| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 96.09% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1279 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 33.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1279 | | totalAiIsms | 17 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "charged" | | 2 | "vibrated" | | 3 | "flickered" | | 4 | "electric" | | 5 | "footsteps" | | 6 | "echoed" | | 7 | "depths" | | 8 | "velvet" | | 9 | "weight" | | 10 | "crystal" | | 11 | "pulsed" | | 12 | "gloom" | | 13 | "dancing" | | 14 | "could feel" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 212 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 212 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 212 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 20 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1277 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 57 | | wordCount | 1277 | | uniqueNames | 15 | | maxNameDensity | 2.04 | | worstName | "Harlow" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Harlow" | | discoveredNames | | London | 1 | | Harlow | 26 | | Quinn | 2 | | Camden | 1 | | Lock | 1 | | Tube | 1 | | Veil | 1 | | Market | 2 | | Metropolitan | 2 | | Police | 2 | | Morris | 8 | | Unsolved | 1 | | Watchers | 1 | | Detective | 2 | | You | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Market" | | 3 | "Police" | | 4 | "Morris" | | 5 | "You" |
| | places | | | globalScore | 0.482 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | 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 | 1277 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 212 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 58 | | mean | 22.02 | | std | 17.07 | | cv | 0.775 | | sampleLengths | | 0 | 59 | | 1 | 72 | | 2 | 64 | | 3 | 40 | | 4 | 37 | | 5 | 10 | | 6 | 38 | | 7 | 66 | | 8 | 41 | | 9 | 47 | | 10 | 28 | | 11 | 6 | | 12 | 10 | | 13 | 1 | | 14 | 18 | | 15 | 3 | | 16 | 28 | | 17 | 29 | | 18 | 6 | | 19 | 8 | | 20 | 15 | | 21 | 45 | | 22 | 15 | | 23 | 12 | | 24 | 27 | | 25 | 29 | | 26 | 7 | | 27 | 27 | | 28 | 5 | | 29 | 8 | | 30 | 42 | | 31 | 9 | | 32 | 8 | | 33 | 10 | | 34 | 26 | | 35 | 15 | | 36 | 2 | | 37 | 23 | | 38 | 21 | | 39 | 11 | | 40 | 13 | | 41 | 18 | | 42 | 23 | | 43 | 1 | | 44 | 6 | | 45 | 8 | | 46 | 19 | | 47 | 2 | | 48 | 21 | | 49 | 9 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 212 | | matches | | 0 | "were gone" | | 1 | "was gone" | | 2 | "was made" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 239 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 212 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1279 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 16 | | adverbRatio | 0.012509773260359656 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003127443315089914 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 212 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 212 | | mean | 6.02 | | std | 3.57 | | cv | 0.593 | | sampleLengths | | 0 | 16 | | 1 | 13 | | 2 | 8 | | 3 | 10 | | 4 | 12 | | 5 | 4 | | 6 | 12 | | 7 | 14 | | 8 | 16 | | 9 | 2 | | 10 | 4 | | 11 | 20 | | 12 | 8 | | 13 | 8 | | 14 | 13 | | 15 | 7 | | 16 | 14 | | 17 | 14 | | 18 | 4 | | 19 | 9 | | 20 | 6 | | 21 | 2 | | 22 | 6 | | 23 | 1 | | 24 | 12 | | 25 | 5 | | 26 | 16 | | 27 | 10 | | 28 | 2 | | 29 | 2 | | 30 | 1 | | 31 | 1 | | 32 | 5 | | 33 | 5 | | 34 | 2 | | 35 | 15 | | 36 | 4 | | 37 | 10 | | 38 | 7 | | 39 | 7 | | 40 | 11 | | 41 | 5 | | 42 | 13 | | 43 | 4 | | 44 | 11 | | 45 | 15 | | 46 | 5 | | 47 | 5 | | 48 | 12 | | 49 | 6 |
| |
| 33.96% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 34 | | diversityRatio | 0.28773584905660377 | | totalSentences | 212 | | uniqueOpeners | 61 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 184 | | matches | (empty) | | ratio | 0 | |
| 83.04% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 63 | | totalSentences | 184 | | matches | | 0 | "She checked the worn leather" | | 1 | "Her sharp jaw tightened." | | 2 | "She wiped her face with" | | 3 | "She had chased enough men" | | 4 | "She turned the corner into" | | 5 | "Her hand drifted to the" | | 6 | "She did not draw the" | | 7 | "She stepped through the gate." | | 8 | "They glowed faintly in the" | | 9 | "She reached the bottom of" | | 10 | "She had heard the rumours." | | 11 | "He never came back." | | 12 | "He held something in his" | | 13 | "He handed it to a" | | 14 | "Her boots clicked on the" | | 15 | "She felt the weight of" | | 16 | "She kept her hands visible." | | 17 | "He backed toward the gap" | | 18 | "You cannot follow." | | 19 | "She held a staff topped" |
| | ratio | 0.342 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 173 | | totalSentences | 184 | | matches | | 0 | "Rain lashed against the pavement," | | 1 | "Detective Harlow Quinn sprinted, her" | | 2 | "Breath hitched in her throat," | | 3 | "She checked the worn leather" | | 4 | "Her sharp jaw tightened." | | 5 | "Water streamed down her closely" | | 6 | "She wiped her face with" | | 7 | "The suspect wore a dark" | | 8 | "Harlow knew the difference." | | 9 | "She had chased enough men" | | 10 | "She turned the corner into" | | 11 | "Trash bins overflowed, spilling refuse" | | 12 | "The smell of damp cardboard" | | 13 | "Graffiti covered the tiled walls," | | 14 | "Harlow slowed her pace." | | 15 | "Her hand drifted to the" | | 16 | "She did not draw the" | | 17 | "The air down there felt" | | 18 | "Static charged the moisture, making" | | 19 | "She stepped through the gate." |
| | ratio | 0.94 | |
| 27.17% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 184 | | matches | | 0 | "If I walk through there," |
| | ratio | 0.005 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 32 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |