| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 101 | | tagDensity | 0.139 | | leniency | 0.277 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.15% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2063 | | 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) | |
| 90.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2063 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "velvet" | | 1 | "trembled" | | 2 | "quivered" | | 3 | "flicked" |
| |
| 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 | 172 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 172 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 259 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 36 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2063 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 21 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 80 | | wordCount | 1433 | | uniqueNames | 10 | | maxNameDensity | 2.02 | | worstName | "Quinn" | | maxWindowNameDensity | 4 | | worstWindowName | "Quinn" | | discoveredNames | | Tube | 1 | | Harlow | 1 | | Quinn | 29 | | Metropolitan | 1 | | Police | 1 | | Leonard | 1 | | Pike | 22 | | Kowalski | 1 | | Vale | 9 | | Eva | 14 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Police" | | 3 | "Leonard" | | 4 | "Pike" | | 5 | "Kowalski" | | 6 | "Eva" |
| | places | | | globalScore | 0.488 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 111 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a coin until she turned it ov" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2063 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 259 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 192 | | mean | 10.74 | | std | 12 | | cv | 1.117 | | sampleLengths | | 0 | 8 | | 1 | 59 | | 2 | 18 | | 3 | 25 | | 4 | 7 | | 5 | 8 | | 6 | 37 | | 7 | 13 | | 8 | 6 | | 9 | 12 | | 10 | 5 | | 11 | 4 | | 12 | 6 | | 13 | 5 | | 14 | 8 | | 15 | 4 | | 16 | 9 | | 17 | 8 | | 18 | 60 | | 19 | 6 | | 20 | 4 | | 21 | 16 | | 22 | 7 | | 23 | 4 | | 24 | 10 | | 25 | 32 | | 26 | 19 | | 27 | 3 | | 28 | 22 | | 29 | 4 | | 30 | 7 | | 31 | 3 | | 32 | 5 | | 33 | 4 | | 34 | 10 | | 35 | 11 | | 36 | 4 | | 37 | 11 | | 38 | 6 | | 39 | 40 | | 40 | 8 | | 41 | 12 | | 42 | 61 | | 43 | 4 | | 44 | 14 | | 45 | 41 | | 46 | 7 | | 47 | 5 | | 48 | 3 | | 49 | 10 |
| |
| 95.06% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 172 | | matches | | 0 | "been drilled" | | 1 | "been torn" | | 2 | "been smashed" | | 3 | "been replaced" | | 4 | "been disturbed" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 232 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 259 | | ratio | 0.004 | | matches | | 0 | "The tiled name signs had been smashed, but enough letters remained along the far wall to make out the station’s former name: DOWN—." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1435 | | adjectiveStacks | 1 | | stackExamples | | 0 | "old nicotine-yellowed wall." |
| | adverbCount | 28 | | adverbRatio | 0.01951219512195122 | | lyAdverbCount | 5 | | lyAdverbRatio | 0.003484320557491289 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 259 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 259 | | mean | 7.97 | | std | 5.99 | | cv | 0.752 | | sampleLengths | | 0 | 8 | | 1 | 30 | | 2 | 21 | | 3 | 8 | | 4 | 18 | | 5 | 15 | | 6 | 10 | | 7 | 6 | | 8 | 1 | | 9 | 8 | | 10 | 10 | | 11 | 9 | | 12 | 18 | | 13 | 7 | | 14 | 6 | | 15 | 6 | | 16 | 12 | | 17 | 5 | | 18 | 4 | | 19 | 6 | | 20 | 5 | | 21 | 8 | | 22 | 4 | | 23 | 8 | | 24 | 1 | | 25 | 8 | | 26 | 22 | | 27 | 6 | | 28 | 14 | | 29 | 18 | | 30 | 6 | | 31 | 2 | | 32 | 2 | | 33 | 16 | | 34 | 7 | | 35 | 4 | | 36 | 10 | | 37 | 14 | | 38 | 9 | | 39 | 9 | | 40 | 5 | | 41 | 9 | | 42 | 5 | | 43 | 3 | | 44 | 22 | | 45 | 4 | | 46 | 7 | | 47 | 3 | | 48 | 5 | | 49 | 4 |
| |
| 52.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.35135135135135137 | | totalSentences | 259 | | uniqueOpeners | 91 | |
| 65.79% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 152 | | matches | | 0 | "Nearly four in the morning." | | 1 | "Then it stopped." | | 2 | "Even the forensic team had" |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 22 | | totalSentences | 152 | | matches | | 0 | "It looked like a coin" | | 1 | "His suit had once been" | | 2 | "Her curly red hair had" | | 3 | "She pushed them up with" | | 4 | "She pulled out a notebook," | | 5 | "His face rested in it," | | 6 | "He had not struck the" | | 7 | "It started three feet away," | | 8 | "She lowered herself until her" | | 9 | "He came over, notebook already" | | 10 | "His right had blood across" | | 11 | "Its casing carried a green" | | 12 | "It pointed at Vale’s outstretched" | | 13 | "It settled on the wall" | | 14 | "She examined the tiles." | | 15 | "Their glaze looked newer, their" | | 16 | "She tapped them with a" | | 17 | "It had been disturbed near" | | 18 | "She moved back to Vale’s" | | 19 | "His fingers were spread towards" |
| | ratio | 0.145 | |
| 38.95% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 128 | | totalSentences | 152 | | matches | | 0 | "The old Tube station had" | | 1 | "Detective Harlow Quinn stood beneath" | | 2 | "Camden roared somewhere above her:" | | 3 | "The gate bore a Metropolitan" | | 4 | "DC Leonard Pike muttered behind" | | 5 | "Quinn held out her gloved" | | 6 | "Pike placed a small disc" | | 7 | "It looked like a coin" | | 8 | "The pale surface held the" | | 9 | "A tiny hole had been" | | 10 | "Quinn pushed the token into" | | 11 | "Something clicked beyond the metal." | | 12 | "The gate swung inward." | | 13 | "Pike stared at the open" | | 14 | "Quinn slipped the token into" | | 15 | "That answer sat between them" | | 16 | "The stairwell had once carried" | | 17 | "A woman in a fur" | | 18 | "A train route map sagged" | | 19 | "Halfway down, the smell reached" |
| | ratio | 0.842 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 152 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 2 | | matches | | 0 | "Camden roared somewhere above her: buses grinding at kerbs, a siren cutting across the market crowd, music leaking from a pub." | | 1 | "Fine protective sigils circled its face, scratched into metal by a hand that had not cared about beauty." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 90.59% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 13 | | fancyCount | 6 | | fancyTags | | 0 | "DC Leonard Pike muttered (mutter)" | | 1 | "Pike added (add)" | | 2 | "she repeated (repeat)" | | 3 | "Quinn continued (continue)" | | 4 | "she whispered (whisper)" | | 5 | "Eva pressed (press)" |
| | dialogueSentences | 101 | | tagDensity | 0.129 | | leniency | 0.257 | | rawRatio | 0.462 | | effectiveRatio | 0.119 | |