| 66.67% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 1 | | adverbTags | | 0 | "he said quietly [quietly]" |
| | dialogueSentences | 15 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0.2 | | effectiveRatio | 0.133 | |
| 92.03% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1254 | | totalAiIsmAdverbs | 2 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
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| | 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) | |
| 92.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1254 | | totalAiIsms | 2 | | found | | 0 | | word | "down her spine" | | count | 1 |
| | 1 | |
| | highlights | | 0 | "down her spine" | | 1 | "warmth" |
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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 | 1 | | narrationSentences | 105 | | matches | | |
| 74.83% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 3 | | narrationSentences | 105 | | 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 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1254 | | ratio | 0.002 | | matches | | 0 | "unexplained circumstances" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1164 | | uniqueNames | 18 | | maxNameDensity | 0.52 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Morris" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | Herrera | 4 | | Saint | 1 | | Christopher | 1 | | Raven | 1 | | Nest | 1 | | Northern | 1 | | Mornington | 1 | | Crescent | 1 | | Met | 1 | | Victorian | 1 | | Tube | 1 | | London | 1 | | Quinn | 6 | | Morris | 3 | | Bermondsey | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Saint" | | 2 | "Christopher" | | 3 | "Raven" | | 4 | "Nest" | | 5 | "Quinn" | | 6 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Mornington" | | 4 | "Crescent" | | 5 | "Victorian" | | 6 | "London" | | 7 | "Bermondsey" |
| | globalScore | 1 | | windowScore | 1 | |
| 81.51% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 73 | | glossingSentenceCount | 2 | | matches | | 0 | "something like struck matches, something lik" | | 1 | "something like copper" | | 2 | "felt like pushing into warm water, a re" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1254 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 2 | | totalSentences | 114 | | matches | | 0 | "watched that footage" | | 1 | "know that the" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 41 | | mean | 30.59 | | std | 23.9 | | cv | 0.781 | | sampleLengths | | 0 | 40 | | 1 | 46 | | 2 | 68 | | 3 | 10 | | 4 | 6 | | 5 | 65 | | 6 | 41 | | 7 | 19 | | 8 | 14 | | 9 | 16 | | 10 | 36 | | 11 | 70 | | 12 | 13 | | 13 | 43 | | 14 | 29 | | 15 | 21 | | 16 | 66 | | 17 | 28 | | 18 | 36 | | 19 | 5 | | 20 | 9 | | 21 | 1 | | 22 | 4 | | 23 | 16 | | 24 | 20 | | 25 | 79 | | 26 | 8 | | 27 | 99 | | 28 | 23 | | 29 | 3 | | 30 | 47 | | 31 | 1 | | 32 | 37 | | 33 | 53 | | 34 | 21 | | 35 | 64 | | 36 | 32 | | 37 | 13 | | 38 | 8 | | 39 | 28 | | 40 | 16 |
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| 91.90% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 105 | | matches | | 0 | "been trained" | | 1 | "was gone" | | 2 | "were plastered" | | 3 | "been caught" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 7 | | totalVerbs | 196 | | matches | | 0 | "was crossing" | | 1 | "was not, technically, wearing" | | 2 | "was fumbling" | | 3 | "was tailing" | | 4 | "was shouting" | | 5 | "was singing" | | 6 | "was gripping" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 114 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1171 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 36 | | adverbRatio | 0.030742954739538857 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005977796754910333 | |
| 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 | 11 | | std | 7.65 | | cv | 0.696 | | sampleLengths | | 0 | 16 | | 1 | 24 | | 2 | 13 | | 3 | 15 | | 4 | 18 | | 5 | 7 | | 6 | 29 | | 7 | 7 | | 8 | 16 | | 9 | 9 | | 10 | 8 | | 11 | 2 | | 12 | 2 | | 13 | 4 | | 14 | 16 | | 15 | 22 | | 16 | 5 | | 17 | 10 | | 18 | 12 | | 19 | 6 | | 20 | 9 | | 21 | 26 | | 22 | 14 | | 23 | 5 | | 24 | 7 | | 25 | 7 | | 26 | 16 | | 27 | 4 | | 28 | 20 | | 29 | 8 | | 30 | 4 | | 31 | 39 | | 32 | 9 | | 33 | 4 | | 34 | 18 | | 35 | 3 | | 36 | 10 | | 37 | 22 | | 38 | 3 | | 39 | 2 | | 40 | 2 | | 41 | 14 | | 42 | 10 | | 43 | 12 | | 44 | 7 | | 45 | 7 | | 46 | 14 | | 47 | 10 | | 48 | 12 | | 49 | 13 |
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| 62.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4298245614035088 | | totalSentences | 114 | | uniqueOpeners | 49 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 92 | | matches | | 0 | "Somewhere between Mornington Crescent and" | | 1 | "Of course he didn't." | | 2 | "Then he was through a" | | 3 | "Then she crossed the yard" | | 4 | "Then it let go with" |
| | ratio | 0.054 | |
| 72.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 92 | | matches | | 0 | "He cut between the late-night" | | 1 | "She had followed him for" | | 2 | "He had been careful all" | | 3 | "Her voice cracked off the" | | 4 | "He swung left at the" | | 5 | "She'd run down muggers, arsonists," | | 6 | "She was not about to" | | 7 | "She used the name deliberately," | | 8 | "His laugh came back thin" | | 9 | "She'd seen the scar on" | | 10 | "He faltered for half a" | | 11 | "She gained five yards." | | 12 | "Its tiled front was the" | | 13 | "Her breath smoked and the" | | 14 | "He was fumbling something out" | | 15 | "He pressed it flat against" | | 16 | "She saw it plainly for" | | 17 | "He looked exhausted, and terrified," | | 18 | "He looked sorry." | | 19 | "His accent, thinned by years" |
| | ratio | 0.37 | |
| 68.70% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 72 | | totalSentences | 92 | | matches | | 0 | "Quinn ran through it with" | | 1 | "Tomás Herrera ran well for" | | 2 | "He cut between the late-night" | | 3 | "The Saint Christopher medallion at" | | 4 | "She had followed him for" | | 5 | "He had been careful all" | | 6 | "That interested her more than" | | 7 | "Her voice cracked off the" | | 8 | "He swung left at the" | | 9 | "Quinn took them two at" | | 10 | "She'd run down muggers, arsonists," | | 11 | "She was not about to" | | 12 | "The towpath was black and" | | 13 | "Water dripped from the arches" | | 14 | "A cyclist's lamp swung past" | | 15 | "She used the name deliberately," | | 16 | "His laugh came back thin" | | 17 | "That was a gamble." | | 18 | "She'd seen the scar on" | | 19 | "He faltered for half a" |
| | ratio | 0.783 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 92 | | matches | | 0 | "If she called it in," | | 1 | "If she went in, no" |
| | ratio | 0.022 | |
| 90.91% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 4 | | matches | | 0 | "Quinn ran through it with her coat flapping open and her lungs burning, eyes fixed on the dark curly head bobbing thirty yards ahead." | | 1 | "Tomás Herrera ran well for a man who fixed people for a living." | | 2 | "Out of the Raven's Nest, past the green neon that buzzed and stuttered above the door, then onto the Northern line with a newspaper held up like an amateur." | | 3 | "It felt like pushing into warm water, a resistance that gave grudgingly, and for one awful moment she thought it would hold her, pin her half in and half out." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |