| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | 0 | "DC Patel stepped aside [aside]" |
| | dialogueSentences | 29 | | tagDensity | 0.414 | | leniency | 0.828 | | rawRatio | 0.083 | | effectiveRatio | 0.069 | |
| 77.92% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1132 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "carefully" | | 1 | "very" | | 2 | "perfectly" | | 3 | "lightly" |
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| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 91.17% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1132 | | totalAiIsms | 2 | | found | | | highlights | | |
| 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 | 69 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 69 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 85 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 69 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 1 | | totalWords | 1124 | | ratio | 0.001 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 17 | | wordCount | 827 | | uniqueNames | 6 | | maxNameDensity | 0.85 | | worstName | "Patel" | | maxWindowNameDensity | 2 | | worstWindowName | "Patel" | | discoveredNames | | Harlow | 1 | | Quinn | 3 | | Patel | 7 | | Mass | 1 | | Morris | 4 | | Bowen | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Patel" | | 3 | "Morris" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 33.72% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 43 | | glossingSentenceCount | 2 | | matches | | 0 | "as if waiting for a train that was already decades late" | | 1 | "not quite a handprint, at the sprung catches, at the dry shoes of a man who had come into a wet city by no road she could name" |
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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 | 1124 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 85 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 34.06 | | std | 27.87 | | cv | 0.818 | | sampleLengths | | 0 | 49 | | 1 | 79 | | 2 | 29 | | 3 | 7 | | 4 | 30 | | 5 | 6 | | 6 | 45 | | 7 | 72 | | 8 | 5 | | 9 | 3 | | 10 | 73 | | 11 | 12 | | 12 | 27 | | 13 | 4 | | 14 | 72 | | 15 | 65 | | 16 | 28 | | 17 | 5 | | 18 | 85 | | 19 | 13 | | 20 | 20 | | 21 | 74 | | 22 | 6 | | 23 | 1 | | 24 | 63 | | 25 | 63 | | 26 | 73 | | 27 | 6 | | 28 | 14 | | 29 | 38 | | 30 | 10 | | 31 | 36 | | 32 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 69 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 130 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 1 | | flaggedSentences | 6 | | totalSentences | 85 | | ratio | 0.071 | | matches | | 0 | "Someone had rigged portable lamps on tripods, and their light made hard shadows of everything—the bowed rail of a ticket barrier, the peeling advertisement for a brand of tea nobody drank anymore, and the man on the floor, whose left hand was still curled around what used to be a briefcase handle." | | 1 | "Almost fingers, but longer than fingers, and the thumb—if it was a thumb—was in the wrong place entirely." | | 2 | "His shoes—she crouched again, lifted the hem of his trouser cuff with a pen—were polished but the soles were dry." | | 3 | "The kebab shop's hatch opened onto an alley with two inches of standing water in it; she had waded through it herself coming in." | | 4 | "For an instant—she would swear to it later, alone, in her kitchen with the kettle screaming—the beam seemed to bend." | | 5 | "\"Name, employer, last movements. And Amrit—\" she turned, sharp jaw catching the lamp-light \"—don't let Bowen sign this off as misadventure. Not today. Tell him I'm asking personally.\"" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 813 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 29 | | adverbRatio | 0.03567035670356704 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.01107011070110701 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 85 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 85 | | mean | 13.22 | | std | 12.02 | | cv | 0.909 | | sampleLengths | | 0 | 25 | | 1 | 24 | | 2 | 27 | | 3 | 52 | | 4 | 14 | | 5 | 15 | | 6 | 7 | | 7 | 30 | | 8 | 6 | | 9 | 28 | | 10 | 17 | | 11 | 2 | | 12 | 6 | | 13 | 36 | | 14 | 13 | | 15 | 11 | | 16 | 1 | | 17 | 2 | | 18 | 1 | | 19 | 5 | | 20 | 2 | | 21 | 1 | | 22 | 69 | | 23 | 4 | | 24 | 12 | | 25 | 27 | | 26 | 2 | | 27 | 2 | | 28 | 19 | | 29 | 6 | | 30 | 6 | | 31 | 25 | | 32 | 16 | | 33 | 7 | | 34 | 13 | | 35 | 20 | | 36 | 5 | | 37 | 7 | | 38 | 13 | | 39 | 7 | | 40 | 8 | | 41 | 4 | | 42 | 9 | | 43 | 5 | | 44 | 4 | | 45 | 15 | | 46 | 23 | | 47 | 23 | | 48 | 2 | | 49 | 18 |
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| 89.41% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.5647058823529412 | | totalSentences | 85 | | uniqueOpeners | 48 | |
| 61.73% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 54 | | matches | | 0 | "Almost fingers, but longer than" |
| | ratio | 0.019 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 54 | | matches | | 0 | "Her knees gave their familiar" | | 1 | "She counted the steps between" | | 2 | "She stood, worked her left" | | 3 | "She made a slow circuit" | | 4 | "She had seen that exactly" | | 5 | "He handed it over." | | 6 | "She thumbed it on and" | | 7 | "she called over her shoulder" | | 8 | "She ran the beam lower" | | 9 | "His tie was knotted." | | 10 | "His shoes—she crouched again, lifted" | | 11 | "It had rained all night." | | 12 | "She lowered the cuff." | | 13 | "She straightened and let the" | | 14 | "She thought of Morris." | | 15 | "She thought of Morris for" | | 16 | "she turned, sharp jaw catching" | | 17 | "She looked again at the" |
| | ratio | 0.333 | |
| 80.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 54 | | matches | | 0 | "The tiles stopped halfway up" | | 1 | "Detective Harlow Quinn ducked under" | | 2 | "Someone had rigged portable lamps" | | 3 | "DC Patel stepped aside for" | | 4 | "Patel said, with the careful" | | 5 | "Her knees gave their familiar" | | 6 | "The victim was perhaps fifty," | | 7 | "Blood had pooled under his" | | 8 | "She counted the steps between" | | 9 | "She stood, worked her left" | | 10 | "She made a slow circuit" | | 11 | "That was the first wrong" | | 12 | "The platform had an inch-thick" | | 13 | "The second wrong thing was" | | 14 | "She had seen that exactly" | | 15 | "The metal was cold to" | | 16 | "The third wrong thing was" | | 17 | "Something floral and burnt, like" | | 18 | "He handed it over." | | 19 | "She thumbed it on and" |
| | ratio | 0.759 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 37.04% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 4 | | matches | | 0 | "The platform had an inch-thick carpet of grime everywhere except a clean oval around the victim, as though someone had swept it with a broom." | | 1 | "Or as though something had been set down very carefully and nothing had approached it since." | | 2 | "There was no *or* that she wanted to say out loud in front of Patel, who believed in mortgages and pensions and the straightforward wickedness of men." | | 3 | "She looked again at the handprint that was not quite a handprint, at the sprung catches, at the dry shoes of a man who had come into a wet city by no road she c…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 7 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 29 | | tagDensity | 0.241 | | leniency | 0.483 | | rawRatio | 0 | | effectiveRatio | 0 | |