| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1474 | | 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) | |
| 69.47% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1474 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "flicked" | | 1 | "measured" | | 2 | "shattered" | | 3 | "stomach" | | 4 | "velvet" | | 5 | "pulse" | | 6 | "weight" | | 7 | "echoed" | | 8 | "gloom" |
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| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "stomach dropped/sank" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "stomach knotted" | | 1 | "hung thick in the air" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 104 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 104 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 117 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1474 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 88.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 60 | | wordCount | 1371 | | uniqueNames | 28 | | maxNameDensity | 1.24 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Harlow | 17 | | Tomás | 12 | | Herrera | 2 | | Met | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Turkish | 1 | | Victorian | 1 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Northern | 2 | | Line | 2 | | Rotherhithe | 2 | | Morris | 2 | | Whitehall | 1 | | Kentish | 1 | | Town | 1 | | London | 1 | | West | 1 | | End | 1 | | Savile | 1 | | Row | 1 | | Metropolitan | 1 | | Police | 1 | | Trojan | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Met" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Raven" | | 7 | "Nest" | | 8 | "Line" | | 9 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Whitehall" | | 4 | "Kentish" | | 5 | "Town" | | 6 | "London" | | 7 | "West" | | 8 | "End" |
| | globalScore | 0.88 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | 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 | 1474 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 117 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 24.16 | | std | 19.05 | | cv | 0.788 | | sampleLengths | | 0 | 15 | | 1 | 23 | | 2 | 35 | | 3 | 58 | | 4 | 71 | | 5 | 15 | | 6 | 13 | | 7 | 36 | | 8 | 25 | | 9 | 48 | | 10 | 7 | | 11 | 2 | | 12 | 11 | | 13 | 22 | | 14 | 50 | | 15 | 10 | | 16 | 4 | | 17 | 22 | | 18 | 8 | | 19 | 4 | | 20 | 8 | | 21 | 10 | | 22 | 2 | | 23 | 61 | | 24 | 14 | | 25 | 2 | | 26 | 23 | | 27 | 10 | | 28 | 45 | | 29 | 8 | | 30 | 40 | | 31 | 42 | | 32 | 22 | | 33 | 3 | | 34 | 18 | | 35 | 46 | | 36 | 15 | | 37 | 81 | | 38 | 16 | | 39 | 12 | | 40 | 63 | | 41 | 40 | | 42 | 21 | | 43 | 30 | | 44 | 34 | | 45 | 43 | | 46 | 31 | | 47 | 14 | | 48 | 31 | | 49 | 1 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 104 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 235 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 117 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1386 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.015151515151515152 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004329004329004329 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 117 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 117 | | mean | 12.6 | | std | 6.67 | | cv | 0.529 | | sampleLengths | | 0 | 15 | | 1 | 23 | | 2 | 20 | | 3 | 12 | | 4 | 3 | | 5 | 17 | | 6 | 10 | | 7 | 11 | | 8 | 20 | | 9 | 10 | | 10 | 19 | | 11 | 23 | | 12 | 19 | | 13 | 15 | | 14 | 13 | | 15 | 8 | | 16 | 9 | | 17 | 19 | | 18 | 9 | | 19 | 9 | | 20 | 7 | | 21 | 5 | | 22 | 13 | | 23 | 14 | | 24 | 16 | | 25 | 7 | | 26 | 2 | | 27 | 11 | | 28 | 22 | | 29 | 14 | | 30 | 27 | | 31 | 9 | | 32 | 10 | | 33 | 4 | | 34 | 22 | | 35 | 8 | | 36 | 4 | | 37 | 8 | | 38 | 10 | | 39 | 2 | | 40 | 11 | | 41 | 18 | | 42 | 2 | | 43 | 3 | | 44 | 27 | | 45 | 14 | | 46 | 2 | | 47 | 17 | | 48 | 6 | | 49 | 10 |
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| 98.86% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5982905982905983 | | totalSentences | 117 | | uniqueOpeners | 70 | |
| 66.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 100 | | matches | | 0 | "Somewhere in the dark, stagnant" | | 1 | "Just like Rotherhithe." |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 20 | | totalSentences | 100 | | matches | | 0 | "She vaulted a low brick" | | 1 | "He moved fast for a" | | 2 | "She cleared the gate without" | | 3 | "She drew her steel baton" | | 4 | "His jacket hung open, exposing" | | 5 | "She struck low for his" | | 6 | "He drove his shoulder into" | | 7 | "He slammed the door behind" | | 8 | "It bounced half an inch" | | 9 | "Her stomach knotted." | | 10 | "She pocketed the disc, nudged" | | 11 | "She clicked her radio off," | | 12 | "Her boots beat a sharp" | | 13 | "Her torch beam swept across" | | 14 | "She looked at the carved" | | 15 | "She collapsed her baton, slid" | | 16 | "She stepped out of the" | | 17 | "She pulled the warm disc" | | 18 | "It groaned, rotated, and clicked" | | 19 | "She was inside." |
| | ratio | 0.2 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 62 | | totalSentences | 100 | | matches | | 0 | "Iron screeched against tarmac as" | | 1 | "She vaulted a low brick" | | 2 | "He moved fast for a" | | 3 | "Harlow tucked her chin, drove" | | 4 | "Amateurs sprinted until their lungs" | | 5 | "Professionals ducked into the warren" | | 6 | "Tomás vaulted a chain-link gate" | | 7 | "She cleared the gate without" | | 8 | "Tomás skidded left behind a" | | 9 | "Brick walls rose thirty feet" | | 10 | "Razor wire crowned the canal" | | 11 | "Harlow slowed to a walk," | | 12 | "She drew her steel baton" | | 13 | "The shaft snapped open, solid" | | 14 | "Tomás spun around, chest heaving." | | 15 | "Rain darkened his olive skin" | | 16 | "His jacket hung open, exposing" | | 17 | "Harlow took two measured steps" | | 18 | "An hour ago, she had" | | 19 | "Tomás reached toward the dead" |
| | ratio | 0.62 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 100 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 71 | | technicalSentenceCount | 3 | | matches | | 0 | "Opposite her, behind velvet ropes, bell jars glowed with pale things that twitched in cloudy brine." | | 1 | "Every buyer who approached dropped a pale disc into a tarnished bronze bowl before pushing through the iron bars." | | 2 | "Above the stone archway, a strung curtain of dried finger-bones rattled violently with no wind, unleashing a shrill metallic shriek that echoed down the tunnel …" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |