| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 2 | | adverbTags | | 0 | "she said quietly [quietly]" | | 1 | "Kowalski said carefully [carefully]" |
| | dialogueSentences | 40 | | tagDensity | 0.475 | | leniency | 0.95 | | rawRatio | 0.105 | | effectiveRatio | 0.1 | |
| 96.50% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1430 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 79.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1430 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "shattered" | | 2 | "etched" | | 3 | "magnetic" | | 4 | "comfortable" | | 5 | "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 | 70 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 70 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 91 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 70 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 2 | | markdownWords | 8 | | totalWords | 1440 | | ratio | 0.006 | | matches | | 0 | "assorted metal debris, floor, 2m from body" | | 1 | "before" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 962 | | uniqueNames | 13 | | maxNameDensity | 1.25 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Met | 1 | | Camden | 1 | | Tube | 2 | | Marcus | 1 | | Osei | 6 | | London | 3 | | Morris | 3 | | Rigor | 1 | | Clean | 1 | | English | 1 | | Kowalski | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Camden" | | 3 | "Marcus" | | 4 | "Osei" | | 5 | "Morris" | | 6 | "Clean" | | 7 | "Kowalski" |
| | places | | | globalScore | 0.876 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | 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 | 1440 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 91 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 31.3 | | std | 27.66 | | cv | 0.884 | | sampleLengths | | 0 | 30 | | 1 | 54 | | 2 | 6 | | 3 | 50 | | 4 | 17 | | 5 | 89 | | 6 | 29 | | 7 | 32 | | 8 | 10 | | 9 | 3 | | 10 | 9 | | 11 | 53 | | 12 | 6 | | 13 | 61 | | 14 | 5 | | 15 | 34 | | 16 | 7 | | 17 | 71 | | 18 | 9 | | 19 | 5 | | 20 | 85 | | 21 | 14 | | 22 | 5 | | 23 | 12 | | 24 | 76 | | 25 | 8 | | 26 | 34 | | 27 | 70 | | 28 | 10 | | 29 | 2 | | 30 | 29 | | 31 | 68 | | 32 | 64 | | 33 | 41 | | 34 | 3 | | 35 | 77 | | 36 | 16 | | 37 | 33 | | 38 | 9 | | 39 | 38 | | 40 | 5 | | 41 | 100 | | 42 | 12 | | 43 | 16 | | 44 | 24 | | 45 | 9 |
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| 85.21% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 70 | | matches | | 0 | "been taught" | | 1 | "been stabbed" | | 2 | "been worn" | | 3 | "been logged" |
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| 16.09% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 145 | | matches | | 0 | "was not pointing" | | 1 | "was not pointing" | | 2 | "was tucking" | | 3 | "was measuring" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 91 | | ratio | 0.055 | | matches | | 0 | "She showed her warrant card to the constable at the police tape, though the woman had already recognized her — everyone in the Met knew Quinn by the cropped salt-and-pepper hair and the bearing that made cadets straighten their spines when she passed." | | 1 | "Dark rectangles on the tile where the grime had been worn away in patches — rectangles the size of crates, the size of stalls." | | 2 | "The third thing wrong was in the victim's coat pocket, where the SOCO's evidence tray held the contents of his person: keys, cash, a phone with a shattered screen — and a dozen small tokens the colour of old ivory, each one the size of a pound coin, each one drilled through and carved with a pattern of fine lines." | | 3 | "The needle lay flat against the glass, straining, trembling like a dog at a door — and it was not pointing north." | | 4 | "She rubbed her thumb over the cracked leather of the watch on her left wrist — his watch, now hers — and kept her face still." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 961 | | adjectiveStacks | 1 | | stackExamples | | | adverbCount | 22 | | adverbRatio | 0.022892819979188347 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.009365244536940686 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 91 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 91 | | mean | 15.82 | | std | 14 | | cv | 0.885 | | sampleLengths | | 0 | 30 | | 1 | 43 | | 2 | 11 | | 3 | 6 | | 4 | 10 | | 5 | 24 | | 6 | 16 | | 7 | 17 | | 8 | 12 | | 9 | 18 | | 10 | 59 | | 11 | 3 | | 12 | 26 | | 13 | 10 | | 14 | 1 | | 15 | 2 | | 16 | 19 | | 17 | 2 | | 18 | 8 | | 19 | 3 | | 20 | 9 | | 21 | 24 | | 22 | 7 | | 23 | 22 | | 24 | 4 | | 25 | 2 | | 26 | 19 | | 27 | 4 | | 28 | 12 | | 29 | 23 | | 30 | 3 | | 31 | 5 | | 32 | 34 | | 33 | 7 | | 34 | 24 | | 35 | 5 | | 36 | 18 | | 37 | 24 | | 38 | 9 | | 39 | 5 | | 40 | 21 | | 41 | 30 | | 42 | 12 | | 43 | 8 | | 44 | 11 | | 45 | 3 | | 46 | 14 | | 47 | 5 | | 48 | 5 | | 49 | 7 |
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| 94.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.6373626373626373 | | totalSentences | 91 | | uniqueOpeners | 58 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 88.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 19 | | totalSentences | 58 | | matches | | 0 | "She showed her warrant card" | | 1 | "Her torch beam cut through" | | 2 | "her colleague said, rising from" | | 3 | "She stood at the edge" | | 4 | "She turned his hand over" | | 5 | "She shone her torch along" | | 6 | "She stood and swept her" | | 7 | "she said quietly" | | 8 | "She followed one of the" | | 9 | "It ran twenty feet across" | | 10 | "She pressed her palm flat" | | 11 | "He did, and she watched" | | 12 | "She set it down and" | | 13 | "It was not pointing anywhere" | | 14 | "It pointed, with the fixed" | | 15 | "She held it up" | | 16 | "She rubbed her thumb over" | | 17 | "They'd parked her on a" | | 18 | "She held the compass up" |
| | ratio | 0.328 | |
| 72.07% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 45 | | totalSentences | 58 | | matches | | 0 | "The smell hit Harlow Quinn" | | 1 | "She showed her warrant card" | | 2 | "Camden's dead Tube station swallowed" | | 3 | "Her torch beam cut through" | | 4 | "The forensic lights someone had" | | 5 | "The body lay in the" | | 6 | "her colleague said, rising from" | | 7 | "Marcus Osei had a round," | | 8 | "Quinn didn't answer." | | 9 | "She stood at the edge" | | 10 | "The victim lay on his" | | 11 | "Latex gloves, then two fingers" | | 12 | "She turned his hand over" | | 13 | "Rigor was barely started in" | | 14 | "Osei shifted his weight." | | 15 | "She shone her torch along" | | 16 | "She stood and swept her" | | 17 | "The second thing wrong was" | | 18 | "she said quietly" | | 19 | "She followed one of the" |
| | ratio | 0.776 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 58.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 34 | | technicalSentenceCount | 4 | | matches | | 0 | "Marcus Osei had a round, agreeable face that had fooled more suspects than any interrogation room in London." | | 1 | "Along the walls, iron hooks sat at chest height in mortar that had no business holding modern fittings." | | 2 | "Small, brass, furred green with verdigris, its casing etched with symbols that were definitely not degrees and definitely not English." | | 3 | "It pointed, with the fixed insistence of a compass that had somewhere to be, directly at the cold brick wall." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 40 | | tagDensity | 0.225 | | leniency | 0.45 | | rawRatio | 0.111 | | effectiveRatio | 0.05 | |