| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.27% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1294 | | totalAiIsmAdverbs | 2 | | 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) | |
| 22.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1294 | | totalAiIsms | 20 | | found | | | highlights | | 0 | "fractured" | | 1 | "pulse" | | 2 | "treacherous" | | 3 | "chill" | | 4 | "navigating" | | 5 | "footsteps" | | 6 | "echoed" | | 7 | "streaming" | | 8 | "flickered" | | 9 | "charged" | | 10 | "structure" | | 11 | "weight" | | 12 | "pulsed" | | 13 | "calculating" | | 14 | "scanning" | | 15 | "familiar" |
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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 | 0 | | narrationSentences | 131 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 131 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 131 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 40 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1294 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 36 | | wordCount | 1294 | | uniqueNames | 18 | | maxNameDensity | 0.85 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 11 | | Soho | 2 | | Raven | 1 | | Nest | 1 | | Metropolitan | 1 | | Police | 1 | | Morris | 5 | | Camden | 3 | | Underground | 1 | | Tube | 1 | | Token | 1 | | Veil | 1 | | Market | 1 | | Tomás | 1 | | Herrera | 2 | | John | 1 | | Doe | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Morris" | | 4 | "Token" | | 5 | "Market" | | 6 | "Tomás" | | 7 | "Herrera" | | 8 | "John" | | 9 | "Doe" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 91 | | glossingSentenceCount | 1 | | matches | | 0 | "symbols that seemed to shift when she looked away" |
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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 | 1294 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 131 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 14 | | mean | 92.43 | | std | 47.8 | | cv | 0.517 | | sampleLengths | | 0 | 146 | | 1 | 10 | | 2 | 116 | | 3 | 115 | | 4 | 118 | | 5 | 132 | | 6 | 61 | | 7 | 3 | | 8 | 97 | | 9 | 170 | | 10 | 116 | | 11 | 54 | | 12 | 106 | | 13 | 50 |
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| 97.23% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 131 | | matches | | 0 | "been erected" | | 1 | "was scratched" | | 2 | "were supposed" |
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| 77.11% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 217 | | matches | | 0 | "was being" | | 1 | "was walking" | | 2 | "was running" | | 3 | "was watching" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 131 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1301 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 27 | | adverbRatio | 0.0207532667179093 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.005380476556495004 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 131 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 131 | | mean | 9.88 | | std | 7.03 | | cv | 0.712 | | sampleLengths | | 0 | 21 | | 1 | 27 | | 2 | 12 | | 3 | 15 | | 4 | 6 | | 5 | 29 | | 6 | 4 | | 7 | 24 | | 8 | 4 | | 9 | 4 | | 10 | 10 | | 11 | 3 | | 12 | 9 | | 13 | 10 | | 14 | 20 | | 15 | 16 | | 16 | 5 | | 17 | 3 | | 18 | 3 | | 19 | 10 | | 20 | 9 | | 21 | 18 | | 22 | 6 | | 23 | 4 | | 24 | 14 | | 25 | 7 | | 26 | 5 | | 27 | 16 | | 28 | 21 | | 29 | 6 | | 30 | 5 | | 31 | 9 | | 32 | 11 | | 33 | 17 | | 34 | 4 | | 35 | 8 | | 36 | 7 | | 37 | 5 | | 38 | 3 | | 39 | 16 | | 40 | 13 | | 41 | 5 | | 42 | 2 | | 43 | 18 | | 44 | 12 | | 45 | 7 | | 46 | 12 | | 47 | 10 | | 48 | 4 | | 49 | 11 |
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| 49.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 17 | | diversityRatio | 0.37404580152671757 | | totalSentences | 131 | | uniqueOpeners | 49 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 4 | | totalSentences | 121 | | matches | | 0 | "Just the low, resonant hum" | | 1 | "Just hollow bones and a" | | 2 | "Somewhere in this labyrinth, her" | | 3 | "Somewhere down here, the answers" |
| | ratio | 0.033 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 35 | | totalSentences | 121 | | matches | | 0 | "She stood across the street" | | 1 | "She pushed off the brick" | | 2 | "She did not run." | | 3 | "She closed the distance." | | 4 | "He spotted her reflection in" | | 5 | "Her breath plumed in the" | | 6 | "She kept her weapon secured." | | 7 | "She kept her eyes locked" | | 8 | "He cut left, then right," | | 9 | "Her left wrist ached where" | | 10 | "She checked the face out" | | 11 | "She had seen the scorch" | | 12 | "She had found the glassy," | | 13 | "She had spent thirty-six months" | | 14 | "She was done pretending." | | 15 | "She approached cautiously, water streaming" | | 16 | "Her boots echoed on the" | | 17 | "She passed a rusted turnstile" | | 18 | "It grew heavier, charged with" | | 19 | "She thumbed the transmit button." |
| | ratio | 0.289 | |
| 88.10% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 90 | | totalSentences | 121 | | matches | | 0 | "Rain needled Harlow Quinn’s face," | | 1 | "She stood across the street" | | 2 | "The buzz of the tubing" | | 3 | "Tonight, the city’s pulse was" | | 4 | "The suspect emerged from the" | | 5 | "Quinn’s sharp jaw tightened." | | 6 | "She pushed off the brick" | | 7 | "She did not run." | | 8 | "She closed the distance." | | 9 | "He spotted her reflection in" | | 10 | "Quinn gave chase." | | 11 | "Rain slicked the cobblestones, turning" | | 12 | "Her breath plumed in the" | | 13 | "The suspect vaulted a low" | | 14 | "Quinn followed, shoulder brushing wet" | | 15 | "She kept her weapon secured." | | 16 | "Drawing meant escalation." | | 17 | "Escalation meant noise." | | 18 | "Noise meant losing him in" | | 19 | "She kept her eyes locked" |
| | ratio | 0.744 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 121 | | matches | | 0 | "Now the myth had a" | | 1 | "If Herrera was right about" | | 2 | "Now it felt heavy in" |
| | ratio | 0.025 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 5 | | matches | | 0 | "The suspect emerged from the bar’s side alley, collar turned up against the downpour, moving with the brisk, purposeful stride of a man who knew he was being wa…" | | 1 | "Three years since DS Morris vanished into a case file that read like a ghost story." | | 2 | "Three years of dead ends, redacted reports, and quiet, corrosive suspicions about the clique that operated in the city’s blind spots." | | 3 | "She had heard the name whispered in interrogation rooms, fragments of confessions from junkies and informants who swore they bought vials of liquid shadow, char…" | | 4 | "She thought of Tomás Herrera, the former paramedic who had lost his NHS license patching up wounds that defied human anatomy, the man who had warned her about t…" |
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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 | |