| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.357 | | leniency | 0.714 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1081 | | 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) | |
| 76.87% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1081 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "fractured" | | 1 | "footfall" | | 2 | "pulsed" | | 3 | "could feel" | | 4 | "pulse" |
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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 | 93 | | matches | | |
| 96.77% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 93 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 101 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 42 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 1 | | markdownWords | 2 | | totalWords | 1083 | | ratio | 0.002 | | matches | | |
| 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 | 40 | | wordCount | 990 | | uniqueNames | 19 | | maxNameDensity | 0.81 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Morris" | | discoveredNames | | Camden | 2 | | Tomás | 1 | | Herrera | 6 | | Silas | 1 | | Raven | 1 | | Nest | 1 | | Morris | 5 | | Saint | 1 | | Christopher | 1 | | High | 1 | | Street | 1 | | Tube | 1 | | Rain | 3 | | Bitter | 1 | | Isle | 1 | | Dogs | 1 | | Services | 1 | | Quinn | 8 | | Unclear | 3 |
| | persons | | 0 | "Tomás" | | 1 | "Herrera" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Rain" | | 7 | "Services" | | 8 | "Quinn" |
| | places | | 0 | "Camden" | | 1 | "Silas" | | 2 | "High" | | 3 | "Street" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 61 | | glossingSentenceCount | 1 | | matches | | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1083 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 101 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 33 | | mean | 32.82 | | std | 26.98 | | cv | 0.822 | | sampleLengths | | 0 | 67 | | 1 | 5 | | 2 | 17 | | 3 | 72 | | 4 | 47 | | 5 | 11 | | 6 | 32 | | 7 | 51 | | 8 | 68 | | 9 | 67 | | 10 | 47 | | 11 | 3 | | 12 | 69 | | 13 | 12 | | 14 | 108 | | 15 | 19 | | 16 | 26 | | 17 | 3 | | 18 | 15 | | 19 | 38 | | 20 | 19 | | 21 | 7 | | 22 | 15 | | 23 | 9 | | 24 | 75 | | 25 | 6 | | 26 | 48 | | 27 | 12 | | 28 | 40 | | 29 | 44 | | 30 | 25 | | 31 | 3 | | 32 | 3 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 82.35% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 170 | | matches | | 0 | "wasn't heading" | | 1 | "was heading" | | 2 | "wasn't bothering" |
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| 86.28% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 2 | | totalSentences | 101 | | ratio | 0.02 | | matches | | 0 | "She knew every inch of this patch; she'd walked it a hundred times looking for a lead on Morris." | | 1 | "Get the address logged, get a van on standby, don't — under any circumstances — pursue an unknown suspect into an unmapped subterranean location without backup." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 992 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.02217741935483871 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 101 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 101 | | mean | 10.72 | | std | 8.43 | | cv | 0.786 | | sampleLengths | | 0 | 18 | | 1 | 15 | | 2 | 34 | | 3 | 5 | | 4 | 9 | | 5 | 3 | | 6 | 5 | | 7 | 5 | | 8 | 35 | | 9 | 2 | | 10 | 2 | | 11 | 28 | | 12 | 10 | | 13 | 25 | | 14 | 12 | | 15 | 11 | | 16 | 3 | | 17 | 21 | | 18 | 8 | | 19 | 18 | | 20 | 2 | | 21 | 19 | | 22 | 6 | | 23 | 6 | | 24 | 7 | | 25 | 25 | | 26 | 9 | | 27 | 27 | | 28 | 42 | | 29 | 9 | | 30 | 16 | | 31 | 4 | | 32 | 3 | | 33 | 15 | | 34 | 13 | | 35 | 12 | | 36 | 3 | | 37 | 31 | | 38 | 17 | | 39 | 4 | | 40 | 17 | | 41 | 6 | | 42 | 6 | | 43 | 15 | | 44 | 6 | | 45 | 19 | | 46 | 18 | | 47 | 11 | | 48 | 13 | | 49 | 7 |
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| 68.32% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.44554455445544555 | | totalSentences | 101 | | uniqueOpeners | 45 | |
| 82.30% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 81 | | matches | | 0 | "Just the gut-hot certainty she'd" | | 1 | "Then he cut." |
| | ratio | 0.025 | |
| 96.54% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 81 | | matches | | 0 | "He didn't slow." | | 1 | "She hadn't expected him to." | | 2 | "Her boots hammered the kerb." | | 3 | "She'd clocked him an hour" | | 4 | "He moved like somebody who'd" | | 5 | "she called, not believing it" | | 6 | "He glanced back." | | 7 | "She cleared the wire in" | | 8 | "Her radio was in her" | | 9 | "She did not reach for" | | 10 | "She caught a glimpse of" | | 11 | "He led her north." | | 12 | "He wasn't heading for a" | | 13 | "He was heading somewhere specific," | | 14 | "She stopped at the top," | | 15 | "She knew every inch of" | | 16 | "She could hear, rising out" | | 17 | "He'd paused, one hand on" | | 18 | "he called up" | | 19 | "He backed down another step" |
| | ratio | 0.309 | |
| 71.11% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 63 | | totalSentences | 81 | | matches | | 0 | "Rain came down in sheets," | | 1 | "Quinn ran hard, the breath" | | 2 | "The shout tore loose and" | | 3 | "He didn't slow." | | 4 | "She hadn't expected him to." | | 5 | "Her boots hammered the kerb." | | 6 | "She'd clocked him an hour" | | 7 | "Herrera hit the mouth of" | | 8 | "Quinn slid round the corner," | | 9 | "He moved like somebody who'd" | | 10 | "she called, not believing it" | | 11 | "He glanced back." | | 12 | "A single look, warm brown" | | 13 | "The Saint Christopher at his" | | 14 | "She cleared the wire in" | | 15 | "Rain ran down the inside" | | 16 | "Her radio was in her" | | 17 | "She did not reach for" | | 18 | "The yard emptied onto Camden" | | 19 | "Saturday-night crowds still thronged the" |
| | ratio | 0.778 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 81 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 40 | | technicalSentenceCount | 1 | | matches | | 0 | "She caught a glimpse of Herrera at the lights, then lost him in a knot of girls in plastic ponchos, then found him again as he bolted across the road between a …" |
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| 25.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 1 | | matches | | 0 | "she called, not believing it herself" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 14 | | tagDensity | 0.214 | | leniency | 0.429 | | rawRatio | 0 | | effectiveRatio | 0 | |