| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 89.53% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 955 | | totalAiIsmAdverbs | 2 | | 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) | |
| 37.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 955 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "pulse" | | 1 | "chill" | | 2 | "glinting" | | 3 | "flickered" | | 4 | "scanning" | | 5 | "weight" | | 6 | "perfect" | | 7 | "stomach" | | 8 | "echoed" | | 9 | "shimmered" |
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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 | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 105 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 941 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.81% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 893 | | uniqueNames | 17 | | maxNameDensity | 1.34 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 4 | | Harlow | 1 | | Quinn | 12 | | Herrera | 1 | | Raven | 2 | | Nest | 2 | | Tomás | 7 | | Greek | 1 | | Street | 1 | | Chinese | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 | | London | 1 | | Saint | 1 | | Christopher | 1 | | Morris | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Raven" | | 4 | "Tomás" | | 5 | "Market" | | 6 | "Saint" | | 7 | "Christopher" | | 8 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Greek" | | 2 | "Street" | | 3 | "London" |
| | globalScore | 0.828 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 0.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 2.125 | | wordCount | 941 | | matches | | 0 | "not brick, but something colder, denser" | | 1 | "not at the man, but at the bookshelf" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 27.68 | | std | 23.97 | | cv | 0.866 | | sampleLengths | | 0 | 71 | | 1 | 83 | | 2 | 51 | | 3 | 70 | | 4 | 19 | | 5 | 37 | | 6 | 55 | | 7 | 3 | | 8 | 47 | | 9 | 6 | | 10 | 24 | | 11 | 42 | | 12 | 4 | | 13 | 4 | | 14 | 69 | | 15 | 3 | | 16 | 33 | | 17 | 63 | | 18 | 11 | | 19 | 53 | | 20 | 7 | | 21 | 34 | | 22 | 15 | | 23 | 2 | | 24 | 29 | | 25 | 9 | | 26 | 9 | | 27 | 15 | | 28 | 18 | | 29 | 9 | | 30 | 29 | | 31 | 6 | | 32 | 7 | | 33 | 4 |
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| 91.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 105 | | matches | | 0 | "been spotted" | | 1 | "being hunted" | | 2 | "was gone" | | 3 | "were embedded" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 162 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 14 | | semicolonCount | 1 | | flaggedSentences | 15 | | totalSentences | 110 | | ratio | 0.136 | | matches | | 0 | "Her leather watch, worn at the wrist, pressed against her pulse as she moved—steady, unrelenting." | | 1 | "She didn’t need a compass; her instincts were sharper than any tool." | | 2 | "Not intentionally, not by choice—he’d been spotted outside the Raven’s Nest by a beat officer who recognised him from a missing persons file." | | 3 | "She’d seen the scar on his arm through his rolled sleeve—a souvenir of violence, maybe the same kind they were all tangled in." | | 4 | "A shadow darted past the entrance of the Raven’s Nest—green neon glinting off wet cobblestones." | | 5 | "Then—he was gone." | | 6 | "She stepped forward, heel grinding into something brittle—broken glass." | | 7 | "Quinn’s eye caught a faint discontinuity in the wall—a seam of shadow too deep, too perfect." | | 8 | "Her fingers met resistance—not brick, but something colder, denser." | | 9 | "The air changed—thicker, warm, saturated with the scent of spices and something older, metallic." | | 10 | "The walls were embedded with stalls of blackened wood, their surfaces glowing softly with enchanted lights—amber, emerald, the colour of old bruises." | | 11 | "She knew the legends—supernatural black market beneath London, moving every full moon to escape detection." | | 12 | "Then she saw him—Tomás, weaving through the crowd, his Saint Christopher medallion swinging against his chest as he moved urgently toward a bookshelf-lined alcove." | | 13 | "A figure stepped from the shadows—a man with a face like carved bone and eyes like polished obsidian." | | 14 | "She fired—not at the man, but at the bookshelf." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 910 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.02967032967032967 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.006593406593406593 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 8.55 | | std | 6.43 | | cv | 0.752 | | sampleLengths | | 0 | 25 | | 1 | 19 | | 2 | 15 | | 3 | 12 | | 4 | 6 | | 5 | 5 | | 6 | 23 | | 7 | 22 | | 8 | 4 | | 9 | 23 | | 10 | 16 | | 11 | 16 | | 12 | 19 | | 13 | 17 | | 14 | 16 | | 15 | 7 | | 16 | 30 | | 17 | 3 | | 18 | 2 | | 19 | 2 | | 20 | 12 | | 21 | 15 | | 22 | 6 | | 23 | 3 | | 24 | 5 | | 25 | 4 | | 26 | 4 | | 27 | 9 | | 28 | 9 | | 29 | 11 | | 30 | 16 | | 31 | 10 | | 32 | 3 | | 33 | 8 | | 34 | 11 | | 35 | 2 | | 36 | 2 | | 37 | 9 | | 38 | 15 | | 39 | 6 | | 40 | 13 | | 41 | 1 | | 42 | 3 | | 43 | 7 | | 44 | 16 | | 45 | 6 | | 46 | 3 | | 47 | 9 | | 48 | 2 | | 49 | 1 |
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| 44.55% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.32727272727272727 | | totalSentences | 110 | | uniqueOpeners | 36 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 92 | | matches | | 0 | "Then—he was gone." | | 1 | "Then she saw him—Tomás, weaving" | | 2 | "Then Tomás was through the" | | 3 | "Only the faintest scent of" | | 4 | "Even the lights dimmed." |
| | ratio | 0.054 | |
| 67.83% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 92 | | matches | | 0 | "Her leather watch, worn at" | | 1 | "She didn’t need a compass;" | | 2 | "She was sure of it." | | 3 | "She’d seen the scar on" | | 4 | "She reached the corner of" | | 5 | "He wove between pedestrians clutching" | | 6 | "Her coat, tailored for practicality," | | 7 | "She pulled her radio from" | | 8 | "Her boots hit the pavement" | | 9 | "She didn’t shout." | | 10 | "She didn’t need witnesses." | | 11 | "She needed the truth." | | 12 | "She stepped forward, heel grinding" | | 13 | "Her pulse thrummed in her" | | 14 | "She pressed her back against" | | 15 | "He didn’t vanish." | | 16 | "He moved through something she" | | 17 | "She reached out." | | 18 | "Her fingers met resistance—not brick," | | 19 | "She exhaled, then pushed." |
| | ratio | 0.38 | |
| 41.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 92 | | matches | | 0 | "The rain came down in" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "Her leather watch, worn at" | | 3 | "She didn’t need a compass;" | | 4 | "Tomás Herrera had led her" | | 5 | "She was sure of it." | | 6 | "Quinn didn’t blame him." | | 7 | "She’d seen the scar on" | | 8 | "She reached the corner of" | | 9 | "Tomás was faster than she" | | 10 | "He wove between pedestrians clutching" | | 11 | "Quinn ducked under the skeletal" | | 12 | "Her coat, tailored for practicality," | | 13 | "She pulled her radio from" | | 14 | "Static hissed back." | | 15 | "Night shift in Soho didn’t" | | 16 | "A shadow darted past the" | | 17 | "Her boots hit the pavement" | | 18 | "She didn’t shout." | | 19 | "A shout would draw eyes." |
| | ratio | 0.837 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 92 | | matches | (empty) | | ratio | 0 | |
| 67.67% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 4 | | matches | | 0 | "Detective Harlow Quinn kept her stride even, boots splashing through puddles that reflected the flickering signs like broken mirrors." | | 1 | "Not intentionally, not by choice—he’d been spotted outside the Raven’s Nest by a beat officer who recognised him from a missing persons file." | | 2 | "He wove between pedestrians clutching umbrellas, moving with the urgency of a man who knew he was being hunted." | | 3 | "A deep, resonant *crash* echoed through the hall as if the shelf connected to something vast and ancient." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |