| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 21 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn said quietly [quietly]" |
| | dialogueSentences | 61 | | tagDensity | 0.344 | | leniency | 0.689 | | rawRatio | 0.048 | | effectiveRatio | 0.033 | |
| 93.91% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1642 | | 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) | |
| 78.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1642 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "tinge" | | 1 | "traced" | | 2 | "processed" | | 3 | "etched" | | 4 | "unwavering" | | 5 | "flickered" | | 6 | "tracing" |
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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 | 104 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 104 | | filterMatches | (empty) | | hedgeMatches | | 0 | "began to" | | 1 | "appeared to" |
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| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 144 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 53 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1642 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 16 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1122 | | uniqueNames | 9 | | maxNameDensity | 1.6 | | worstName | "Davies" | | maxWindowNameDensity | 3 | | worstWindowName | "Davies" | | discoveredNames | | Quinn | 12 | | Camden | 1 | | High | 1 | | Street | 1 | | Tube | 1 | | Davies | 18 | | Bottles | 1 | | Ayling | 1 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Davies" | | 2 | "Bottles" | | 3 | "Ayling" | | 4 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 0.698 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 1 | | matches | | 0 | "as though reaching for something that had already fled" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.609 | | wordCount | 1642 | | matches | | 0 | "not north but directly at the far end of the platform" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 144 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 70 | | mean | 23.46 | | std | 21.85 | | cv | 0.932 | | sampleLengths | | 0 | 45 | | 1 | 3 | | 2 | 34 | | 3 | 13 | | 4 | 106 | | 5 | 11 | | 6 | 34 | | 7 | 83 | | 8 | 35 | | 9 | 60 | | 10 | 7 | | 11 | 21 | | 12 | 6 | | 13 | 5 | | 14 | 47 | | 15 | 15 | | 16 | 3 | | 17 | 12 | | 18 | 43 | | 19 | 36 | | 20 | 15 | | 21 | 9 | | 22 | 2 | | 23 | 65 | | 24 | 5 | | 25 | 1 | | 26 | 41 | | 27 | 18 | | 28 | 17 | | 29 | 64 | | 30 | 44 | | 31 | 6 | | 32 | 28 | | 33 | 2 | | 34 | 4 | | 35 | 16 | | 36 | 10 | | 37 | 2 | | 38 | 30 | | 39 | 42 | | 40 | 7 | | 41 | 5 | | 42 | 16 | | 43 | 52 | | 44 | 34 | | 45 | 4 | | 46 | 6 | | 47 | 1 | | 48 | 7 | | 49 | 51 |
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| 98.52% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 104 | | matches | | 0 | "were lined" | | 1 | "was dressed" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 195 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 144 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1126 | | adjectiveStacks | 1 | | stackExamples | | 0 | "thin verdigris-green line" |
| | adverbCount | 30 | | adverbRatio | 0.02664298401420959 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.007104795737122558 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 144 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 144 | | mean | 11.4 | | std | 9.48 | | cv | 0.831 | | sampleLengths | | 0 | 8 | | 1 | 24 | | 2 | 5 | | 3 | 8 | | 4 | 3 | | 5 | 15 | | 6 | 5 | | 7 | 14 | | 8 | 4 | | 9 | 9 | | 10 | 11 | | 11 | 18 | | 12 | 29 | | 13 | 15 | | 14 | 5 | | 15 | 28 | | 16 | 10 | | 17 | 1 | | 18 | 12 | | 19 | 14 | | 20 | 8 | | 21 | 3 | | 22 | 21 | | 23 | 24 | | 24 | 21 | | 25 | 5 | | 26 | 2 | | 27 | 2 | | 28 | 5 | | 29 | 8 | | 30 | 27 | | 31 | 2 | | 32 | 20 | | 33 | 10 | | 34 | 12 | | 35 | 9 | | 36 | 7 | | 37 | 7 | | 38 | 21 | | 39 | 6 | | 40 | 3 | | 41 | 2 | | 42 | 2 | | 43 | 25 | | 44 | 15 | | 45 | 2 | | 46 | 3 | | 47 | 4 | | 48 | 11 | | 49 | 3 |
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| 70.37% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4513888888888889 | | totalSentences | 144 | | uniqueOpeners | 65 | |
| 77.52% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 86 | | matches | | 0 | "Just below the jawline, where" | | 1 | "Then every lamp on the" |
| | ratio | 0.023 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 86 | | matches | | 0 | "He held a second evidence" | | 1 | "She straightened and let her" | | 2 | "They had reached the old" | | 3 | "She didn't answer." | | 4 | "She walked the length of" | | 5 | "He was dressed in a" | | 6 | "She pulled a glove tighter" | | 7 | "His fingernails were clean, filed" | | 8 | "She checked the face: quarter" | | 9 | "She traced the air above" | | 10 | "He tugged at his collar," | | 11 | "She remembered Ayling's name from" | | 12 | "She remembered the report he'd" | | 13 | "He crouched across from her," | | 14 | "She held up the evidence" | | 15 | "She turned the bag over" | | 16 | "She stood and walked to" | | 17 | "She took a photograph and" | | 18 | "She pocketed her phone" | | 19 | "She began to move down" |
| | ratio | 0.291 | |
| 23.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 75 | | totalSentences | 86 | | matches | | 0 | "The token in her palm" | | 1 | "Quinn turned it over under" | | 2 | "Bone chalked yellow with age." | | 3 | "This had the translucence of" | | 4 | "Davies crouched beside her, his" | | 5 | "Dust fumed around his trousers." | | 6 | "He held a second evidence" | | 7 | "Quinn slipped the token into" | | 8 | "She straightened and let her" | | 9 | "They had reached the old" | | 10 | "Arches rose into blackness overhead." | | 11 | "The walls were lined with" | | 12 | "Davies snapped a photograph of" | | 13 | "Bottles lined its shelf, dozens" | | 14 | "She didn't answer." | | 15 | "She walked the length of" | | 16 | "The dead man lay halfway" | | 17 | "He was dressed in a" | | 18 | "Nothing in the waistcoat either." | | 19 | "Davies said, arriving at her" |
| | ratio | 0.872 | |
| 58.14% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 86 | | matches | | 0 | "Now the room stretched out" |
| | ratio | 0.012 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 39 | | technicalSentenceCount | 8 | | matches | | 0 | "They had reached the old platform level forty minutes ago, squeezing through a maintenance tunnel behind a boarded-up entrance on Camden High Street that smelle…" | | 1 | "The dead man lay halfway between the second and third stall, face-down, one arm outstretched as though reaching for something that had already fled." | | 2 | "She remembered the report he'd signed three years ago, the one that listed cause of death as natural causes for a man found in a drainage culvert with no explan…" | | 3 | "A man arrives at a hidden entrance with a token, descends into a station with no cameras, and dies of something that leaves no physical cause except two small m…" | | 4 | "Small, brass, its face etched with lines and symbols that radiated from the centre like a spider's web." | | 5 | "He watched the needle hold its position, unwavering, as though some force beyond their sight had anchored it." | | 6 | "The air temperature on the platform dropped, and every bottle on every stall hummed in unison, a thin glassy note that built until it pressed against her teeth." | | 7 | "The needle pointed directly at it, and the sigils on the compass face had begun to glow, a thin verdigris-green line tracing each symbol in sequence, as though …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 21 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 61 | | tagDensity | 0.18 | | leniency | 0.361 | | rawRatio | 0.091 | | effectiveRatio | 0.033 | |