| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1550 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 38.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1550 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "measured" | | 1 | "pulse" | | 2 | "fluttered" | | 3 | "perfect" | | 4 | "tracing" | | 5 | "weight" | | 6 | "standard" | | 7 | "resonance" | | 8 | "familiar" | | 9 | "could feel" | | 10 | "etched" | | 11 | "quivered" | | 12 | "etching" | | 13 | "navigate" | | 14 | "echoing" |
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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 | 150 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 150 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 150 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1550 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 2 | | matches | | 0 | "The thermal gradient is wrong for a chemical pool, she said, her voice low and even." | | 1 | "Crafted by a Shade artisan, Eva murmured, leaning in." |
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| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 1550 | | uniqueNames | 18 | | maxNameDensity | 0.71 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 2 | | Harlow | 11 | | Quinn | 2 | | Tube | 1 | | Sergeant | 1 | | Aris | 3 | | Thorne | 6 | | Kowalski | 2 | | Harlows | 1 | | Ms | 1 | | British | 1 | | Museum | 1 | | Morris | 2 | | Shade | 1 | | Eva | 6 | | Veil | 1 | | Compass | 1 | | You | 4 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Sergeant" | | 4 | "Thorne" | | 5 | "Kowalski" | | 6 | "Ms" | | 7 | "Museum" | | 8 | "Morris" | | 9 | "Eva" | | 10 | "Compass" | | 11 | "You" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 100 | | 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 | 1550 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 150 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 59.62 | | std | 33.84 | | cv | 0.568 | | sampleLengths | | 0 | 92 | | 1 | 29 | | 2 | 87 | | 3 | 33 | | 4 | 66 | | 5 | 83 | | 6 | 43 | | 7 | 39 | | 8 | 19 | | 9 | 118 | | 10 | 74 | | 11 | 21 | | 12 | 39 | | 13 | 92 | | 14 | 50 | | 15 | 106 | | 16 | 49 | | 17 | 95 | | 18 | 18 | | 19 | 111 | | 20 | 19 | | 21 | 22 | | 22 | 28 | | 23 | 108 | | 24 | 89 | | 25 | 20 |
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| 86.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 8 | | totalSentences | 150 | | matches | | 0 | "been sealed" | | 1 | "were flexed" | | 2 | "been restructured" | | 3 | "were dismissed" | | 4 | "was buried" | | 5 | "was etched" | | 6 | "are keyed" | | 7 | "were misaligned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 268 | | matches | | 0 | "were measuring" | | 1 | "were shipping" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 150 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1553 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.018673535093367676 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.00643915003219575 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 150 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 150 | | mean | 10.33 | | std | 7.42 | | cv | 0.718 | | sampleLengths | | 0 | 17 | | 1 | 22 | | 2 | 28 | | 3 | 25 | | 4 | 23 | | 5 | 4 | | 6 | 1 | | 7 | 1 | | 8 | 24 | | 9 | 11 | | 10 | 7 | | 11 | 25 | | 12 | 20 | | 13 | 13 | | 14 | 20 | | 15 | 5 | | 16 | 9 | | 17 | 6 | | 18 | 18 | | 19 | 12 | | 20 | 16 | | 21 | 5 | | 22 | 13 | | 23 | 16 | | 24 | 20 | | 25 | 4 | | 26 | 17 | | 27 | 3 | | 28 | 5 | | 29 | 7 | | 30 | 7 | | 31 | 29 | | 32 | 4 | | 33 | 6 | | 34 | 11 | | 35 | 11 | | 36 | 7 | | 37 | 2 | | 38 | 4 | | 39 | 7 | | 40 | 6 | | 41 | 13 | | 42 | 20 | | 43 | 27 | | 44 | 11 | | 45 | 15 | | 46 | 10 | | 47 | 22 | | 48 | 14 | | 49 | 13 |
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| 38.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.26666666666666666 | | totalSentences | 150 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 139 | | matches | (empty) | | ratio | 0 | |
| 47.34% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 60 | | totalSentences | 139 | | matches | | 0 | "It moved like breath." | | 1 | "Her target sat upright against" | | 2 | "He wore a charcoal overcoat," | | 3 | "He looked up as Harlow" | | 4 | "We pulled the body at" | | 5 | "We will know more after" | | 6 | "She knelt, ignoring the damp" | | 7 | "It deflected gravity." | | 8 | "She looked at the mans" | | 9 | "They were flexed into a" | | 10 | "I know what you are" | | 11 | "You are thinking it does" | | 12 | "We will send samples to" | | 13 | "Her sharp jaw tightened." | | 14 | "They will not give you" | | 15 | "Her curly red hair escaped" | | 16 | "Her freckled nose wrinkled as" | | 17 | "She tucked a stray curl" | | 18 | "She opened it to a" | | 19 | "You are talking about a" |
| | ratio | 0.432 | |
| 42.73% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 116 | | totalSentences | 139 | | matches | | 0 | "The service stairwell behind the" | | 1 | "Detective Harlow Quinn descended with" | | 2 | "The worn leather watch on" | | 3 | "The abandoned Tube station had" | | 4 | "It moved like breath." | | 5 | "Harlow ducked beneath the strip" | | 6 | "Her target sat upright against" | | 7 | "He wore a charcoal overcoat," | | 8 | "Detective Sergeant Aris Thorne stood" | | 9 | "He looked up as Harlow" | | 10 | "Thorne gestured with his pen." | | 11 | "We pulled the body at" | | 12 | "The platform shows signs of" | | 13 | "Cause of death looks like" | | 14 | "We will know more after" | | 15 | "Harlow did not answer immediately." | | 16 | "She knelt, ignoring the damp" | | 17 | "The scorch marks formed a" | | 18 | "The tiles inside the circle" | | 19 | "The dark fluid pooling beneath" |
| | ratio | 0.835 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 139 | | matches | (empty) | | ratio | 0 | |
| 43.96% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 65 | | technicalSentenceCount | 9 | | matches | | 0 | "At five feet four, she barely cleared Harlows shoulder, but her presence carried the quiet weight of someone who spent her days reading languages that predated …" | | 1 | "She could feel the faint hum through the plastic, a vibration that matched the low frequency she had felt in her teeth since stepping onto the platform." | | 2 | "Its face was etched with protective sigils, interlocking curves and intersecting lines that looked less like decoration and more like structural reinforcement." | | 3 | "It pointed straight down into the track bed, trembling in time with a draft that moved against the natural airflow of the tunnels." | | 4 | "She turned the compass in her fingers, watching the needle hold its bearing despite the slight tilt of her wrist." | | 5 | "There was a transit corridor that opened without warning, dragged atmospheric mass inward, and left behind a man whose body could not survive the pressure drop." | | 6 | "The clique she had been tracking for months,the buyers, the brokers, the quiet men in tailored coats who moved crates marked as antiquities and walked away with…" | | 7 | "It lived in the inward drag of fluid that forgot gravity." | | 8 | "Harlow Quinn had spent eighteen years chasing men who left footprints." |
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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 | |