| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 6 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 9 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 990 | | 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) | |
| 54.55% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 990 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "glistening" | | 1 | "weight" | | 2 | "familiar" | | 3 | "echoing" | | 4 | "footsteps" | | 5 | "glint" | | 6 | "etched" |
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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 | 42 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 42 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 45 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 990 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 940 | | uniqueNames | 16 | | maxNameDensity | 0.74 | | worstName | "Harlow" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Tomás" | | discoveredNames | | Harlow | 7 | | Quinn | 1 | | Dean | 1 | | Street | 1 | | Tomás | 7 | | Herrera | 1 | | Morris | 5 | | Camden | 1 | | London | 1 | | Saint | 2 | | Christopher | 2 | | Tube | 1 | | Veil | 1 | | Market | 1 | | Sandhurst | 1 | | Three | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Saint" | | 6 | "Christopher" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "London" | | 3 | "Market" | | 4 | "Sandhurst" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | 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 | 990 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 45 | | matches | (empty) | |
| 72.79% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 16 | | mean | 61.88 | | std | 25.04 | | cv | 0.405 | | sampleLengths | | 0 | 81 | | 1 | 80 | | 2 | 78 | | 3 | 84 | | 4 | 12 | | 5 | 47 | | 6 | 55 | | 7 | 69 | | 8 | 93 | | 9 | 95 | | 10 | 80 | | 11 | 74 | | 12 | 52 | | 13 | 37 | | 14 | 30 | | 15 | 23 |
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| 88.55% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 42 | | matches | | 0 | "are lined" | | 1 | "being draped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 132 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 950 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 17 | | adverbRatio | 0.017894736842105262 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004210526315789474 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 45 | | echoCount | 0 | | echoWords | (empty) | |
| 70.32% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 45 | | mean | 22 | | std | 7.17 | | cv | 0.326 | | sampleLengths | | 0 | 27 | | 1 | 29 | | 2 | 25 | | 3 | 30 | | 4 | 30 | | 5 | 20 | | 6 | 29 | | 7 | 26 | | 8 | 23 | | 9 | 22 | | 10 | 21 | | 11 | 18 | | 12 | 23 | | 13 | 11 | | 14 | 1 | | 15 | 20 | | 16 | 27 | | 17 | 18 | | 18 | 24 | | 19 | 13 | | 20 | 24 | | 21 | 24 | | 22 | 21 | | 23 | 14 | | 24 | 27 | | 25 | 25 | | 26 | 27 | | 27 | 25 | | 28 | 22 | | 29 | 25 | | 30 | 23 | | 31 | 13 | | 32 | 22 | | 33 | 25 | | 34 | 20 | | 35 | 48 | | 36 | 26 | | 37 | 20 | | 38 | 8 | | 39 | 24 | | 40 | 22 | | 41 | 15 | | 42 | 19 | | 43 | 11 | | 44 | 23 |
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| 54.81% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.35555555555555557 | | totalSentences | 45 | | uniqueOpeners | 16 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 42 | | matches | (empty) | | ratio | 0 | |
| 86.67% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 14 | | totalSentences | 42 | | matches | | 0 | "She’s three paces behind the" | | 1 | "She’s been tracking Tomás Herrera" | | 2 | "She draws her service weapon," | | 3 | "He yanks a leather-bound travel" | | 4 | "he shouts, but he’s already" | | 5 | "She grabs the edge of" | | 6 | "She grips the ladder’s cold" | | 7 | "She counts twelve rungs, same" | | 8 | "She shakes her head, blinking" | | 9 | "He presses the bone to" | | 10 | "he says, his voice shaking" | | 11 | "She remembers Morris’s final radio" | | 12 | "she says, her voice sharp," | | 13 | "She takes another step forward," |
| | ratio | 0.333 | |
| 19.52% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 37 | | totalSentences | 42 | | matches | | 0 | "Rain slices Harlow Quinn’s eyes" | | 1 | "She’s three paces behind the" | | 2 | "A thin, pale scar runs" | | 3 | "She’s been tracking Tomás Herrera" | | 4 | "This is the same kind" | | 5 | "Tomás dives through a back" | | 6 | "She draws her service weapon," | | 7 | "The air inside hits her" | | 8 | "The walls are lined with" | | 9 | "The bartender, a man with" | | 10 | "Harlow barks, nodding at the" | | 11 | "Tomás is halfway across the" | | 12 | "He yanks a leather-bound travel" | | 13 | "he shouts, but he’s already" | | 14 | "Harlow fires a single round" | | 15 | "The bar patrons duck, and" | | 16 | "She grabs the edge of" | | 17 | "The hallway behind the bar" | | 18 | "A single bare bulb hangs" | | 19 | "Tomás’s boots thud on the" |
| | ratio | 0.881 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 42 | | matches | (empty) | | ratio | 0 | |
| 23.81% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 6 | | matches | | 0 | "She’s three paces behind the suspect, the dark curl of his hair bobbing ahead of her, olive skin glistening with rain that has soaked through his cheap wool jac…" | | 1 | "A single bare bulb hangs from the ceiling, its glow flickering as she follows the sound of Tomás’s quickening footsteps ahead." | | 2 | "She counts twelve rungs, same as the ladder outside Morris’s flat, a trivial, unsettling detail that makes her pause for half a second before she pushes onward." | | 3 | "A single dead pigeon lies crumpled on the third rail, its feathers matted with rainwater that has seeped through the concrete ceiling." | | 4 | "The air smells like ozone and burnt sugar, a scent that makes the back of her throat tighten, the taste of copper flooding her mouth." | | 5 | "Harlow steps closer, her flashlight beam cutting through the market’s glow, catching the glint of the Saint Christopher medallion on his chest." |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 6 | | uselessAdditionCount | 3 | | matches | | 0 | "bone she found, marked" | | 1 | "he says, his voice shaking" | | 2 | "She remembers, static crackling over the line, a single word:" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 2 | | fancyTags | | 0 | "Harlow barks (harlow bark)" | | 1 | "he shouts (shout)" |
| | dialogueSentences | 9 | | tagDensity | 0.444 | | leniency | 0.889 | | rawRatio | 0.5 | | effectiveRatio | 0.444 | |