| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 5 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 83.32% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1199 | | totalAiIsmAdverbs | 4 | | found | | | highlights | | 0 | "very" | | 1 | "slowly" | | 2 | "carefully" |
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| 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) | |
| 66.64% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1199 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "efficient" | | 1 | "charm" | | 2 | "flicked" | | 3 | "charged" | | 4 | "trembled" | | 5 | "echoed" | | 6 | "flicker" | | 7 | "pulse" |
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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 | 102 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 102 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 105 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 44 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1211 | | 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 | 44 | | wordCount | 1195 | | uniqueNames | 22 | | maxNameDensity | 0.59 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Three" | | discoveredNames | | Dean | 1 | | Street | 1 | | Harlow | 2 | | Quinn | 7 | | Tomás | 3 | | Herrera | 6 | | Saint | 1 | | Christopher | 1 | | Camden | 2 | | Road | 1 | | Tube | 2 | | Morris | 3 | | Rotherhithe | 1 | | Raven | 1 | | Nest | 1 | | Hendon | 1 | | Calais | 1 | | Glock | 1 | | Detective | 2 | | Veil | 1 | | Market | 1 | | Three | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | 0 | "Dean" | | 1 | "Street" | | 2 | "Camden" | | 3 | "Road" | | 4 | "Rotherhithe" | | 5 | "Raven" | | 6 | "Hendon" | | 7 | "Calais" | | 8 | "Market" | | 9 | "Three" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 63 | | 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 | 1211 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 105 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 34 | | mean | 35.62 | | std | 27.39 | | cv | 0.769 | | sampleLengths | | 0 | 50 | | 1 | 3 | | 2 | 47 | | 3 | 53 | | 4 | 12 | | 5 | 14 | | 6 | 62 | | 7 | 37 | | 8 | 10 | | 9 | 6 | | 10 | 93 | | 11 | 45 | | 12 | 65 | | 13 | 2 | | 14 | 58 | | 15 | 43 | | 16 | 68 | | 17 | 8 | | 18 | 61 | | 19 | 9 | | 20 | 7 | | 21 | 43 | | 22 | 42 | | 23 | 3 | | 24 | 26 | | 25 | 110 | | 26 | 9 | | 27 | 44 | | 28 | 68 | | 29 | 6 | | 30 | 35 | | 31 | 10 | | 32 | 46 | | 33 | 16 |
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| 91.50% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 102 | | matches | | 0 | "been kicked" | | 1 | "was muffled" | | 2 | "been stripped" | | 3 | "being stared" |
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| 59.65% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 190 | | matches | | 0 | "was playing" | | 1 | "was weighing" | | 2 | "was selling" | | 3 | "was examining" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 12 | | semicolonCount | 0 | | flaggedSentences | 8 | | totalSentences | 105 | | ratio | 0.076 | | matches | | 0 | "A wheelie bin crashed over in front of her — his work — and she vaulted it without breaking stride, landing in a puddle deep enough to soak her to the shin." | | 1 | "That was the trick of grief — it made a body very efficient." | | 2 | "At the bottom, a boarded hoarding that said CLOSED — TUBE WORKS." | | 3 | "The beam cut into a passage of tiled walls — the old cream-and-maroon of a pre-war Tube station — and at the edge of the light, movement." | | 4 | "She killed the torch — it was a lighthouse in here, announcing her — and let her eyes adjust to the greenish glow bleeding up from the bend ahead." | | 5 | "The light came from lanterns — real lanterns, with real flames that burned green and didn't flicker." | | 6 | "He was reaching into his jacket for something — a token, she saw, pale and carved, held out to a figure in a hooded robe at what she slowly understood was a checkpoint." | | 7 | "She pulled her warrant card from her inside pocket, looked at it for a moment — the photo of a younger woman with more hope in her jaw — and tucked it, very carefully, into the lining of her coat where no one could see it." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1190 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 20 | | adverbRatio | 0.01680672268907563 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.0025210084033613447 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 105 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 105 | | mean | 11.53 | | std | 10.19 | | cv | 0.883 | | sampleLengths | | 0 | 14 | | 1 | 16 | | 2 | 20 | | 3 | 3 | | 4 | 3 | | 5 | 3 | | 6 | 41 | | 7 | 4 | | 8 | 32 | | 9 | 6 | | 10 | 4 | | 11 | 2 | | 12 | 2 | | 13 | 3 | | 14 | 7 | | 15 | 5 | | 16 | 9 | | 17 | 5 | | 18 | 3 | | 19 | 12 | | 20 | 14 | | 21 | 20 | | 22 | 13 | | 23 | 7 | | 24 | 13 | | 25 | 17 | | 26 | 1 | | 27 | 9 | | 28 | 6 | | 29 | 25 | | 30 | 5 | | 31 | 22 | | 32 | 13 | | 33 | 28 | | 34 | 2 | | 35 | 13 | | 36 | 12 | | 37 | 11 | | 38 | 7 | | 39 | 22 | | 40 | 8 | | 41 | 27 | | 42 | 8 | | 43 | 2 | | 44 | 5 | | 45 | 7 | | 46 | 3 | | 47 | 1 | | 48 | 18 | | 49 | 6 |
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| 55.87% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.4095238095238095 | | totalSentences | 105 | | uniqueOpeners | 43 | |
| 74.07% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 90 | | matches | | 0 | "Somewhere below and ahead, a" | | 1 | "Then Detective Harlow Quinn stepped" |
| | ratio | 0.022 | |
| 73.33% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 90 | | matches | | 0 | "He didn't stop." | | 1 | "He never did." | | 2 | "She'd known he wouldn't the" | | 3 | "She hit the alley." | | 4 | "Her radio crackled against her" | | 5 | "She slapped it off." | | 6 | "She kept her voice level," | | 7 | "He laughed, high and breathless," | | 8 | "She pushed harder, her lungs" | | 9 | "He broke out onto the" | | 10 | "He kept going, weaving between" | | 11 | "They went under the railway" | | 12 | "He glanced over his shoulder." | | 13 | "His face, for half a" | | 14 | "It was the face of" | | 15 | "She drew her torch and" | | 16 | "She could smell woodsmoke, and" | | 17 | "She looked at the gap" | | 18 | "He'd be nowhere." | | 19 | "He'd be one more name" |
| | ratio | 0.367 | |
| 82.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 90 | | matches | | 0 | "Detective Harlow Quinn ran flat-out," | | 1 | "He didn't stop." | | 2 | "He never did." | | 3 | "She'd known he wouldn't the" | | 4 | "She hit the alley." | | 5 | "A wheelie bin crashed over" | | 6 | "Her radio crackled against her" | | 7 | "She slapped it off." | | 8 | "Tonight was hers." | | 9 | "She kept her voice level," | | 10 | "He laughed, high and breathless," | | 11 | "A good sign." | | 12 | "A running man who talked" | | 13 | "She pushed harder, her lungs" | | 14 | "That was the trick of" | | 15 | "He broke out onto the" | | 16 | "A black cab blared at" | | 17 | "He kept going, weaving between" | | 18 | "That was the second sign," | | 19 | "Quinn filed it away and" |
| | ratio | 0.756 | |
| 55.56% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 90 | | matches | | 0 | "By the time she'd finished" |
| | ratio | 0.011 | |
| 94.16% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 44 | | technicalSentenceCount | 3 | | matches | | 0 | "Three years, almost to the week, since DS Morris had walked into a warehouse in Rotherhithe and walked out in three plastic bags, with a coroner's report that r…" | | 1 | "Three years of Quinn building quiet files on the people who'd been in that warehouse and the people who knew those people, a careful pyramid of names with Tomás…" | | 2 | "Protocol said wait for backup, for an entry team, for somebody in a high-vis to survey the structural integrity of an abandoned Tube station while Herrera disap…" |
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| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 1 | | matches | | 0 | "She kept, conversational" |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 5 | | tagDensity | 0.2 | | leniency | 0.4 | | rawRatio | 1 | | effectiveRatio | 0.4 | |