| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 24 | | tagDensity | 0.375 | | leniency | 0.75 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.71% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1521 | | 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) | |
| 60.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1521 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "flicked" | | 1 | "warmth" | | 2 | "loomed" | | 3 | "echo" | | 4 | "weight" | | 5 | "jaw clenched" | | 6 | "fractured" | | 7 | "chill" | | 8 | "flickered" | | 9 | "pulse" | | 10 | "silence" | | 11 | "echoed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 178 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 178 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 193 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1512 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1375 | | uniqueNames | 15 | | maxNameDensity | 0.51 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 3 | | High | 1 | | Street | 1 | | Quinn | 1 | | Saint | 1 | | Christopher | 1 | | Bodies | 1 | | Harlow | 7 | | Tube | 2 | | Met | 1 | | Underground | 1 | | Silence | 1 | | Rain | 5 | | Herrera | 7 | | Wait | 3 |
| | persons | | 0 | "Quinn" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Bodies" | | 4 | "Harlow" | | 5 | "Tube" | | 6 | "Silence" | | 7 | "Rain" | | 8 | "Herrera" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 105 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 67.72% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.323 | | wordCount | 1512 | | matches | | 0 | "not the damp chill of a normal underground stair, but an absence, as if the air itself didn’t want to exist down t" | | 1 | "not the steady yellow of Tube lights, but a shifting, many-coloured pulse, like candle flames seen thr" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 193 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 89 | | mean | 16.99 | | std | 13.24 | | cv | 0.779 | | sampleLengths | | 0 | 42 | | 1 | 30 | | 2 | 1 | | 3 | 39 | | 4 | 34 | | 5 | 14 | | 6 | 10 | | 7 | 14 | | 8 | 3 | | 9 | 19 | | 10 | 2 | | 11 | 4 | | 12 | 3 | | 13 | 40 | | 14 | 27 | | 15 | 2 | | 16 | 12 | | 17 | 33 | | 18 | 18 | | 19 | 5 | | 20 | 17 | | 21 | 32 | | 22 | 2 | | 23 | 44 | | 24 | 32 | | 25 | 6 | | 26 | 2 | | 27 | 34 | | 28 | 26 | | 29 | 9 | | 30 | 24 | | 31 | 42 | | 32 | 31 | | 33 | 5 | | 34 | 10 | | 35 | 3 | | 36 | 21 | | 37 | 19 | | 38 | 6 | | 39 | 2 | | 40 | 39 | | 41 | 4 | | 42 | 15 | | 43 | 6 | | 44 | 17 | | 45 | 3 | | 46 | 10 | | 47 | 10 | | 48 | 9 | | 49 | 38 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 178 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 239 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 193 | | ratio | 0.005 | | matches | | 0 | "The smell—stale beer, bleach, something sweet and rotting—rolled out in waves." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1384 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 34 | | adverbRatio | 0.024566473988439308 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004335260115606936 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 193 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 193 | | mean | 7.83 | | std | 5.31 | | cv | 0.678 | | sampleLengths | | 0 | 16 | | 1 | 9 | | 2 | 17 | | 3 | 1 | | 4 | 2 | | 5 | 7 | | 6 | 20 | | 7 | 1 | | 8 | 19 | | 9 | 7 | | 10 | 13 | | 11 | 11 | | 12 | 12 | | 13 | 2 | | 14 | 9 | | 15 | 5 | | 16 | 9 | | 17 | 10 | | 18 | 14 | | 19 | 3 | | 20 | 8 | | 21 | 11 | | 22 | 2 | | 23 | 4 | | 24 | 3 | | 25 | 11 | | 26 | 7 | | 27 | 10 | | 28 | 12 | | 29 | 9 | | 30 | 12 | | 31 | 6 | | 32 | 2 | | 33 | 11 | | 34 | 1 | | 35 | 2 | | 36 | 12 | | 37 | 19 | | 38 | 4 | | 39 | 4 | | 40 | 7 | | 41 | 3 | | 42 | 5 | | 43 | 17 | | 44 | 8 | | 45 | 24 | | 46 | 2 | | 47 | 4 | | 48 | 7 | | 49 | 15 |
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| 58.03% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.38860103626943004 | | totalSentences | 193 | | uniqueOpeners | 75 | |
| 65.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 153 | | matches | | 0 | "Somewhere behind the door, music" | | 1 | "Then the gate shifted." | | 2 | "Then a single shout echoed" |
| | ratio | 0.02 | |
| 60.52% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 61 | | totalSentences | 153 | | matches | | 0 | "He touched the scar along" | | 1 | "Her thumb rolled along the" | | 2 | "He cut between a stopped" | | 3 | "Her hand already closed around" | | 4 | "Their eyes met through the" | | 5 | "His widened, the warmth in" | | 6 | "Her boots hit the road" | | 7 | "She shoved off the bonnet" | | 8 | "He moved fast for a" | | 9 | "He wove through a knot" | | 10 | "Her voice ripped out of" | | 11 | "Her feet hammered the slick" | | 12 | "His hood had slipped." | | 13 | "His mouth twisted." | | 14 | "she called, breath cutting the" | | 15 | "He veered left, away from" | | 16 | "Her own footfalls chased his" | | 17 | "He hurdled a stack of" | | 18 | "She ploughed straight through them." | | 19 | "She stumbled, caught herself on" |
| | ratio | 0.399 | |
| 38.43% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 129 | | totalSentences | 153 | | matches | | 0 | "Rain sheeted across Camden High" | | 1 | "Headlights smeared into long, greasy" | | 2 | "Harlow Quinn hunched deeper into" | | 3 | "The Saint Christopher medallion flashed" | | 4 | "He touched the scar along" | | 5 | "That same nervous tic from" | | 6 | "The worn satchel thumped against" | | 7 | "Her thumb rolled along the" | | 8 | "The clique’s quiet paramedic who" | | 9 | "The traffic light flicked red." | | 10 | "He cut between a stopped" | | 11 | "Her hand already closed around" | | 12 | "Herrera glanced back." | | 13 | "Their eyes met through the" | | 14 | "His widened, the warmth in" | | 15 | "Harlow dropped the radio." | | 16 | "Her boots hit the road" | | 17 | "Horns blared as she sliced" | | 18 | "A driver leaned out his" | | 19 | "She shoved off the bonnet" |
| | ratio | 0.843 | |
| 32.68% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 153 | | matches | | 0 | "Before the response crackled back," |
| | ratio | 0.007 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 3 | | matches | | 0 | "Doors that opened for bone-white tokens and shut again as if nothing had touched them." | | 1 | "A patrol car and two uniforms who would crank that padlock, find it stubborn and ordinary, and log the whole thing as a wild goose chase in the Camden rain." | | 2 | "Cold breathed up from below, not the damp chill of a normal underground stair, but an absence, as if the air itself didn’t want to exist down there." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 4 | | matches | | 0 | "she called, breath cutting the words short," | | 1 | "The word tore, raw" | | 2 | "she called, voice dropping in the shaft" | | 3 | "she spoke, not taking her eyes from the light pulsing below," |
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| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "she muttered (mutter)" | | 1 | "he had (have)" | | 2 | "she spoke (speak)" |
| | dialogueSentences | 24 | | tagDensity | 0.208 | | leniency | 0.417 | | rawRatio | 0.6 | | effectiveRatio | 0.25 | |