| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 10 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.68% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1157 | | 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) | |
| 48.14% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1157 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "weight" | | 1 | "familiar" | | 2 | "fluttered" | | 3 | "pulsed" | | 4 | "warmth" | | 5 | "grave" | | 6 | "velvet" | | 7 | "silk" | | 8 | "porcelain" | | 9 | "gleaming" | | 10 | "trembled" |
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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 | 95 | | matches | (empty) | |
| 82.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 100 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1157 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 38 | | wordCount | 1083 | | uniqueNames | 19 | | maxNameDensity | 0.83 | | worstName | "Herrera" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Herrera" | | discoveredNames | | Herrera | 9 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Quinn | 7 | | Saint | 2 | | Christopher | 2 | | Morris | 3 | | Thames | 1 | | Metropolitan | 1 | | Police | 1 | | Tube | 1 | | London | 1 | | Veil | 1 | | Market | 1 | | Tomás | 2 |
| | persons | | 0 | "Herrera" | | 1 | "Raven" | | 2 | "Quinn" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Morris" | | 6 | "Police" | | 7 | "Market" | | 8 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" | | 4 | "Thames" | | 5 | "Metropolitan" | | 6 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like stretched skin" |
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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 | 1157 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 100 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 48.21 | | std | 36.4 | | cv | 0.755 | | sampleLengths | | 0 | 133 | | 1 | 6 | | 2 | 88 | | 3 | 73 | | 4 | 143 | | 5 | 14 | | 6 | 53 | | 7 | 28 | | 8 | 26 | | 9 | 13 | | 10 | 8 | | 11 | 63 | | 12 | 93 | | 13 | 41 | | 14 | 61 | | 15 | 36 | | 16 | 41 | | 17 | 20 | | 18 | 3 | | 19 | 45 | | 20 | 65 | | 21 | 44 | | 22 | 19 | | 23 | 41 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 95 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 173 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 100 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1088 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.014705882352941176 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0027573529411764708 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 100 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 100 | | mean | 11.57 | | std | 7.37 | | cv | 0.637 | | sampleLengths | | 0 | 13 | | 1 | 18 | | 2 | 23 | | 3 | 16 | | 4 | 18 | | 5 | 8 | | 6 | 37 | | 7 | 4 | | 8 | 2 | | 9 | 16 | | 10 | 17 | | 11 | 8 | | 12 | 11 | | 13 | 20 | | 14 | 16 | | 15 | 6 | | 16 | 13 | | 17 | 17 | | 18 | 5 | | 19 | 32 | | 20 | 1 | | 21 | 16 | | 22 | 18 | | 23 | 7 | | 24 | 10 | | 25 | 4 | | 26 | 1 | | 27 | 23 | | 28 | 7 | | 29 | 5 | | 30 | 27 | | 31 | 24 | | 32 | 4 | | 33 | 10 | | 34 | 6 | | 35 | 16 | | 36 | 8 | | 37 | 7 | | 38 | 16 | | 39 | 2 | | 40 | 10 | | 41 | 4 | | 42 | 12 | | 43 | 4 | | 44 | 15 | | 45 | 7 | | 46 | 6 | | 47 | 7 | | 48 | 8 | | 49 | 8 |
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| 53.54% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 12 | | diversityRatio | 0.3939393939393939 | | totalSentences | 99 | | uniqueOpeners | 39 | |
| 37.88% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 88 | | matches | | 0 | "Only calculation, the same sterile" |
| | ratio | 0.011 | |
| 60.91% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 88 | | matches | | 0 | "Her boot slipped on a" | | 1 | "She kept pace, eighteen years" | | 2 | "Her salt-and-pepper hair clung to" | | 3 | "She had him cornered three" | | 4 | "He was leading her." | | 5 | "She adjusted her grip on" | | 6 | "They burst onto Charing Cross" | | 7 | "He moved with the fluid" | | 8 | "She checked her watch." | | 9 | "She knew this territory." | | 10 | "She did not understand it," | | 11 | "His warm brown eyes met" | | 12 | "His voice carried, precise and" | | 13 | "She did not lower the" | | 14 | "Her finger rested on the" | | 15 | "His hand brushed the plywood," | | 16 | "She thought of Morris." | | 17 | "She thought of the whispers" | | 18 | "She thought of the bone" | | 19 | "She looked at the gap" |
| | ratio | 0.398 | |
| 28.18% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 88 | | matches | | 0 | "Her boot slipped on a" | | 1 | "Tomás Herrera cut left into" | | 2 | "Rain sheeted down in solid" | | 3 | "She kept pace, eighteen years" | | 4 | "The worn leather of her" | | 5 | "Her salt-and-pepper hair clung to" | | 6 | "She had him cornered three" | | 7 | "He was leading her." | | 8 | "Herrera's trainers splashed through a" | | 9 | "The scar running along his" | | 10 | "Quinn followed, her shoulder slamming" | | 11 | "The stench of fried oil" | | 12 | "She adjusted her grip on" | | 13 | "They burst onto Charing Cross" | | 14 | "Traffic roared, a river of" | | 15 | "The city noise crashed over" | | 16 | "Herrera did not look back." | | 17 | "He moved with the fluid" | | 18 | "The streets grew rougher, the" | | 19 | "Graffiti climbed the brickwork in" |
| | ratio | 0.864 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 51 | | technicalSentenceCount | 12 | | matches | | 0 | "Rain sheeted down in solid walls, turning the pavement into a river of black glass that swallowed the neon from the Soho strip." | | 1 | "Herrera's trainers splashed through a puddle that reflected the green neon of a shuttered kebab shop." | | 2 | "He moved with the fluid economy of a man who knew the route by heart, his Saint Christopher medallion swinging against his collarbone with each stride, a pendul…" | | 3 | "Something that did not belong in the Metropolitan Police's jurisdiction, something that had reached up with hands made of weed and river silt and pulled him und…" | | 4 | "The case files sat in her desk at the station, sealed, the ones that made no sense, the ones her superiors called stress-induced hallucinations." | | 5 | "Plywood sheets blocked a disused Tube entrance, the kind that dotted London like forgotten, rotten teeth." | | 6 | "Quinn skidded to a halt, her boots scraping gravel, rain plastering her hair to her skull." | | 7 | "How they found him in the water, twisted in ways that defied anatomy, his eyes replaced with river stones." | | 8 | "She thought of the bone token clutched in her pocket, evidence she had confiscated from a suspect last week, a carved fragment of femur that had pulsed with a w…" | | 9 | "The air that wafted up smelled of ozone, ancient stone, and something sweet and rotting, like lilies left too long in stagnant water." | | 10 | "Vendors with too many joints in their fingers sold vials of liquid starlight and jars of eyes that blinked." | | 11 | "From the tunnel behind her, a sound like a train approaching, but wrong, metal screaming against stone in a rhythm that matched her own heartbeat." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 50.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "Herrera whispered (whisper)" |
| | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0.25 | | effectiveRatio | 0.2 | |