| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 94.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 868 | | 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) | |
| 42.40% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 868 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "measured" | | 1 | "familiar" | | 2 | "chill" | | 3 | "rhythmic" | | 4 | "standard" | | 5 | "pulsed" | | 6 | "resonance" | | 7 | "pulse" | | 8 | "echoed" | | 9 | "depths" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 59 | | matches | (empty) | |
| 94.43% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 59 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 59 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 37 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 862 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 862 | | uniqueNames | 12 | | maxNameDensity | 0.93 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | London | 2 | | Harlow | 1 | | Quinn | 8 | | Raven | 1 | | Nest | 1 | | Herrera | 5 | | Victorian | 1 | | Tube | 1 | | Morris | 3 | | Metropolitan | 1 | | Police | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Herrera" | | 4 | "Tube" | | 5 | "Morris" | | 6 | "Police" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 53.85% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 52 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like this" | | 1 | "resonance that seemed to bypass her ears and vibrate directly inside her skull" |
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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 | 862 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 59 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 20 | | mean | 43.1 | | std | 23.39 | | cv | 0.543 | | sampleLengths | | 0 | 65 | | 1 | 28 | | 2 | 2 | | 3 | 93 | | 4 | 43 | | 5 | 3 | | 6 | 52 | | 7 | 42 | | 8 | 70 | | 9 | 37 | | 10 | 55 | | 11 | 55 | | 12 | 12 | | 13 | 77 | | 14 | 43 | | 15 | 61 | | 16 | 40 | | 17 | 17 | | 18 | 36 | | 19 | 31 |
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| 81.47% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 59 | | matches | | 0 | "was gone" | | 1 | "was canted" | | 2 | "been modified" | | 3 | "been welded" |
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| 0.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 129 | | matches | | 0 | "was chasing" | | 1 | "was chasing" | | 2 | "was running" | | 3 | "was crawling" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 59 | | ratio | 0.102 | | matches | | 0 | "She knew what Herrera was—or at least, she knew what he did." | | 1 | "She checked the worn leather watch on her left wrist—02:14." | | 2 | "Just a dead wall of grimy brick and—" | | 3 | "There was no sound save for the distant, rhythmic thrum of the underground—a low-frequency vibration that rattled her teeth and made the hairs on the back of her neck stand on end." | | 4 | "Three years ago, she had stood in a basement just like this—dark, quiet, smelling of ozone and iron—and watched her partner, DS Morris, step into a shadow that had no business existing." | | 5 | "A sharp, metallic clink echoed from the depths below—the distinct sound of a boot striking loose gravel." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 874 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 24 | | adverbRatio | 0.02745995423340961 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.010297482837528604 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 59 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 59 | | mean | 14.61 | | std | 8.37 | | cv | 0.573 | | sampleLengths | | 0 | 30 | | 1 | 9 | | 2 | 26 | | 3 | 28 | | 4 | 2 | | 5 | 10 | | 6 | 24 | | 7 | 12 | | 8 | 18 | | 9 | 6 | | 10 | 6 | | 11 | 17 | | 12 | 16 | | 13 | 17 | | 14 | 10 | | 15 | 3 | | 16 | 13 | | 17 | 10 | | 18 | 12 | | 19 | 2 | | 20 | 7 | | 21 | 8 | | 22 | 18 | | 23 | 24 | | 24 | 9 | | 25 | 18 | | 26 | 11 | | 27 | 32 | | 28 | 21 | | 29 | 16 | | 30 | 10 | | 31 | 14 | | 32 | 10 | | 33 | 21 | | 34 | 10 | | 35 | 18 | | 36 | 6 | | 37 | 21 | | 38 | 12 | | 39 | 32 | | 40 | 4 | | 41 | 17 | | 42 | 10 | | 43 | 14 | | 44 | 4 | | 45 | 14 | | 46 | 25 | | 47 | 37 | | 48 | 6 | | 49 | 2 |
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| 73.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.4915254237288136 | | totalSentences | 59 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 56 | | matches | | 0 | "Just a dead wall of" | | 1 | "Then came a muffled voice," |
| | ratio | 0.036 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 15 | | totalSentences | 56 | | matches | | 0 | "She kept her stride measured," | | 1 | "She had tracked the former" | | 2 | "She knew what Herrera was—or" | | 3 | "She rounded the corner into" | | 4 | "She checked the worn leather" | | 5 | "Her sharp jaw tightened as" | | 6 | "She crouched, her knees popping" | | 7 | "She pulled a heavy-duty tactical" | | 8 | "She knew the sewer mains," | | 9 | "She hesitated, her boot poised" | | 10 | "She had found the coldness" | | 11 | "She was a cop." | | 12 | "She believed in evidence, ballistics," | | 13 | "She gripped the metal rail" | | 14 | "Her knuckles turned white beneath" |
| | ratio | 0.268 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 36 | | totalSentences | 56 | | matches | | 0 | "The rain over Soho did" | | 1 | "Detective Harlow Quinn did not" | | 2 | "She kept her stride measured," | | 3 | "Quinn’s breath plumed in the" | | 4 | "She had tracked the former" | | 5 | "She knew what Herrera was—or" | | 6 | "A disgraced NHS medic patching" | | 7 | "Tonight, Quinn was chasing a" | | 8 | "She rounded the corner into" | | 9 | "The brick walls were slick" | | 10 | "A rusted dumpster rattled in" | | 11 | "Herrera was gone." | | 12 | "Quinn cursed under her breath," | | 13 | "She checked the worn leather" | | 14 | "Her sharp jaw tightened as" | | 15 | "A heavy iron grate, set" | | 16 | "She crouched, her knees popping" | | 17 | "She pulled a heavy-duty tactical" | | 18 | "The light caught the slick" | | 19 | "This wasn't a standard storm" |
| | ratio | 0.643 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 56 | | matches | | 0 | "If she followed him down" | | 1 | "If things went sideways, the" |
| | ratio | 0.036 | |
| 74.83% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 42 | | technicalSentenceCount | 4 | | matches | | 0 | "A disgraced NHS medic patching up the city's untouchables, laundering underworld violence through back-alley stitches and unrecorded prescriptions." | | 1 | "Rising from the dark gap beneath was a thin plume of warm, greasy air that smelled faintly of ozone, old copper, and damp earth." | | 2 | "There was no sound save for the distant, rhythmic thrum of the underground—a low-frequency vibration that rattled her teeth and made the hairs on the back of he…" | | 3 | "None of them hummed with that unnatural, thrumming resonance that seemed to bypass her ears and vibrate directly inside her skull." |
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| 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 | |