| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 8 | | adverbTagCount | 1 | | adverbTags | | 0 | "Quinn crouched again [again]" |
| | dialogueSentences | 26 | | tagDensity | 0.308 | | leniency | 0.615 | | rawRatio | 0.125 | | effectiveRatio | 0.077 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1022 | | 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) | |
| 95.11% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1022 | | totalAiIsms | 1 | | found | | | highlights | | |
| 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 | 32 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 32 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 50 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 56 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1024 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 82.77% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 21 | | wordCount | 595 | | uniqueNames | 9 | | maxNameDensity | 1.34 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Blake" | | discoveredNames | | Camden | 1 | | Ronan | 1 | | Blake | 6 | | Quinn | 8 | | Underneath | 1 | | Deptford | 1 | | Morris | 1 | | British | 1 | | Museum | 1 |
| | persons | | 0 | "Ronan" | | 1 | "Blake" | | 2 | "Quinn" | | 3 | "Morris" |
| | places | | | globalScore | 0.828 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 27 | | 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 | 1024 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 50 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 31 | | mean | 33.03 | | std | 23.64 | | cv | 0.716 | | sampleLengths | | 0 | 67 | | 1 | 16 | | 2 | 28 | | 3 | 18 | | 4 | 4 | | 5 | 2 | | 6 | 26 | | 7 | 10 | | 8 | 5 | | 9 | 75 | | 10 | 3 | | 11 | 22 | | 12 | 50 | | 13 | 50 | | 14 | 73 | | 15 | 15 | | 16 | 7 | | 17 | 25 | | 18 | 69 | | 19 | 32 | | 20 | 45 | | 21 | 64 | | 22 | 8 | | 23 | 11 | | 24 | 61 | | 25 | 44 | | 26 | 62 | | 27 | 7 | | 28 | 33 | | 29 | 37 | | 30 | 55 |
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| 72.37% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 32 | | matches | | 0 | "got used" | | 1 | "been forced" | | 2 | "was supposed" |
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| 71.79% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 104 | | matches | | 0 | "was reading" | | 1 | "was looking" |
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| 28.57% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 50 | | ratio | 0.04 | | matches | | 0 | "She was looking at the floor now, at a scuff mark near the skirting board that could've been nothing, could've been the cleaner's mop, except it curved in a shape too deliberate for an accident — half a circle, broken where the wall interrupted it, like someone had begun to draw something and been forced to stop." | | 1 | "She stepped back over the tape, already reaching for her phone, already thinking of a number she hadn't called in eighteen months — a curly-haired researcher at the British Museum who owed her more than one favour, and who might, if pressed, know exactly what a green line at the throat was supposed to mean." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 594 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 21 | | adverbRatio | 0.03535353535353535 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.003367003367003367 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 50 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 50 | | mean | 20.48 | | std | 14.5 | | cv | 0.708 | | sampleLengths | | 0 | 20 | | 1 | 28 | | 2 | 11 | | 3 | 8 | | 4 | 16 | | 5 | 28 | | 6 | 18 | | 7 | 4 | | 8 | 2 | | 9 | 26 | | 10 | 10 | | 11 | 5 | | 12 | 26 | | 13 | 22 | | 14 | 27 | | 15 | 3 | | 16 | 22 | | 17 | 23 | | 18 | 2 | | 19 | 2 | | 20 | 14 | | 21 | 9 | | 22 | 15 | | 23 | 35 | | 24 | 54 | | 25 | 16 | | 26 | 3 | | 27 | 15 | | 28 | 7 | | 29 | 25 | | 30 | 19 | | 31 | 24 | | 32 | 26 | | 33 | 32 | | 34 | 19 | | 35 | 26 | | 36 | 30 | | 37 | 34 | | 38 | 8 | | 39 | 11 | | 40 | 4 | | 41 | 57 | | 42 | 7 | | 43 | 37 | | 44 | 7 | | 45 | 55 | | 46 | 7 | | 47 | 33 | | 48 | 37 | | 49 | 55 |
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| 94.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.66 | | totalSentences | 50 | | uniqueOpeners | 33 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 28 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 28 | | matches | | 0 | "His shirt had burned through" | | 1 | "She let the collar fall" | | 2 | "She turned in a slow" | | 3 | "She was looking at the" | | 4 | "She'd seen a shape like" | | 5 | "She stepped back over the" |
| | ratio | 0.214 | |
| 49.29% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 23 | | totalSentences | 28 | | matches | | 0 | "Quinn ducked under the tape" | | 1 | "The shop sat wedged between" | | 2 | "DC Ronan Blake met her" | | 3 | "Quinn crouched at the doorway" | | 4 | "Blake shrugged, flipped a page" | | 5 | "Quinn moved in now, slow," | | 6 | "The victim lay on his" | | 7 | "His shirt had burned through" | | 8 | "Quinn crouched again, closer this" | | 9 | "Something bright and metallic, like" | | 10 | "Blake crouched beside her, closer" | | 11 | "Quinn reached into her coat," | | 12 | "Blake leaned in, squinted, straightened" | | 13 | "She let the collar fall" | | 14 | "She turned in a slow" | | 15 | "Blake offered, and to his" | | 16 | "Quinn nodded toward the corpse's" | | 17 | "Blake exhaled through his nose," | | 18 | "Quinn didn't answer straightaway." | | 19 | "She was looking at the" |
| | ratio | 0.821 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 28 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 18 | | technicalSentenceCount | 4 | | matches | | 0 | "The shop sat wedged between a defunct locksmith and a shuttered laundrette, one of those Camden storefronts that changed hands every eighteen months and never q…" | | 1 | "She was looking at the floor now, at a scuff mark near the skirting board that could've been nothing, could've been the cleaner's mop, except it curved in a sha…" | | 2 | "Three years back, in a warehouse near Deptford, the night she'd lost Morris and gained nothing in return but a case file that closed itself with words like inco…" | | 3 | "She stepped back over the tape, already reaching for her phone, already thinking of a number she hadn't called in eighteen months — a curly-haired researcher at…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 8 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 26 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0 | | effectiveRatio | 0 | |