| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1459 | | 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) | |
| 86.29% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1459 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "weight" | | 1 | "racing" | | 2 | "resolved" | | 3 | "resolve" |
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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 | 95 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 95 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | 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 | 1469 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 28 | | wordCount | 1381 | | uniqueNames | 22 | | maxNameDensity | 0.36 | | worstName | "Herrera" | | maxWindowNameDensity | 1 | | worstWindowName | "Camden" | | discoveredNames | | Camden | 2 | | High | 1 | | Street | 1 | | Herrera | 5 | | Soho | 1 | | Raven | 1 | | Nest | 1 | | Tottenham | 1 | | Court | 1 | | Road | 1 | | Seville | 1 | | Saint | 1 | | Christopher | 1 | | Horse | 1 | | Tunnel | 1 | | Stables | 1 | | Land | 1 | | Registry | 1 | | Quinn | 2 | | Silver | 1 | | Greenwich | 1 | | Morris | 1 |
| | persons | | 0 | "Herrera" | | 1 | "Raven" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Tunnel" | | 5 | "Stables" | | 6 | "Quinn" | | 7 | "Morris" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Soho" | | 4 | "Tottenham" | | 5 | "Court" | | 6 | "Road" | | 7 | "Seville" | | 8 | "Horse" | | 9 | "Greenwich" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 79 | | 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 | 1469 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 107 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 32.64 | | std | 27.2 | | cv | 0.833 | | sampleLengths | | 0 | 42 | | 1 | 33 | | 2 | 76 | | 3 | 89 | | 4 | 14 | | 5 | 15 | | 6 | 3 | | 7 | 5 | | 8 | 5 | | 9 | 14 | | 10 | 44 | | 11 | 37 | | 12 | 2 | | 13 | 29 | | 14 | 2 | | 15 | 4 | | 16 | 24 | | 17 | 58 | | 18 | 24 | | 19 | 58 | | 20 | 36 | | 21 | 46 | | 22 | 14 | | 23 | 9 | | 24 | 46 | | 25 | 34 | | 26 | 66 | | 27 | 48 | | 28 | 83 | | 29 | 30 | | 30 | 1 | | 31 | 27 | | 32 | 19 | | 33 | 25 | | 34 | 17 | | 35 | 5 | | 36 | 21 | | 37 | 78 | | 38 | 73 | | 39 | 120 | | 40 | 19 | | 41 | 31 | | 42 | 27 | | 43 | 1 | | 44 | 15 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 95 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 216 | | matches | | 0 | "wasn't doing" | | 1 | "was already moving" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 10 | | semicolonCount | 1 | | flaggedSentences | 9 | | totalSentences | 107 | | ratio | 0.084 | | matches | | 0 | "Struck off the paramedic register for treatments no tribunal ever got a straight answer on — patients who refused hospitals, wound reports that matched no blade on record, witnesses who recanted by morning." | | 1 | "At the third stall down he crouched to retie a boot and glanced up into the corrugated shutter — where the metal had doubled her, warped and unmistakable." | | 2 | "He hurdled the crossing barrier; she took the low end of it on her thigh and kept her stride." | | 3 | "Past them, the Stables market swallowed the chase — dead corridors, shutter bolts, an iron staircase running with water." | | 4 | "He was faster, nine years younger and lighter on wet stone, and pulling away — so she quit racing him and started cutting angles." | | 5 | "At the tunnel's end hung a lamp on a chain, a gate of black iron bars, and a doorman built like the arches up top — a big man in a railway coat, brass buttons gone green." | | 6 | "Something in the cage clicked — beak or knuckle, Quinn couldn't say — in a rhythm she couldn't count." | | 7 | "Herrera went through, and before the light took him he looked back up the tunnel — straight at the column she'd pressed herself behind — held the look a beat too long, and turned into the glow and was gone." | | 8 | "Backup was forty minutes away if she sprinted, and by then the doors would be shut and the market would be wherever it went when the moon was done — nowhere a warrant could reach." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 705 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 12 | | adverbRatio | 0.01702127659574468 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0028368794326241137 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 13.73 | | std | 9.81 | | cv | 0.714 | | sampleLengths | | 0 | 15 | | 1 | 27 | | 2 | 33 | | 3 | 26 | | 4 | 3 | | 5 | 29 | | 6 | 5 | | 7 | 13 | | 8 | 10 | | 9 | 6 | | 10 | 33 | | 11 | 40 | | 12 | 14 | | 13 | 15 | | 14 | 3 | | 15 | 5 | | 16 | 5 | | 17 | 14 | | 18 | 9 | | 19 | 1 | | 20 | 2 | | 21 | 6 | | 22 | 26 | | 23 | 9 | | 24 | 28 | | 25 | 2 | | 26 | 11 | | 27 | 18 | | 28 | 2 | | 29 | 4 | | 30 | 4 | | 31 | 20 | | 32 | 9 | | 33 | 19 | | 34 | 11 | | 35 | 19 | | 36 | 8 | | 37 | 16 | | 38 | 11 | | 39 | 7 | | 40 | 24 | | 41 | 16 | | 42 | 25 | | 43 | 11 | | 44 | 11 | | 45 | 19 | | 46 | 16 | | 47 | 8 | | 48 | 6 | | 49 | 9 |
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| 63.24% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.40186915887850466 | | totalSentences | 107 | | uniqueOpeners | 43 | |
| 72.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 92 | | matches | | 0 | "Then he crossed against the" | | 1 | "Then a tunnel mouth with" |
| | ratio | 0.022 | |
| 59.13% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 37 | | totalSentences | 92 | | matches | | 0 | "It came off the market" | | 1 | "She had picked Herrera up" | | 2 | "He'd come out carrying a" | | 3 | "She'd sat two cars down." | | 4 | "His file ran forty pages" | | 5 | "Her phone buzzed against her" | | 6 | "She pocketed the phone and" | | 7 | "He ran like a man" | | 8 | "She ran the way eighteen" | | 9 | "He knew her name." | | 10 | "She filed that where she" | | 11 | "He hurdled the crossing barrier;" | | 12 | "He took the stairs two" | | 13 | "She caught the rail and" | | 14 | "He was faster, nine years" | | 15 | "She noted it the way" | | 16 | "She got a fistful of" | | 17 | "He turned under her grip," | | 18 | "He was already moving" | | 19 | "She dropped the jacket in" |
| | ratio | 0.402 | |
| 79.57% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 70 | | totalSentences | 92 | | matches | | 0 | "The rain had held Camden" | | 1 | "It came off the market" | | 2 | "Quinn stood deep in the" | | 3 | "She had picked Herrera up" | | 4 | "The Raven's Nest." | | 5 | "He'd come out carrying a" | | 6 | "She'd sat two cars down." | | 7 | "His file ran forty pages" | | 8 | "The man attached to the" | | 9 | "Her phone buzzed against her" | | 10 | "Osei's voice carried a canteen's" | | 11 | "She pocketed the phone and" | | 12 | "The tunnel ran with shuttered" | | 13 | "He ran like a man" | | 14 | "She ran the way eighteen" | | 15 | "He knew her name." | | 16 | "She filed that where she" | | 17 | "A night bus crawled past" | | 18 | "He hurdled the crossing barrier;" | | 19 | "He took the stairs two" |
| | ratio | 0.761 | |
| 54.35% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 92 | | matches | | 0 | "Now he stood in Camden," |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 2 | | matches | | 0 | "She had picked Herrera up at half past eleven, outside a bar in Soho under a green sign that buzzed like a wasp in a jar." | | 1 | "Struck off the paramedic register for treatments no tribunal ever got a straight answer on — patients who refused hospitals, wound reports that matched no blade…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 3 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |