| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said quietly [quietly]" |
| | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.25 | | effectiveRatio | 0.222 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1096 | | 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) | |
| 86.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1096 | | totalAiIsms | 3 | | found | | | highlights | | 0 | "pulse" | | 1 | "shattered" | | 2 | "footsteps" |
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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 | 94 | | matches | (empty) | |
| 97.26% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 94 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1091 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 92.20% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 43 | | wordCount | 1038 | | uniqueNames | 17 | | maxNameDensity | 1.16 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Soho | 1 | | Quinn | 2 | | Raven | 1 | | Nest | 1 | | Tomás | 8 | | Herrera | 3 | | Saint | 1 | | Christopher | 1 | | Camden | 2 | | Harlow | 12 | | Tube | 2 | | Veil | 1 | | Market | 1 | | Bethnal | 2 | | Green | 2 | | Morris | 2 | | London | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Tomás" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Harlow" | | 6 | "Market" | | 7 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Raven" | | 2 | "Camden" | | 3 | "Bethnal" | | 4 | "Green" | | 5 | "London" |
| | globalScore | 0.922 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 64 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.917 | | wordCount | 1091 | | matches | | 0 | "not afraid, but surprised" |
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| 99.33% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 99 | | matches | | 0 | "seen that mark" | | 1 | "understand that much" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 30 | | mean | 36.37 | | std | 31.19 | | cv | 0.858 | | sampleLengths | | 0 | 74 | | 1 | 78 | | 2 | 17 | | 3 | 2 | | 4 | 57 | | 5 | 41 | | 6 | 21 | | 7 | 6 | | 8 | 57 | | 9 | 2 | | 10 | 109 | | 11 | 6 | | 12 | 39 | | 13 | 3 | | 14 | 109 | | 15 | 72 | | 16 | 4 | | 17 | 12 | | 18 | 42 | | 19 | 62 | | 20 | 18 | | 21 | 15 | | 22 | 10 | | 23 | 18 | | 24 | 53 | | 25 | 63 | | 26 | 61 | | 27 | 17 | | 28 | 7 | | 29 | 16 |
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| 82.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 94 | | matches | | 0 | "been shut" | | 1 | "been peeled" | | 2 | "was strained" | | 3 | "were chained" | | 4 | "been scratched" | | 5 | "been open" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 181 | | matches | | |
| 56.28% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 4 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 99 | | ratio | 0.03 | | matches | | 0 | "Harlow knew this area—an old Tube station had been shut here years earlier." | | 1 | "He held the door for half a second too long—long enough for her to see the pale disc in his other hand, carved from bone—and then he let it swing shut." | | 2 | "Above the tunnel mouth, someone had painted a sigil in white—a circle with a vertical slash through it." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1046 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.033460803059273424 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.004780114722753346 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 11.02 | | std | 7.09 | | cv | 0.643 | | sampleLengths | | 0 | 16 | | 1 | 26 | | 2 | 11 | | 3 | 16 | | 4 | 5 | | 5 | 19 | | 6 | 10 | | 7 | 25 | | 8 | 12 | | 9 | 12 | | 10 | 5 | | 11 | 12 | | 12 | 2 | | 13 | 5 | | 14 | 24 | | 15 | 20 | | 16 | 4 | | 17 | 4 | | 18 | 21 | | 19 | 13 | | 20 | 7 | | 21 | 5 | | 22 | 16 | | 23 | 6 | | 24 | 16 | | 25 | 16 | | 26 | 5 | | 27 | 20 | | 28 | 2 | | 29 | 4 | | 30 | 18 | | 31 | 4 | | 32 | 13 | | 33 | 9 | | 34 | 16 | | 35 | 14 | | 36 | 31 | | 37 | 6 | | 38 | 9 | | 39 | 15 | | 40 | 15 | | 41 | 3 | | 42 | 18 | | 43 | 16 | | 44 | 9 | | 45 | 11 | | 46 | 5 | | 47 | 30 | | 48 | 9 | | 49 | 11 |
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| 46.46% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.30303030303030304 | | totalSentences | 99 | | uniqueOpeners | 30 | |
| 76.63% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 87 | | matches | | 0 | "Then he began walking north" | | 1 | "Then Harlow Quinn stepped into" |
| | ratio | 0.023 | |
| 77.47% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 87 | | matches | | 0 | "She had been watching Tomás" | | 1 | "He came out of the" | | 2 | "He had olive skin, short" | | 3 | "He looked right, then left." | | 4 | "He changed buses twice, doubled" | | 5 | "She was already reaching for" | | 6 | "She was forty metres behind." | | 7 | "He crossed the dead concourse," | | 8 | "It smelled of standing water" | | 9 | "She took the steps two" | | 10 | "He held the door for" | | 11 | "It resisted like a living" | | 12 | "She stumbled through and found" | | 13 | "She didn’t know the name" | | 14 | "Her left hand drifted to" | | 15 | "She could see his medallion" | | 16 | "She pushed after him, past" | | 17 | "She knocked the jar aside." | | 18 | "It shattered and the boy" | | 19 | "She had seen that mark" |
| | ratio | 0.356 | |
| 46.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 87 | | matches | | 0 | "The rain had been falling" | | 1 | "Harlow Quinn stood outside the" | | 2 | "The green sign above the" | | 3 | "She had been watching Tomás" | | 4 | "That night he made it." | | 5 | "He came out of the" | | 6 | "The flame caught the Saint" | | 7 | "He had olive skin, short" | | 8 | "Harlow noted it all without" | | 9 | "The worn leather watch on" | | 10 | "He looked right, then left." | | 11 | "The tail took ninety minutes." | | 12 | "He changed buses twice, doubled" | | 13 | "The city grew quieter." | | 14 | "The rain kept coming." | | 15 | "Harlow knew this area—an old" | | 16 | "She was already reaching for" | | 17 | "The radio crackled and returned" | | 18 | "Tomás scrambled over a section" | | 19 | "Harlow went after him, landing" |
| | ratio | 0.828 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 87 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 2 | | matches | | 0 | "Tomás scrambled over a section of chain-link fence that had been peeled back like a bandage." | | 1 | "She squared her shoulders with the military carriage that had never left her." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.25 | | effectiveRatio | 0.222 | |