| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 15 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 34 | | tagDensity | 0.441 | | leniency | 0.882 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.90% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1615 | | 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) | |
| 34.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1615 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | |
| | highlights | | 0 | "echoed" | | 1 | "gloom" | | 2 | "navigated" | | 3 | "rhythmic" | | 4 | "mosaic" | | 5 | "silk" | | 6 | "scanning" | | 7 | "intensity" | | 8 | "predator" | | 9 | "processed" | | 10 | "flickered" | | 11 | "weight" | | 12 | "etched" | | 13 | "whisper" | | 14 | "navigate" | | 15 | "facade" | | 16 | "profound" | | 17 | "chill" | | 18 | "standard" | | 19 | "grave" |
| |
| 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 | 1 | | narrationSentences | 95 | | matches | | |
| 82.71% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 2 | | narrationSentences | 95 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 114 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 54 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1604 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 72 | | wordCount | 1192 | | uniqueNames | 25 | | maxNameDensity | 2.18 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 4 | | Harlow | 26 | | Quinn | 2 | | South | 1 | | Kentish | 1 | | Town | 1 | | North | 1 | | London | 3 | | Tube | 1 | | Miller | 8 | | Tie | 1 | | Northern | 1 | | Line | 1 | | Victorian | 1 | | Vane | 2 | | Morris | 2 | | Thames | 1 | | Kowalski | 1 | | Crown | 1 | | Jewels | 1 | | Eva | 8 | | Veil | 1 | | Compass | 1 | | High | 1 | | Street | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Miller" | | 3 | "Line" | | 4 | "Vane" | | 5 | "Morris" | | 6 | "Kowalski" | | 7 | "Eva" |
| | places | | 0 | "Camden" | | 1 | "South" | | 2 | "Kentish" | | 3 | "Town" | | 4 | "North" | | 5 | "London" | | 6 | "Thames" | | 7 | "High" | | 8 | "Street" |
| | globalScore | 0.409 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 74 | | 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 | 1604 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 114 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 37.3 | | std | 23.45 | | cv | 0.629 | | sampleLengths | | 0 | 87 | | 1 | 46 | | 2 | 10 | | 3 | 44 | | 4 | 30 | | 5 | 58 | | 6 | 50 | | 7 | 56 | | 8 | 35 | | 9 | 53 | | 10 | 7 | | 11 | 8 | | 12 | 44 | | 13 | 8 | | 14 | 61 | | 15 | 75 | | 16 | 8 | | 17 | 58 | | 18 | 65 | | 19 | 41 | | 20 | 10 | | 21 | 12 | | 22 | 10 | | 23 | 79 | | 24 | 32 | | 25 | 9 | | 26 | 40 | | 27 | 31 | | 28 | 4 | | 29 | 13 | | 30 | 12 | | 31 | 71 | | 32 | 47 | | 33 | 53 | | 34 | 2 | | 35 | 65 | | 36 | 51 | | 37 | 41 | | 38 | 34 | | 39 | 36 | | 40 | 4 | | 41 | 54 | | 42 | 50 |
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| 90.49% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 95 | | matches | | 0 | "been twisted" | | 1 | "was illuminated" | | 2 | "was dressed" | | 3 | "was locked" | | 4 | "were fixed" |
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| 32.50% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 199 | | matches | | 0 | "were falling" | | 1 | "was tugging" | | 2 | "was looking" | | 3 | "was stuttering" | | 4 | "were failing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 11 | | semicolonCount | 5 | | flaggedSentences | 13 | | totalSentences | 114 | | ratio | 0.114 | | matches | | 0 | "It was the smell that bothered her—a sharp, metallic tang that shouldn't have been there, rising from the throat of an alleyway leading toward the boarded-up entrance of the old South Kentish Town station." | | 1 | "Harlow navigated the debris of the alley—shards of green glass, crumpled energy drink cans, the urban detritus of North London." | | 2 | "The padlock hadn't been picked or cut; it had been twisted until the steel snapped like a dry twig." | | 3 | "Vane’s expression wasn't one of narcotic bliss; his mouth was locked in a silent, jagged O, and his eyes—wide and clouded—were fixed on the ceiling." | | 4 | "Something was tugging at the back of her mind—a ghost of a memory from three years ago." | | 5 | "Inside that clean circle, something caught the overhead light—a faint, greenish glimmer." | | 6 | "On one side was a debossed image of a gate; on the other, a series of symbols that made her head ache if she stared too long." | | 7 | "Harlow pocketed the token—a breach of protocol she’d justify later—and headed back up the stairs." | | 8 | "As Harlow approached, Eva tucked a stray lock of hair behind her left ear—a nervous flick of the wrist Harlow had seen her do a dozen times in the museum’s reading room." | | 9 | "Harlow caught a glimpse of brass and a hint of green patina—verdigris." | | 10 | "They didn't point north; they pointed toward things that shouldn't exist." | | 11 | "Miller and the others saw a crime scene—a tragedy of the urban underbelly." | | 12 | "The station wasn't just a grave; it was a doorway." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1213 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.02225886232481451 | | lyAdverbCount | 10 | | lyAdverbRatio | 0.008244023083264633 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 114 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 114 | | mean | 14.07 | | std | 9.51 | | cv | 0.676 | | sampleLengths | | 0 | 27 | | 1 | 21 | | 2 | 5 | | 3 | 34 | | 4 | 6 | | 5 | 2 | | 6 | 25 | | 7 | 13 | | 8 | 10 | | 9 | 20 | | 10 | 11 | | 11 | 13 | | 12 | 15 | | 13 | 15 | | 14 | 26 | | 15 | 19 | | 16 | 13 | | 17 | 4 | | 18 | 31 | | 19 | 15 | | 20 | 19 | | 21 | 21 | | 22 | 16 | | 23 | 10 | | 24 | 25 | | 25 | 18 | | 26 | 5 | | 27 | 5 | | 28 | 25 | | 29 | 7 | | 30 | 8 | | 31 | 5 | | 32 | 10 | | 33 | 7 | | 34 | 13 | | 35 | 9 | | 36 | 4 | | 37 | 4 | | 38 | 4 | | 39 | 15 | | 40 | 42 | | 41 | 10 | | 42 | 17 | | 43 | 17 | | 44 | 12 | | 45 | 3 | | 46 | 16 | | 47 | 8 | | 48 | 3 | | 49 | 6 |
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| 55.26% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.3684210526315789 | | totalSentences | 114 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 70.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 88 | | matches | | 0 | "She didn't mind the damp." | | 1 | "It was the smell that" | | 2 | "She paused to check her" | | 3 | "She adjusted it, her sharp" | | 4 | "He looked tired, his Tie" | | 5 | "She ran a hand through" | | 6 | "They descended a spiral staircase" | | 7 | "He was dressed in expensive" | | 8 | "She didn't touch him yet." | | 9 | "She looked at his face." | | 10 | "She reached out, gingerly tilting" | | 11 | "It was a perfectly circular" | | 12 | "She sat back on her" | | 13 | "She stood up and paced" | | 14 | "She’d seen the way the" | | 15 | "She was looking at the" | | 16 | "It looked as though someone" | | 17 | "She reached into her pocket," | | 18 | "She knelt and picked up" | | 19 | "It was a bone token," |
| | ratio | 0.375 | |
| 16.82% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 78 | | totalSentences | 88 | | matches | | 0 | "The rain over Camden didn't" | | 1 | "Detective Harlow Quinn stepped out" | | 2 | "She didn't mind the damp." | | 3 | "It was the smell that" | | 4 | "She paused to check her" | | 5 | "The leather strap was butter-soft" | | 6 | "She adjusted it, her sharp" | | 7 | "a voice echoed from the" | | 8 | "Harlow navigated the debris of" | | 9 | "He looked tired, his Tie" | | 10 | "Harlow said, her voice a" | | 11 | "She ran a hand through" | | 12 | "Miller gestured toward the rusted" | | 13 | "The padlock hadn't been picked" | | 14 | "Harlow followed him down." | | 15 | "The air grew colder with" | | 16 | "They descended a spiral staircase" | | 17 | "The scene was illuminated by" | | 18 | "He was dressed in expensive" | | 19 | "Miller said, reading from a" |
| | ratio | 0.886 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 80.20% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 5 | | matches | | 0 | "The rain over Camden didn't fall so much as it drifted, a fine, grey mist that turned the soot of the brickwork into a slick, black patina." | | 1 | "It was the smell that bothered her—a sharp, metallic tang that shouldn't have been there, rising from the throat of an alleyway leading toward the boarded-up en…" | | 2 | "The scene was illuminated by portable halogen lamps that threw long, jagged shadows against the curves of the tunnel." | | 3 | "Eva Kowalski looked exactly as she had in the surveillance photos Harlow had been collecting for months: curly red hair escaping a messy bun, round glasses fogg…" | | 4 | "She used her height, leaning in just enough to be intimidating." |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 15 | | uselessAdditionCount | 3 | | matches | | 0 | "Harlow said, her voice a low, gravelly alto" | | 1 | "Eva said, her voice small but steady" | | 2 | "Harlow said, her voice dropping to a dangerous whisper" |
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| 61.76% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 12 | | fancyCount | 3 | | fancyTags | | 0 | "Harlow whispered (whisper)" | | 1 | "Harlow corrected (correct)" | | 2 | "Harlow muttered (mutter)" |
| | dialogueSentences | 34 | | tagDensity | 0.353 | | leniency | 0.706 | | rawRatio | 0.25 | | effectiveRatio | 0.176 | |