| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.5 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1515 | | 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) | |
| 63.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1515 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "echoed" | | 1 | "constructed" | | 2 | "absolutely" | | 3 | "scanning" | | 4 | "pulse" | | 5 | "silence" | | 6 | "weight" | | 7 | "gleaming" | | 8 | "footsteps" | | 9 | "pulsed" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 146 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 146 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 153 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 46 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1515 | | ratio | 0 | | matches | (empty) | |
| 97.22% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 1 | | matches | | 0 | "Eight metres down, she guessed, based on the time it took and the pressure change in her ears." |
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| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 41 | | wordCount | 1461 | | uniqueNames | 22 | | maxNameDensity | 0.75 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Herrera" | | discoveredNames | | Camden | 1 | | High | 1 | | Street | 1 | | London | 1 | | Gilgamesh | 1 | | Static | 1 | | Chalk | 1 | | Farm | 1 | | Road | 1 | | Morris | 2 | | Rotherhithe | 1 | | Raven | 1 | | Nest | 1 | | Silas | 1 | | Tube | 2 | | Metropolitan | 1 | | Police | 1 | | Commissioner | 1 | | Quinn | 11 | | Didn | 2 | | Christopher | 1 | | Herrera | 7 |
| | persons | | 0 | "Static" | | 1 | "Morris" | | 2 | "Raven" | | 3 | "Silas" | | 4 | "Quinn" | | 5 | "Christopher" | | 6 | "Herrera" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "London" | | 4 | "Gilgamesh" | | 5 | "Chalk" | | 6 | "Farm" | | 7 | "Road" | | 8 | "Rotherhithe" | | 9 | "Commissioner" |
| | globalScore | 1 | | windowScore | 0.833 | |
| 65.73% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 89 | | glossingSentenceCount | 3 | | matches | | 0 | "looked like this" | | 1 | "looked like a taxidermied crow, except th" | | 2 | "smelled like the surface" |
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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 | 1515 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 153 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 53 | | mean | 28.58 | | std | 24.2 | | cv | 0.847 | | sampleLengths | | 0 | 22 | | 1 | 57 | | 2 | 58 | | 3 | 1 | | 4 | 54 | | 5 | 6 | | 6 | 55 | | 7 | 18 | | 8 | 3 | | 9 | 37 | | 10 | 41 | | 11 | 5 | | 12 | 32 | | 13 | 6 | | 14 | 69 | | 15 | 31 | | 16 | 7 | | 17 | 49 | | 18 | 65 | | 19 | 72 | | 20 | 8 | | 21 | 65 | | 22 | 5 | | 23 | 81 | | 24 | 1 | | 25 | 24 | | 26 | 1 | | 27 | 12 | | 28 | 26 | | 29 | 10 | | 30 | 7 | | 31 | 21 | | 32 | 57 | | 33 | 4 | | 34 | 78 | | 35 | 3 | | 36 | 59 | | 37 | 33 | | 38 | 3 | | 39 | 29 | | 40 | 1 | | 41 | 2 | | 42 | 49 | | 43 | 15 | | 44 | 10 | | 45 | 57 | | 46 | 45 | | 47 | 9 | | 48 | 47 | | 49 | 10 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 146 | | matches | | |
| 93.76% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 251 | | matches | | 0 | "was heading" | | 1 | "was crossing" | | 2 | "was talking" | | 3 | "were watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 153 | | ratio | 0.007 | | matches | | 0 | "The tracks here weren't boarded over; she could see the old rails gleaming dully, flanked by maintenance alcoves carved into the walls at regular intervals." |
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| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1470 | | adjectiveStacks | 1 | | stackExamples | | 0 | "faint blue light pulsed" |
| | adverbCount | 28 | | adverbRatio | 0.01904761904761905 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0027210884353741495 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 153 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 153 | | mean | 9.9 | | std | 8.84 | | cv | 0.892 | | sampleLengths | | 0 | 10 | | 1 | 12 | | 2 | 19 | | 3 | 4 | | 4 | 31 | | 5 | 3 | | 6 | 12 | | 7 | 2 | | 8 | 5 | | 9 | 2 | | 10 | 27 | | 11 | 10 | | 12 | 1 | | 13 | 13 | | 14 | 14 | | 15 | 27 | | 16 | 2 | | 17 | 2 | | 18 | 2 | | 19 | 5 | | 20 | 16 | | 21 | 6 | | 22 | 6 | | 23 | 3 | | 24 | 6 | | 25 | 13 | | 26 | 6 | | 27 | 4 | | 28 | 4 | | 29 | 4 | | 30 | 3 | | 31 | 6 | | 32 | 12 | | 33 | 10 | | 34 | 9 | | 35 | 14 | | 36 | 5 | | 37 | 4 | | 38 | 5 | | 39 | 7 | | 40 | 6 | | 41 | 5 | | 42 | 6 | | 43 | 19 | | 44 | 4 | | 45 | 1 | | 46 | 1 | | 47 | 1 | | 48 | 6 | | 49 | 11 |
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| 62.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.43790849673202614 | | totalSentences | 153 | | uniqueOpeners | 67 | |
| 53.76% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 124 | | matches | | 0 | "Then his warrant card." | | 1 | "Too fast for innocent." |
| | ratio | 0.016 | |
| 81.29% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 43 | | totalSentences | 124 | | matches | | 0 | "She caught herself on a" | | 1 | "She marked the turn." | | 2 | "She had him." | | 3 | "she said into her radio" | | 4 | "She thumbed the channel selector." | | 5 | "She smacked the unit against" | | 6 | "Her torch beam cut the" | | 7 | "She slowed to a walk." | | 8 | "Her torch swept left, right," | | 9 | "She'd watched him turn in" | | 10 | "He hadn't doubled back." | | 11 | "He hadn't climbed out." | | 12 | "She found it behind the" | | 13 | "She knelt and hooked her" | | 14 | "They'd found his radio first." | | 15 | "Her therapist lied for a" | | 16 | "She pulled the hatch open." | | 17 | "Her radio crackled once and" | | 18 | "She'd told no one where" | | 19 | "She'd been staking out the" |
| | ratio | 0.347 | |
| 52.74% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 101 | | totalSentences | 124 | | matches | | 0 | "Quinn's shoe hit a puddle" | | 1 | "She caught herself on a" | | 2 | "She marked the turn." | | 3 | "Years of chasing suspects through" | | 4 | "She had him." | | 5 | "she said into her radio" | | 6 | "She thumbed the channel selector." | | 7 | "The rain had been hammering" | | 8 | "She smacked the unit against" | | 9 | "The alley mouth gaped between" | | 10 | "Rainwater sluiced down from a" | | 11 | "Her torch beam cut the" | | 12 | "She slowed to a walk." | | 13 | "The click echoed off the" | | 14 | "Her torch swept left, right," | | 15 | "The skip sat too far" | | 16 | "She'd watched him turn in" | | 17 | "He hadn't doubled back." | | 18 | "He hadn't climbed out." | | 19 | "She found it behind the" |
| | ratio | 0.815 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 124 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 52 | | technicalSentenceCount | 3 | | matches | | 0 | "She'd been staking out the Raven's Nest on her own time, personal surveillance that would never hold up as evidence but might confirm what six months of officia…" | | 1 | "Her suspect had vanished into this impossible bazaar and the smart play, the by-the-book play, was to climb back up that ladder, drive to the station, and file …" | | 2 | "She kept her head low and her eyes moving, scanning for the dark hoodie, the build, the gait." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 0 | | effectiveRatio | 0 | |