67 | | 47 | 8 | | 48 | 48 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 198 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 232 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 231 | | ratio | 0 | | matches | (empty) | |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1532 | | adjectiveStacks | 2 | | stackExamples | | 0 | "small, ancient, round wicker" | | 1 | "Same hand-drawn ink." |
| | adverbCount | 44 | | adverbRatio | 0.028720626631853787 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0026109660574412533 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 231 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 231 | | mean | 7.63 | | std | 6.1 | | cv | 0.8 | | sampleLengths | | 0 | 18 | | 1 | 7 | | 2 | 12 | | 3 | 3 | | 4 | 14 | | 5 | 6 | | 6 | 17 | | 7 | 4 | | 8 | 13 | | 9 | 2 | | 10 | 2 | | 11 | 1 | | 12 | 10 | | 13 | 13 | | 14 | 14 | | 15 | 10 | | 16 | 1 | | 17 | 6 | | 18 | 28 | | 19 | 1 | | 20 | 2 | | 21 | 5 | | 22 | 10 | | 23 | 4 | | 24 | 3 | | 25 | 8 | | 26 | 2 | | 27 | 7 | | 28 | 16 | | 29 | 7 | | 30 | 2 | | 31 | 5 | | 32 | 11 | | 33 | 6 | | 34 | 5 | | 35 | 23 | | 36 | 3 | | 37 | 2 | | 38 | 1 | | 39 | 18 | | 40 | 2 | | 41 | 7 | | 42 | 3 | | 43 | 11 | | 44 | 5 | | 45 | 9 | | 46 | 2 | | 47 | 9 | | 48 | 9 | | 49 | 6 |
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| 69.12% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.4588744588744589 | | totalSentences | 231 | | uniqueOpeners | 106 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 10 | | totalSentences | 157 | | matches | | 0 | "Just a man who had" | | 1 | "Then he ducked left onto" | | 2 | "Then Herrera stopped." | | 3 | "Dead still, in front of" | | 4 | "Then at her." | | 5 | "Then at the steel shutter" | | 6 | "Somewhere beneath her feet, something" | | 7 | "Too many voices at once." | | 8 | "Softly this time." | | 9 | "Just the empty archway and" |
| | ratio | 0.064 | |
| 84.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 53 | | totalSentences | 157 | | matches | | 0 | "Her knee cracked against the" | | 1 | "She kept moving." | | 2 | "It always waited until you" | | 3 | "Her boots slapped through puddles" | | 4 | "She'd left her radio in" | | 5 | "Their eyes met across the" | | 6 | "He didn't look frightened." | | 7 | "He looked apologetic." | | 8 | "Her lungs had turned to" | | 9 | "She clocked the details out" | | 10 | "He cut beneath the railway" | | 11 | "She slowed to a walk." | | 12 | "Her hand drifted to the" | | 13 | "He pushed the hair out" | | 14 | "His accent softened the hard" | | 15 | "He smiled, and it didn't" | | 16 | "He tilted his head, as" | | 17 | "He dropped flat and rolled" | | 18 | "Her fingers closed on wet" | | 19 | "She crouched there, breathing hard," |
| | ratio | 0.338 | |
| 96.94% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 114 | | totalSentences | 157 | | matches | | 0 | "Herrera vaulted the bollard outside" | | 1 | "Quinn took it at a" | | 2 | "Her knee cracked against the" | | 3 | "She kept moving." | | 4 | "It always waited until you" | | 5 | "Someone swore in Portuguese." | | 6 | "A pint glass hit the" | | 7 | "Camden never hurried for anyone," | | 8 | "Water sheeted off the awnings" | | 9 | "Her boots slapped through puddles" | | 10 | "She'd left her radio in" | | 11 | "Herrera glanced over his shoulder." | | 12 | "Their eyes met across the" | | 13 | "He didn't look frightened." | | 14 | "He looked apologetic." | | 15 | "Her lungs had turned to" | | 16 | "She clocked the details out" | | 17 | "A silver glint at his" | | 18 | "He cut beneath the railway" | | 19 | "Pigeons exploded from the girders." |
| | ratio | 0.726 | |
| 31.85% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 157 | | matches | | 0 | "Now she needed the man" |
| | ratio | 0.006 | |
| 51.95% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 7 | | matches | | 0 | "Herrera vaulted the bollard outside Camden Town station like a man who'd done it a hundred times before." | | 1 | "Just a man who had walked out of the back door of Silas's bar at half eleven carrying a medical bag that dripped something black onto the cobbles." | | 2 | "A night bus hissed past, its windows full of blank, sleepy faces that turned to watch the madwoman in the soaked wool coat." | | 3 | "He tilted his head, as if the question genuinely puzzled him." | | 4 | "Beyond it, stone steps dropped into a darkness that smelled of wet brick, clove smoke and something sweet underneath." | | 5 | "A woman with moth wings folded down her back sold jars of something that glowed and pulsed like a heartbeat." | | 6 | "Beside him, a small, ancient, round wicker cage held a bird with a human mouth that whispered numbers to anyone who leaned close." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 43 | | tagDensity | 0.116 | | leniency | 0.233 | | rawRatio | 0.2 | | effectiveRatio | 0.047 | |