| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.62% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1141 | | totalAiIsmAdverbs | 1 | | 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) | |
| 73.71% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1141 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "measured" | | 1 | "glint" | | 2 | "shattered" | | 3 | "gloom" | | 4 | "racing" | | 5 | "silence" |
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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 | 65 | | matches | (empty) | |
| 98.90% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 65 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 67 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1135 | | ratio | 0 | | matches | (empty) | |
| 62.50% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 1 | | matches | | 0 | "In the darker corners of the station houses, among the older cops who drank too much and spoke in hushed tones about the…" |
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| 99.59% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1091 | | uniqueNames | 18 | | maxNameDensity | 1.01 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 2 | | Harlow | 2 | | Quinn | 11 | | Herrera | 8 | | Saint | 1 | | Christopher | 1 | | Soho | 1 | | Wounds | 1 | | Morris | 4 | | Glock | 1 | | Tube | 1 | | Blitz | 1 | | TfL | 1 | | Whitechapel | 1 | | Veil | 1 | | Market | 1 | | God | 1 | | London | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Whitechapel" | | 2 | "London" |
| | globalScore | 0.996 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | 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 | 1135 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 67 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 25 | | mean | 45.4 | | std | 28.18 | | cv | 0.621 | | sampleLengths | | 0 | 21 | | 1 | 64 | | 2 | 76 | | 3 | 16 | | 4 | 120 | | 5 | 43 | | 6 | 57 | | 7 | 30 | | 8 | 111 | | 9 | 27 | | 10 | 42 | | 11 | 51 | | 12 | 52 | | 13 | 61 | | 14 | 12 | | 15 | 39 | | 16 | 16 | | 17 | 70 | | 18 | 51 | | 19 | 15 | | 20 | 65 | | 21 | 21 | | 22 | 41 | | 23 | 17 | | 24 | 17 |
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| 94.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 65 | | matches | | 0 | "was flushed" | | 1 | "been retrofitted" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 170 | | matches | | 0 | "was moving" | | 1 | "was waiting" | | 2 | "was crouching" | | 3 | "wasn't running" | | 4 | "was standing" | | 5 | "was suffering" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 1 | | flaggedSentences | 6 | | totalSentences | 67 | | ratio | 0.09 | | matches | | 0 | "The rain in Camden didn’t fall; it hammered, turning the slick asphalt of the high street into a dark, oil-stained mirror." | | 1 | "Her left wrist caught the light of an oncoming delivery van—the worn leather strap of her watch soaked through, the hands ticking past midnight." | | 2 | "The alley was a dead end—or it should have been." | | 3 | "The fabric tore back, exposing his left forearm—and the jagged, pale line of an old knife scar running from his wrist to his elbow." | | 4 | "The air rising from the stairwell was warm—unnaturally so." | | 5 | "He produced a small, pale object—no larger than a cigarette lighter—and tossed it into a brass bowl held by the hooded guardian." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 269 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 4 | | adverbRatio | 0.01486988847583643 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0037174721189591076 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 67 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 67 | | mean | 16.94 | | std | 7.77 | | cv | 0.459 | | sampleLengths | | 0 | 21 | | 1 | 26 | | 2 | 11 | | 3 | 27 | | 4 | 20 | | 5 | 14 | | 6 | 16 | | 7 | 26 | | 8 | 12 | | 9 | 4 | | 10 | 24 | | 11 | 11 | | 12 | 26 | | 13 | 28 | | 14 | 31 | | 15 | 19 | | 16 | 24 | | 17 | 16 | | 18 | 10 | | 19 | 11 | | 20 | 20 | | 21 | 12 | | 22 | 18 | | 23 | 11 | | 24 | 18 | | 25 | 10 | | 26 | 17 | | 27 | 24 | | 28 | 31 | | 29 | 7 | | 30 | 20 | | 31 | 22 | | 32 | 20 | | 33 | 9 | | 34 | 24 | | 35 | 18 | | 36 | 11 | | 37 | 17 | | 38 | 24 | | 39 | 20 | | 40 | 19 | | 41 | 22 | | 42 | 9 | | 43 | 3 | | 44 | 18 | | 45 | 4 | | 46 | 17 | | 47 | 16 | | 48 | 5 | | 49 | 6 |
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| 65.17% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.417910447761194 | | totalSentences | 67 | | uniqueOpeners | 28 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 64 | | matches | (empty) | | ratio | 0 | |
| 76.25% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 64 | | matches | | 0 | "Her boots struck the wet" | | 1 | "He was twenty-nine, built light," | | 2 | "Her voice was a sharp" | | 3 | "He knew what was waiting" | | 4 | "He was the patch-up man" | | 5 | "She reached into her coat," | | 6 | "Her left wrist caught the" | | 7 | "She held her flashlight in" | | 8 | "she called out, her sharp" | | 9 | "His olive skin was flushed" | | 10 | "He yanked himself free with" | | 11 | "She squeezed through the tear," | | 12 | "It smelled of ozone, heavy" | | 13 | "It was a smell she" | | 14 | "He was standing before a" | | 15 | "She watched as Herrera fumbled" | | 16 | "He produced a small, pale" | | 17 | "It hit the metal with" | | 18 | "He ducked under the bar" | | 19 | "She knew where she was." |
| | ratio | 0.359 | |
| 61.56% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 64 | | matches | | 0 | "The rain in Camden didn’t" | | 1 | "Detective Harlow Quinn ran with" | | 2 | "Her boots struck the wet" | | 3 | "Tomás Herrera bumped against a" | | 4 | "He was twenty-nine, built light," | | 5 | "Quinn rounded the corner a" | | 6 | "Her voice was a sharp" | | 7 | "He knew what was waiting" | | 8 | "Quinn had been trailing him" | | 9 | "He was the patch-up man" | | 10 | "Wounds that looked suspiciously like" | | 11 | "She reached into her coat," | | 12 | "Her left wrist caught the" | | 13 | "Herrera cut left, slipping into" | | 14 | "The alley was a dead" | | 15 | "Quinn closed the distance, her" | | 16 | "She held her flashlight in" | | 17 | "she called out, her sharp" | | 18 | "The beam caught him at" | | 19 | "Herrera was crouching by a" |
| | ratio | 0.797 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 64 | | matches | | 0 | "Even through the deluge, Quinn" | | 1 | "If she stepped past that" | | 2 | "If he slipped into that" |
| | ratio | 0.047 | |
| 35.04% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 53 | | technicalSentenceCount | 8 | | matches | | 0 | "Detective Harlow Quinn ran with the measured, low-impact stride of someone who had spent eighteen years on the force learning how not to destroy her knees." | | 1 | "Herrera was crouching by a heavy chain-link fence that had been ruthlessly peeled back from the concrete foundation." | | 2 | "It smelled of ozone, heavy damp earth, and a thick, aromatic resin that stung the back of her throat like burnt cinnamon and copper." | | 3 | "He was standing before a heavy iron turnstile that had been retrofitted into the ancient tiled archway." | | 4 | "Quinn stayed pinned to the wall, her mind racing against the ticking seconds on her watch." | | 5 | "A hidden bazaar beneath Camden, moving with the cycle of the full moon, trading in things that breached every law of God, man, and nature." | | 6 | "She unbuttoned her coat, drawing her sidearm with a smooth, practiced motion, keeping it low against her thigh." | | 7 | "She clicked off her flashlight, letting her eyes adjust to the strange, flickering amber glow seeping up from the bottom of the shaft." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 1 | | matches | | 0 | "she called out, her sharp jaw tightening" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | 0 | "she called out (call out)" |
| | dialogueSentences | 3 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 1 | | effectiveRatio | 0.667 | |