| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 45 | | tagDensity | 0.222 | | leniency | 0.444 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1724 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 68.10% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1724 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "glint" | | 1 | "familiar" | | 2 | "pulsed" | | 3 | "silence" | | 4 | "whisper" | | 5 | "pulse" | | 6 | "warmth" | | 7 | "flicked" | | 8 | "footsteps" | | 9 | "velvet" |
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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 | 145 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 145 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 180 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1724 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.84% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 72 | | wordCount | 1455 | | uniqueNames | 22 | | maxNameDensity | 1.44 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 4 | | Harlow | 1 | | Quinn | 21 | | Herrera | 17 | | High | 1 | | Street | 1 | | Saint | 1 | | Christopher | 1 | | Raven | 2 | | Nest | 2 | | Mile | 1 | | End | 1 | | Chalk | 1 | | Farm | 1 | | Tube | 2 | | Rain | 4 | | Morris | 6 | | Bovril | 1 | | Brylcreem | 1 | | Essex | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Nest" | | 6 | "Rain" | | 7 | "Morris" | | 8 | "Market" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Raven" | | 4 | "Mile" | | 5 | "Chalk" | | 6 | "Farm" | | 7 | "Essex" |
| | globalScore | 0.778 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 98 | | 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 | 1724 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 180 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 23.3 | | std | 20.89 | | cv | 0.897 | | sampleLengths | | 0 | 55 | | 1 | 74 | | 2 | 5 | | 3 | 8 | | 4 | 12 | | 5 | 3 | | 6 | 3 | | 7 | 57 | | 8 | 48 | | 9 | 21 | | 10 | 24 | | 11 | 6 | | 12 | 14 | | 13 | 4 | | 14 | 70 | | 15 | 13 | | 16 | 70 | | 17 | 12 | | 18 | 40 | | 19 | 31 | | 20 | 42 | | 21 | 16 | | 22 | 7 | | 23 | 53 | | 24 | 10 | | 25 | 11 | | 26 | 5 | | 27 | 49 | | 28 | 10 | | 29 | 1 | | 30 | 34 | | 31 | 8 | | 32 | 6 | | 33 | 42 | | 34 | 19 | | 35 | 7 | | 36 | 2 | | 37 | 40 | | 38 | 36 | | 39 | 55 | | 40 | 9 | | 41 | 3 | | 42 | 7 | | 43 | 17 | | 44 | 73 | | 45 | 18 | | 46 | 8 | | 47 | 2 | | 48 | 25 | | 49 | 13 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 145 | | matches | | 0 | "was gone" | | 1 | "being struck" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 249 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 180 | | ratio | 0.006 | | matches | | 0 | "Her worn leather watch had slipped down her left wrist; she shoved it back with her thumb and felt the familiar ridge of scar tissue beneath the strap, the one that pulsed when storms came in from the river." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1457 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.024021962937542895 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.002745367192862045 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 180 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 180 | | mean | 9.58 | | std | 7.77 | | cv | 0.812 | | sampleLengths | | 0 | 13 | | 1 | 42 | | 2 | 12 | | 3 | 3 | | 4 | 2 | | 5 | 28 | | 6 | 29 | | 7 | 5 | | 8 | 8 | | 9 | 4 | | 10 | 8 | | 11 | 3 | | 12 | 3 | | 13 | 11 | | 14 | 7 | | 15 | 39 | | 16 | 18 | | 17 | 18 | | 18 | 7 | | 19 | 5 | | 20 | 18 | | 21 | 3 | | 22 | 13 | | 23 | 11 | | 24 | 6 | | 25 | 11 | | 26 | 3 | | 27 | 4 | | 28 | 2 | | 29 | 1 | | 30 | 16 | | 31 | 21 | | 32 | 16 | | 33 | 4 | | 34 | 10 | | 35 | 7 | | 36 | 6 | | 37 | 12 | | 38 | 23 | | 39 | 16 | | 40 | 3 | | 41 | 2 | | 42 | 14 | | 43 | 7 | | 44 | 5 | | 45 | 11 | | 46 | 2 | | 47 | 27 | | 48 | 17 | | 49 | 14 |
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| 70.19% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4444444444444444 | | totalSentences | 180 | | uniqueOpeners | 80 | |
| 52.49% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 127 | | matches | | 0 | "Somewhere above, a window slammed." | | 1 | "Instead she set her boot" |
| | ratio | 0.016 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 127 | | matches | | 0 | "He cut left off Camden" | | 1 | "Her voice crossed the alley" | | 2 | "He didn’t look back." | | 3 | "She stepped through it without" | | 4 | "Her worn leather watch had" | | 5 | "It swallowed her shoes to" | | 6 | "She swept her coat aside" | | 7 | "He paused under a fire" | | 8 | "She had carried silence for" | | 9 | "It weighed more than a" | | 10 | "It moves when the moon" | | 11 | "She put everything into it:" | | 12 | "He spun at the mouth" | | 13 | "He lifted the bag an" | | 14 | "He touched the scar under" | | 15 | "His face changed." | | 16 | "He produced a pale token" | | 17 | "She thumbed it dead before" | | 18 | "He tilted his head, kind" | | 19 | "She had found salt in" |
| | ratio | 0.26 | |
| 66.30% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 100 | | totalSentences | 127 | | matches | | 0 | "Rain needled the Camden pavement" | | 1 | "Detective Harlow Quinn kept her" | | 2 | "Tomás Herrera moved like someone" | | 3 | "He cut left off Camden" | | 4 | "The Saint Christopher medallion bounced" | | 5 | "Her voice crossed the alley" | | 6 | "He didn’t look back." | | 7 | "A delivery moped snarled past," | | 8 | "She stepped through it without" | | 9 | "Her worn leather watch had" | | 10 | "Herrera vaulted a chained gate," | | 11 | "Quinn hit the gate hard" | | 12 | "It swallowed her shoes to" | | 13 | "She swept her coat aside" | | 14 | "He paused under a fire" | | 15 | "The name knocked the alley" | | 16 | "Quinn came off the wall" | | 17 | "Quinn drove after him, every" | | 18 | "Morris had left blood on" | | 19 | "The file said misadventure." |
| | ratio | 0.787 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 127 | | matches | (empty) | | ratio | 0 | |
| 90.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 4 | | matches | | 0 | "A sign promising redevelopment by men who washed money through gyms." | | 1 | "The detective in her, the part decorated by men who had never missed a partner, catalogued exits: one stairwell, one lift shaft dead since the forties, one serv…" | | 2 | "Old adverts watched from behind grime: Bovril, Brylcreem, a woman smiling at a husband who had not come home." | | 3 | "Downstairs waited a market that moved under the full moon and sold forbidden things to people who no longer had legal names." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 45 | | tagDensity | 0.022 | | leniency | 0.044 | | rawRatio | 0 | | effectiveRatio | 0 | |