| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn stood slowly [slowly]" |
| | dialogueSentences | 38 | | tagDensity | 0.316 | | leniency | 0.632 | | rawRatio | 0.083 | | effectiveRatio | 0.053 | |
| 80.28% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1268 | | totalAiIsmAdverbs | 5 | | found | | 0 | | | 1 | | | 2 | | adverb | "reluctantly" | | count | 1 |
| | 3 | | | 4 | |
| | highlights | | 0 | "very" | | 1 | "slowly" | | 2 | "reluctantly" | | 3 | "carefully" | | 4 | "gently" |
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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) | |
| 88.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1268 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "methodical" | | 1 | "etched" | | 2 | "flickered" |
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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 | 58 | | matches | (empty) | |
| 44.33% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 58 | | filterMatches | | | hedgeMatches | | |
| 95.24% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 84 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 1 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 68 | | ratio | 0.012 | | matches | | 0 | "\"Nasty one. No visible wounds, no blood, nothing. Doc's guess is cardiac arrest. Rough sleeper wandered down here, ticker gave out.\"" |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1280 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 761 | | uniqueNames | 9 | | maxNameDensity | 1.18 | | worstName | "Ferris" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Ferris" | | discoveredNames | | Camden | 1 | | Harlow | 1 | | Quinn | 7 | | Birmingham | 1 | | Ferris | 9 | | Three | 3 | | Morris | 1 | | Deptford | 1 | | Like | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Ferris" | | 3 | "Morris" |
| | places | | | globalScore | 0.909 | | windowScore | 0.833 | |
| 18.42% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 38 | | glossingSentenceCount | 2 | | matches | | 0 | "paint that seemed to swallow the scene lamps rather than reflect them" | | 1 | "as if waiting for a train that would never come" |
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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 | 1280 | | matches | (empty) | |
| 87.30% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 84 | | matches | | 0 | "were that oxblood" | | 1 | "called that a" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 38 | | mean | 33.68 | | std | 31.32 | | cv | 0.93 | | sampleLengths | | 0 | 91 | | 1 | 8 | | 2 | 21 | | 3 | 3 | | 4 | 109 | | 5 | 18 | | 6 | 61 | | 7 | 51 | | 8 | 35 | | 9 | 5 | | 10 | 9 | | 11 | 50 | | 12 | 8 | | 13 | 54 | | 14 | 29 | | 15 | 56 | | 16 | 10 | | 17 | 8 | | 18 | 34 | | 19 | 1 | | 20 | 13 | | 21 | 45 | | 22 | 12 | | 23 | 7 | | 24 | 71 | | 25 | 42 | | 26 | 10 | | 27 | 40 | | 28 | 3 | | 29 | 5 | | 30 | 11 | | 31 | 85 | | 32 | 76 | | 33 | 16 | | 34 | 1 | | 35 | 120 | | 36 | 44 | | 37 | 18 |
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| 99.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 58 | | matches | | 0 | "been closed" | | 1 | "being remembered" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 132 | | matches | | 0 | "was listening" | | 1 | "was waiting" | | 2 | "wasn't pointing" | | 3 | "was pointing" | | 4 | "was feeling" |
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| 6.80% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 84 | | ratio | 0.048 | | matches | | 0 | "The station had been closed since before she was born — one of those wartime casualties the city had simply bricked over and forgotten, though forgotten places had a way of being remembered by the wrong people." | | 1 | "Male, late thirties, well dressed — good coat, good shoes, a wedding band." | | 2 | "Through the plastic, the needle swung — she watched it swing — and settled again, pointing the same direction." | | 3 | "Behind him, one of the scene lamps flickered, and Quinn could have sworn — she would never put it in a report, never say it aloud — that the darkness at the tunnel mouth breathed in." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 758 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.03430079155672823 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.009234828496042216 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 84 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 84 | | mean | 15.24 | | std | 14.13 | | cv | 0.927 | | sampleLengths | | 0 | 35 | | 1 | 4 | | 2 | 52 | | 3 | 4 | | 4 | 4 | | 5 | 21 | | 6 | 3 | | 7 | 11 | | 8 | 37 | | 9 | 29 | | 10 | 3 | | 11 | 8 | | 12 | 21 | | 13 | 18 | | 14 | 13 | | 15 | 2 | | 16 | 2 | | 17 | 32 | | 18 | 12 | | 19 | 19 | | 20 | 23 | | 21 | 9 | | 22 | 14 | | 23 | 21 | | 24 | 5 | | 25 | 9 | | 26 | 39 | | 27 | 11 | | 28 | 3 | | 29 | 5 | | 30 | 31 | | 31 | 23 | | 32 | 29 | | 33 | 15 | | 34 | 41 | | 35 | 7 | | 36 | 3 | | 37 | 8 | | 38 | 3 | | 39 | 21 | | 40 | 7 | | 41 | 3 | | 42 | 1 | | 43 | 13 | | 44 | 8 | | 45 | 37 | | 46 | 12 | | 47 | 7 | | 48 | 10 | | 49 | 13 |
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| 88.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5595238095238095 | | totalSentences | 84 | | uniqueOpeners | 47 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 17 | | totalSentences | 51 | | matches | | 0 | "She ducked the crime scene" | | 1 | "She pulled on gloves and" | | 2 | "His hands rested in his" | | 3 | "He was a solid man," | | 4 | "He straightened up, wincing at" | | 5 | "She crouched beside the body" | | 6 | "She lifted her gaze along" | | 7 | "He did, reluctantly." | | 8 | "She kept her voice level" | | 9 | "She turned back to the" | | 10 | "She lifted it carefully with" | | 11 | "It was pointing down the" | | 12 | "She dropped it in." | | 13 | "She turned the bag a" | | 14 | "She sealed the bag and" | | 15 | "It smelled like this." | | 16 | "She looked at the sigils" |
| | ratio | 0.333 | |
| 77.65% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 39 | | totalSentences | 51 | | matches | | 0 | "The stairs down to the" | | 1 | "Incense and old stone." | | 2 | "She ducked the crime scene" | | 3 | "She pulled on gloves and" | | 4 | "The station had been closed" | | 5 | "The tiles along the curved" | | 6 | "Quinn had seen enough gang" | | 7 | "These were careful, symmetrical, almost" | | 8 | "The body sat propped against" | | 9 | "His hands rested in his" | | 10 | "DS Ferris was waiting for" | | 11 | "He was a solid man," | | 12 | "He straightened up, wincing at" | | 13 | "She crouched beside the body" | | 14 | "Ferris leaned in." | | 15 | "She lifted her gaze along" | | 16 | "Quinn stood slowly, looking at" | | 17 | "Ferris rubbed the back of" | | 18 | "He did, reluctantly." | | 19 | "The beam struck the nearest" |
| | ratio | 0.765 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 51 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 5 | | matches | | 0 | "The stairs down to the old Camden passage smelled of wet concrete and something sweeter underneath, something that put Harlow Quinn in mind of a church her gran…" | | 1 | "She ducked the crime scene tape at the bottom of the steps and held her warrant card up to the uniform posted there, a boy who couldn't have been more than twen…" | | 2 | "These were careful, symmetrical, almost calligraphic, rendered in a paint that seemed to swallow the scene lamps rather than reflect them." | | 3 | "He was a solid man, methodical, the kind of detective who wrote everything down and trusted the paperwork more than his own eyes." | | 4 | "Something glinted against the dead man's left hand, half tucked beneath his thigh as though he'd tried to hide it or tried to hold onto it at the last." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 38 | | tagDensity | 0.132 | | leniency | 0.263 | | rawRatio | 0 | | effectiveRatio | 0 | |