| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 16 | | tagDensity | 0.688 | | leniency | 1 | | rawRatio | 0.091 | | effectiveRatio | 0.091 | |
| 97.37% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1899 | | 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) | |
| 71.04% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1899 | | totalAiIsms | 11 | | found | | 0 | | | 1 | | | 2 | | word | "the last thing" | | count | 1 |
| | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | |
| | highlights | | 0 | "treacherous" | | 1 | "streaming" | | 2 | "the last thing" | | 3 | "flickered" | | 4 | "etched" | | 5 | "scanning" | | 6 | "rhythmic" | | 7 | "echoed" | | 8 | "structure" | | 9 | "footsteps" | | 10 | "flicked" |
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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 | 140 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 140 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 145 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1895 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1788 | | uniqueNames | 24 | | maxNameDensity | 0.84 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 15 | | Raven | 2 | | Nest | 2 | | Tomás | 2 | | Herrera | 9 | | Saint | 3 | | Christopher | 3 | | Morris | 4 | | Old | 1 | | Compton | 1 | | Street | 3 | | Oxford | 1 | | Camden | 3 | | Limehouse | 1 | | High | 1 | | Veil | 3 | | Market | 3 | | Tube | 1 | | Victorian | 1 | | English | 1 | | Seville | 1 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Nest" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Old" | | 3 | "Compton" | | 4 | "Street" | | 5 | "Oxford" | | 6 | "Camden" | | 7 | "Limehouse" | | 8 | "High" | | 9 | "Veil" | | 10 | "Victorian" | | 11 | "Seville" | | 12 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 38.39% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 112 | | glossingSentenceCount | 5 | | matches | | 0 | "felt like a countdown" | | 1 | "sounded like wings" | | 2 | "looked like living shadow offered her a b" | | 3 | "something like pity in his expression" | | 4 | "felt like forward momentum instead of m" |
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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 | 1895 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 145 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 57.42 | | std | 36.75 | | cv | 0.64 | | sampleLengths | | 0 | 134 | | 1 | 76 | | 2 | 10 | | 3 | 110 | | 4 | 110 | | 5 | 107 | | 6 | 93 | | 7 | 75 | | 8 | 103 | | 9 | 51 | | 10 | 7 | | 11 | 124 | | 12 | 84 | | 13 | 74 | | 14 | 76 | | 15 | 22 | | 16 | 60 | | 17 | 65 | | 18 | 75 | | 19 | 60 | | 20 | 36 | | 21 | 49 | | 22 | 25 | | 23 | 30 | | 24 | 41 | | 25 | 59 | | 26 | 10 | | 27 | 12 | | 28 | 7 | | 29 | 31 | | 30 | 19 | | 31 | 56 | | 32 | 4 |
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| 92.73% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 140 | | matches | | 0 | "was plastered" | | 1 | "been marked" | | 2 | "been widened" | | 3 | "been converted" | | 4 | "been torn" | | 5 | "were stacked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 316 | | matches | | 0 | "was running" | | 1 | "were mocking" | | 2 | "was deciding" |
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| 24.63% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 2 | | flaggedSentences | 6 | | totalSentences | 145 | | ratio | 0.041 | | matches | | 0 | "At the bottom, another door—this one heavier, iron-bound, etched with symbols that hurt to look at directly." | | 1 | "She was clearly a cop; the rain still dripped from her coat in steady beats, and the baton was still in her hand." | | 2 | "The platform edge had been converted into a thoroughfare; beyond it, the rails had been torn up to make room for more stalls." | | 3 | "Strange music drifted from somewhere ahead—strings and a low, rhythmic chanting that made her teeth ache." | | 4 | "Further ahead, she heard footsteps—fast, retreating." | | 5 | "A shadow passed there—something tall, jointed wrong." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1800 | | adjectiveStacks | 1 | | stackExamples | | 0 | "neat, blood-red script" |
| | adverbCount | 68 | | adverbRatio | 0.03777777777777778 | | lyAdverbCount | 21 | | lyAdverbRatio | 0.011666666666666667 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 145 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 145 | | mean | 13.07 | | std | 8.23 | | cv | 0.63 | | sampleLengths | | 0 | 22 | | 1 | 23 | | 2 | 19 | | 3 | 29 | | 4 | 24 | | 5 | 17 | | 6 | 26 | | 7 | 2 | | 8 | 19 | | 9 | 11 | | 10 | 8 | | 11 | 10 | | 12 | 8 | | 13 | 2 | | 14 | 2 | | 15 | 15 | | 16 | 10 | | 17 | 14 | | 18 | 12 | | 19 | 16 | | 20 | 21 | | 21 | 15 | | 22 | 5 | | 23 | 7 | | 24 | 22 | | 25 | 14 | | 26 | 11 | | 27 | 9 | | 28 | 7 | | 29 | 15 | | 30 | 16 | | 31 | 9 | | 32 | 4 | | 33 | 24 | | 34 | 10 | | 35 | 10 | | 36 | 18 | | 37 | 15 | | 38 | 8 | | 39 | 18 | | 40 | 7 | | 41 | 2 | | 42 | 26 | | 43 | 4 | | 44 | 6 | | 45 | 3 | | 46 | 17 | | 47 | 7 | | 48 | 3 | | 49 | 18 |
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| 55.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.35172413793103446 | | totalSentences | 145 | | uniqueOpeners | 51 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 131 | | matches | | 0 | "Then Tomás Herrera had slipped" | | 1 | "Just like it had followed" | | 2 | "Then he ducked left into" | | 3 | "Of course it had." | | 4 | "Then he vaulted a low" | | 5 | "Further ahead, she heard footsteps—fast," |
| | ratio | 0.046 | |
| 76.49% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 47 | | totalSentences | 131 | | matches | | 0 | "Her closely cropped salt-and-pepper hair" | | 1 | "She had recognized him instantly:" | | 2 | "She suspected he was running" | | 3 | "she barked, voice raw against" | | 4 | "He bolted east along Old" | | 5 | "She kept her eyes fixed" | | 6 | "She had the file memorized." | | 7 | "They burst out of Soho" | | 8 | "Her breath sawed in and" | | 9 | "He’d gone alone into an" | | 10 | "She wasn’t about to let" | | 11 | "He ran like a man" | | 12 | "Their eyes met across twenty" | | 13 | "His warm brown gaze held" | | 14 | "She drew her baton, not" | | 15 | "It moved every full moon," | | 16 | "You needed a bone token" | | 17 | "She had none." | | 18 | "She took them two at" | | 19 | "Her shadow jerked ahead of" |
| | ratio | 0.359 | |
| 59.24% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 105 | | totalSentences | 131 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "Her closely cropped salt-and-pepper hair" | | 3 | "The worn leather watch on" | | 4 | "She had recognized him instantly:" | | 5 | "She suspected he was running" | | 6 | "she barked, voice raw against" | | 7 | "He bolted east along Old" | | 8 | "Quinn gave chase, lungs already" | | 9 | "The streets were still busy" | | 10 | "A taxi horn blared as" | | 11 | "Quinn followed, boots skidding on" | | 12 | "She kept her eyes fixed" | | 13 | "That scar had come from" | | 14 | "She had the file memorized." | | 15 | "The chase carved a jagged" | | 16 | "They burst out of Soho" | | 17 | "Her breath sawed in and" | | 18 | "Every splash of her footfalls" | | 19 | "Morris had chased something just" |
| | ratio | 0.802 | |
| 76.34% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 131 | | matches | | 0 | "Because she did know." | | 1 | "To seal the entrance and" |
| | ratio | 0.015 | |
| 50.09% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 77 | | technicalSentenceCount | 10 | | matches | | 0 | "Three hours tailing shadows outside The Raven’s Nest, watching that distinctive green neon buzz above the entrance like a warning no one ever heeded." | | 1 | "She had recognized him instantly: the short curly dark brown hair, the olive skin, the Saint Christopher medallion that bounced against his collarbone when he r…" | | 2 | "He bolted east along Old Compton Street, trainers slapping through puddles that exploded like gunfire." | | 3 | "The rational part of her brain, the part that had earned her commendations and a reputation for icy calm, screamed at her to call for backup." | | 4 | "One stall offered glowing vials of banned alchemical substances that swirled like captive galaxies." | | 5 | "Strange music drifted from somewhere ahead—strings and a low, rhythmic chanting that made her teeth ache." | | 6 | "She caught a flash of movement thirty metres ahead: Herrera slipping between a stall selling maps that redrew themselves and another peddling fresh nightmares i…" | | 7 | "He moved with the easy confidence of a man on home ground, nodding to a vendor who tossed him a small leather pouch without breaking stride." | | 8 | "Then he vaulted a low table laden with enchanted rings and disappeared behind a bookshelf that stood in the middle of the tunnel for no architectural reason Qui…" | | 9 | "Herrera exhaled, almost regretful, and the medallion at his throat caught the light one last time as he turned toward a second, narrower passage that hadn’t bee…" |
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| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "she barked, voice raw against the rain" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 3 | | fancyTags | | 0 | "she barked (bark)" | | 1 | "she whispered (whisper)" | | 2 | "she shouted (shout)" |
| | dialogueSentences | 16 | | tagDensity | 0.563 | | leniency | 1 | | rawRatio | 0.333 | | effectiveRatio | 0.333 | |