| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 90.43% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1568 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "slightly" | | 1 | "slowly" | | 2 | "carefully" |
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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) | |
| 52.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1568 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "weight" | | 1 | "scanning" | | 2 | "pulse" | | 3 | "traced" | | 4 | "etched" | | 5 | "trembled" | | 6 | "magnetic" | | 7 | "silence" | | 8 | "vibrated" | | 9 | "echoed" | | 10 | "processed" | | 11 | "pulsed" |
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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 | 270 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 270 | | filterMatches | | 0 | "watch" | | 1 | "think" | | 2 | "notice" |
| | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 270 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 25 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1568 | | ratio | 0 | | matches | (empty) | |
| 0.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 22 | | unquotedAttributions | 22 | | matches | | 0 | "Met arrived twenty minutes ago, he muttered." | | 1 | "Cause of death, she asked, eyes scanning the tiled walls." | | 2 | "Unknown so far, Thorne said." | | 3 | "Look at the scuffs, she said." | | 4 | "Gas does not leave chalk circles on Victorian brick, she said." | | 5 | "What am I looking at, Thorne asked, stepping closer." | | 6 | "Something you have not catalogued, Quinn said." | | 7 | "Novelty item, Thorne said." | | 8 | "Novelty items do not override magnetic fields, Quinn replied." | | 9 | "Dr Lin is running the analysis, Thorne said." | | 10 | "You are focusing on the commute, Quinn said." | | 11 | "Three witnesses who walked past a crime scene without noticing a man sitting on a bench, bleeding from nowhere, Quinn co…" | | 12 | "It is bait, she said." | | 13 | "Bone tokens do not sit in hollows inside closed stations, Quinn said." | | 14 | "The Veil Market, Quinn whispered." | | 15 | "Underground network, Quinn said, voice steady." | | 16 | "You are speculating based on a toy compass and a carved tooth, Thorne said flatly." | | 17 | "He was not smuggled out, Quinn said." | | 18 | "Someone tried to sanitize the scene, Quinn said." | | 19 | "For the person who sold him something he could not return, Quinn said." | | 20 | "I plan to verify a hypothesis, she said." | | 21 | "Check the victim pockets again, she said." |
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| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 67 | | wordCount | 1568 | | uniqueNames | 15 | | maxNameDensity | 1.72 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 3 | | Quinn | 27 | | Camden | 1 | | Aris | 1 | | Thorne | 19 | | Victorian | 1 | | Lin | 1 | | Kings | 1 | | Cross | 1 | | London | 1 | | Kowalski | 1 | | Veil | 1 | | Market | 1 | | You | 5 | | Look | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Thorne" | | 3 | "Lin" | | 4 | "Kowalski" | | 5 | "You" | | 6 | "Look" |
| | places | | | globalScore | 0.639 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 128 | | 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 | 1568 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 270 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 56 | | mean | 28 | | std | 20.48 | | cv | 0.731 | | sampleLengths | | 0 | 123 | | 1 | 24 | | 2 | 20 | | 3 | 37 | | 4 | 10 | | 5 | 23 | | 6 | 39 | | 7 | 19 | | 8 | 11 | | 9 | 13 | | 10 | 33 | | 11 | 40 | | 12 | 46 | | 13 | 11 | | 14 | 31 | | 15 | 51 | | 16 | 9 | | 17 | 29 | | 18 | 12 | | 19 | 43 | | 20 | 45 | | 21 | 48 | | 22 | 69 | | 23 | 12 | | 24 | 31 | | 25 | 30 | | 26 | 5 | | 27 | 39 | | 28 | 4 | | 29 | 10 | | 30 | 8 | | 31 | 34 | | 32 | 22 | | 33 | 8 | | 34 | 41 | | 35 | 5 | | 36 | 3 | | 37 | 34 | | 38 | 19 | | 39 | 75 | | 40 | 14 | | 41 | 40 | | 42 | 23 | | 43 | 48 | | 44 | 15 | | 45 | 7 | | 46 | 49 | | 47 | 13 | | 48 | 27 | | 49 | 21 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 270 | | matches | | 0 | "was laid" | | 1 | "been dragged" | | 2 | "gets paved" | | 3 | "was extracted" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 316 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 270 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1568 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 45 | | adverbRatio | 0.028698979591836735 | | lyAdverbCount | 14 | | lyAdverbRatio | 0.008928571428571428 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 270 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 270 | | mean | 5.81 | | std | 3.74 | | cv | 0.643 | | sampleLengths | | 0 | 13 | | 1 | 18 | | 2 | 12 | | 3 | 10 | | 4 | 12 | | 5 | 8 | | 6 | 13 | | 7 | 3 | | 8 | 24 | | 9 | 10 | | 10 | 13 | | 11 | 8 | | 12 | 3 | | 13 | 7 | | 14 | 6 | | 15 | 4 | | 16 | 3 | | 17 | 4 | | 18 | 6 | | 19 | 10 | | 20 | 9 | | 21 | 4 | | 22 | 1 | | 23 | 3 | | 24 | 10 | | 25 | 5 | | 26 | 6 | | 27 | 6 | | 28 | 6 | | 29 | 4 | | 30 | 5 | | 31 | 12 | | 32 | 7 | | 33 | 2 | | 34 | 4 | | 35 | 5 | | 36 | 6 | | 37 | 8 | | 38 | 5 | | 39 | 4 | | 40 | 4 | | 41 | 3 | | 42 | 8 | | 43 | 5 | | 44 | 7 | | 45 | 6 | | 46 | 1 | | 47 | 2 | | 48 | 2 | | 49 | 5 |
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| 89.01% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.5518518518518518 | | totalSentences | 270 | | uniqueOpeners | 149 | |
| 74.74% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 223 | | matches | | 0 | "Maybe it is a gas" | | 1 | "Just worn grooves where metal" | | 2 | "Then shifted again, aligning with" | | 3 | "Then how did he get" | | 4 | "Then north again." |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 51 | | totalSentences | 223 | | matches | | 0 | "He looked exhausted." | | 1 | "You are early." | | 2 | "She walked straight to the" | | 3 | "Her leather watch ticked a" | | 4 | "She crouched, gloved fingers hovering" | | 5 | "Her gaze traced the floor." | | 6 | "He did not stumble back" | | 7 | "He was laid here first." | | 8 | "He fell backward into the" | | 9 | "She turned slowly, taking in" | | 10 | "I am covering bases, Harlow." | | 11 | "We need a working theory" | | 12 | "We book it as accidental" | | 13 | "She approached the body again." | | 14 | "Her gloves brushed a small" | | 15 | "She lifted it carefully." | | 16 | "She flipped the device." | | 17 | "She set the compass on" | | 18 | "She scraped a sample tray" | | 19 | "It lacked the copper bite" |
| | ratio | 0.229 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 142 | | totalSentences | 223 | | matches | | 0 | "Detective Harlow Quinn stepped off" | | 1 | "The abandoned Camden station reeked" | | 2 | "Blood pooled beneath him, dark" | | 3 | "DS Aris Thorne leaned against" | | 4 | "Rainwater dripped from the brim" | | 5 | "He looked exhausted." | | 6 | "Ambulance pronounced him dead on" | | 7 | "Forensics is setting up." | | 8 | "You are early." | | 9 | "Quinn did not answer." | | 10 | "She walked straight to the" | | 11 | "Her leather watch ticked a" | | 12 | "She crouched, gloved fingers hovering" | | 13 | "The wool felt cold." | | 14 | "Cause of death, she asked," | | 15 | "Paramedics found his pulse before" | | 16 | "Quinn shook her head." | | 17 | "Her gaze traced the floor." | | 18 | "Scuff marks ran parallel to" | | 19 | "The body faced away from" |
| | ratio | 0.637 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 223 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 2 | | matches | | 0 | "Male, late twenties, draped over a wooden bench that groaned under his weight." | | 1 | "Three witnesses who walked past a crime scene without noticing a man sitting on a bench, bleeding from nowhere, Quinn corrected." |
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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 | |