| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 90.01% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1501 | | totalAiIsmAdverbs | 3 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | |
| | highlights | | 0 | "slowly" | | 1 | "deliberately" | | 2 | "very" |
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| 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) | |
| 70.02% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1501 | | totalAiIsms | 9 | | found | | 0 | | | 1 | | word | "the last thing" | | count | 1 |
| | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | |
| | highlights | | 0 | "familiar" | | 1 | "the last thing" | | 2 | "weight" | | 3 | "encounter" | | 4 | "pulse" | | 5 | "footsteps" | | 6 | "could feel" | | 7 | "silence" |
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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 | 97 | | matches | | |
| 98.67% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 97 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 98 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 71 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 45 | | totalWords | 1508 | | ratio | 0.03 | | matches | | 0 | "Harlow, there are things in this city that don't file into Form 87B, and one of these days one of them is going to file you." | | 1 | "You can still go back." | | 2 | "I know what you are walking into, and I am praying for you anyway." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 49 | | wordCount | 1504 | | uniqueNames | 25 | | maxNameDensity | 0.47 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "You" | | discoveredNames | | Harlow | 2 | | Quinn | 7 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Met | 1 | | Shaftesbury | 1 | | Raven | 1 | | Nest | 3 | | Dean | 1 | | Street | 2 | | Morris | 7 | | Rotherhithe | 1 | | Whitechapel | 1 | | Holborn | 1 | | Brewer | 1 | | You | 6 | | Metropolitan | 1 | | Police | 1 | | Form | 1 | | Camden | 2 | | Soho | 2 | | Tomás | 2 | | Saint | 1 | | Christopher | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "You" | | 5 | "Tomás" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | 0 | "Charing" | | 1 | "Cross" | | 2 | "Road" | | 3 | "Met" | | 4 | "Dean" | | 5 | "Street" | | 6 | "Rotherhithe" | | 7 | "Brewer" | | 8 | "Form" | | 9 | "Soho" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 45.83% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 72 | | glossingSentenceCount | 3 | | matches | | 0 | "quite name" | | 1 | "sounded like Morris" | | 2 | "sounded like the Morris of two years befor" |
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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 | 1508 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 98 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 48.65 | | std | 38.11 | | cv | 0.784 | | sampleLengths | | 0 | 6 | | 1 | 92 | | 2 | 21 | | 3 | 6 | | 4 | 128 | | 5 | 12 | | 6 | 87 | | 7 | 79 | | 8 | 90 | | 9 | 42 | | 10 | 38 | | 11 | 2 | | 12 | 81 | | 13 | 19 | | 14 | 63 | | 15 | 93 | | 16 | 5 | | 17 | 63 | | 18 | 76 | | 19 | 6 | | 20 | 129 | | 21 | 21 | | 22 | 14 | | 23 | 58 | | 24 | 69 | | 25 | 10 | | 26 | 70 | | 27 | 4 | | 28 | 22 | | 29 | 85 | | 30 | 17 |
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| 90.79% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 97 | | matches | | 0 | "was supposed" | | 1 | "been oiled" | | 2 | "been closed" | | 3 | "were tiled" | | 4 | "been worn" |
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| 53.80% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 5 | | totalVerbs | 228 | | matches | | 0 | "was doing" | | 1 | "was giving" | | 2 | "was heading" | | 3 | "wasn't drinking" | | 4 | "were fading" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 98 | | ratio | 0.061 | | matches | | 0 | "Her boots struck the wet pavement and the city swallowed her in sound — a taxi horn somewhere on Shaftesbury, the sullen rush of stormwater along the kerb, the distant thrum of a bass line bleeding out of a basement club." | | 1 | "Her mind was doing the thing it always did during a chase — splitting into two clean, parallel tracks." | | 2 | "She had built her career on a clean world — cause and effect, motive and means, the solid scaffolding of a justice system that held." | | 3 | "She remembered a brief, chance encounter a month back — a man with olive skin and a scar running along his left forearm, sitting in the back booth of the Nest with a glass of water he wasn't drinking." | | 4 | "She could feel it the way a sailor feels weather — a pressure, a change, a wrongness in the bones of the place." | | 5 | "A voice — her own, or something that had learned to sound like her own — said, *You can still go back.*" |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 224 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 8 | | adverbRatio | 0.03571428571428571 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 98 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 98 | | mean | 15.39 | | std | 13.21 | | cv | 0.859 | | sampleLengths | | 0 | 6 | | 1 | 30 | | 2 | 31 | | 3 | 31 | | 4 | 12 | | 5 | 1 | | 6 | 8 | | 7 | 6 | | 8 | 41 | | 9 | 25 | | 10 | 29 | | 11 | 3 | | 12 | 2 | | 13 | 28 | | 14 | 6 | | 15 | 6 | | 16 | 13 | | 17 | 4 | | 18 | 19 | | 19 | 9 | | 20 | 42 | | 21 | 6 | | 22 | 39 | | 23 | 25 | | 24 | 9 | | 25 | 12 | | 26 | 22 | | 27 | 44 | | 28 | 6 | | 29 | 3 | | 30 | 3 | | 31 | 21 | | 32 | 16 | | 33 | 3 | | 34 | 2 | | 35 | 18 | | 36 | 3 | | 37 | 3 | | 38 | 14 | | 39 | 2 | | 40 | 18 | | 41 | 6 | | 42 | 20 | | 43 | 19 | | 44 | 2 | | 45 | 2 | | 46 | 14 | | 47 | 19 | | 48 | 13 | | 49 | 21 |
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| 34.35% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 18 | | diversityRatio | 0.3163265306122449 | | totalSentences | 98 | | uniqueOpeners | 31 | |
| 75.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 88 | | matches | | 0 | "Too fast for a man" | | 1 | "Somewhere beneath the street, something" |
| | ratio | 0.023 | |
| 65.45% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 34 | | totalSentences | 88 | | matches | | 0 | "Her breath came in short," | | 1 | "She counted to three and" | | 2 | "Her boots struck the wet" | | 3 | "She had questions about that" | | 4 | "Her mind was doing the" | | 5 | "She had built her career" | | 6 | "She'd tailed him from a" | | 7 | "She'd learned what to look" | | 8 | "He turned down a narrow" | | 9 | "Her hand found the cold" | | 10 | "She didn't draw." | | 11 | "She stood in the rain" | | 12 | "You do not descend into" | | 13 | "You call it in." | | 14 | "You hold the perimeter." | | 15 | "You wait for armed response" | | 16 | "She could still feel the" | | 17 | "She looked down into the" | | 18 | "She remembered a brief, chance" | | 19 | "He had looked at her" |
| | ratio | 0.386 | |
| 22.50% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 88 | | matches | | 0 | "The rain hit like a" | | 1 | "Detective Harlow Quinn pressed herself" | | 2 | "Her breath came in short," | | 3 | "The leather of her watch" | | 4 | "She counted to three and" | | 5 | "Her boots struck the wet" | | 6 | "Soho at quarter past eleven" | | 7 | "The neon bled pink and" | | 8 | "The Raven's Nest." | | 9 | "The one with the old" | | 10 | "She had questions about that" | | 11 | "Questions she hadn't yet written" | | 12 | "The suspect cut left onto" | | 13 | "Quinn lengthened her stride." | | 14 | "Her mind was doing the" | | 15 | "The first was operational: target," | | 16 | "The second was the one" | | 17 | "Something was wrong in this" | | 18 | "Something threaded through the cases" | | 19 | "She had built her career" |
| | ratio | 0.875 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 88 | | matches | (empty) | | ratio | 0 | |
| 55.14% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 7 | | matches | | 0 | "Detective Harlow Quinn pressed herself flat against the wet brick of the Charing Cross Road alley, her shoulder blades grinding into graffiti that had long sinc…" | | 1 | "The one with the old maps and the black-and-white photographs, the one with the bookshelf at the back that didn't open the way a bookshelf was supposed to." | | 2 | "Something threaded through the cases she couldn't close, the witnesses who changed their stories in ways that had nothing to do with fear, the bodies that arriv…" | | 3 | "She had built her career on a clean world — cause and effect, motive and means, the solid scaffolding of a justice system that held." | | 4 | "The suspect's footsteps were fading now into a sound that was less like footsteps and more like the city itself shifting, rearranging itself to make room for so…" | | 5 | "Her boots found each step by instinct, her hand trailing along the rail, and the rain above her faded into a hush, and then a silence, and then a sound that was…" | | 6 | "She thought of Tomás and his Saint Christopher and the look in his eyes that said *I know what you are walking into, and I am praying for you anyway.* She thoug…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 0 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |