| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 3 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 70 | | tagDensity | 0.043 | | leniency | 0.086 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1869 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 78.60% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1869 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "etched" | | 1 | "quivered" | | 2 | "charm" | | 3 | "pulse" | | 4 | "trembled" | | 5 | "footsteps" | | 6 | "echo" | | 7 | "silk" |
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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 | 145 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 145 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 211 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1869 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1369 | | uniqueNames | 9 | | maxNameDensity | 1.24 | | worstName | "Bell" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Bell" | | discoveredNames | | Harlow | 1 | | Quinn | 10 | | Tube | 1 | | West | 1 | | End | 1 | | Sergeant | 1 | | Bell | 17 | | Morris | 1 | | Kowalski | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Sergeant" | | 3 | "Bell" | | 4 | "Morris" | | 5 | "Kowalski" |
| | places | | | globalScore | 0.879 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 87 | | 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 | 1869 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 211 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 105 | | mean | 17.8 | | std | 22.5 | | cv | 1.264 | | sampleLengths | | 0 | 3 | | 1 | 33 | | 2 | 5 | | 3 | 15 | | 4 | 110 | | 5 | 48 | | 6 | 3 | | 7 | 44 | | 8 | 7 | | 9 | 37 | | 10 | 23 | | 11 | 80 | | 12 | 2 | | 13 | 5 | | 14 | 4 | | 15 | 58 | | 16 | 6 | | 17 | 10 | | 18 | 1 | | 19 | 5 | | 20 | 20 | | 21 | 34 | | 22 | 3 | | 23 | 6 | | 24 | 11 | | 25 | 81 | | 26 | 5 | | 27 | 7 | | 28 | 24 | | 29 | 3 | | 30 | 9 | | 31 | 4 | | 32 | 12 | | 33 | 63 | | 34 | 10 | | 35 | 6 | | 36 | 43 | | 37 | 11 | | 38 | 76 | | 39 | 4 | | 40 | 4 | | 41 | 2 | | 42 | 2 | | 43 | 1 | | 44 | 1 | | 45 | 1 | | 46 | 1 | | 47 | 1 | | 48 | 32 | | 49 | 16 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 145 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 212 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 211 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1373 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 39 | | adverbRatio | 0.028404952658412235 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.0029133284777858705 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 211 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 211 | | mean | 8.86 | | std | 8.02 | | cv | 0.905 | | sampleLengths | | 0 | 3 | | 1 | 16 | | 2 | 4 | | 3 | 1 | | 4 | 12 | | 5 | 5 | | 6 | 15 | | 7 | 20 | | 8 | 26 | | 9 | 11 | | 10 | 10 | | 11 | 26 | | 12 | 17 | | 13 | 8 | | 14 | 3 | | 15 | 4 | | 16 | 10 | | 17 | 2 | | 18 | 2 | | 19 | 19 | | 20 | 3 | | 21 | 12 | | 22 | 23 | | 23 | 9 | | 24 | 7 | | 25 | 5 | | 26 | 32 | | 27 | 2 | | 28 | 21 | | 29 | 34 | | 30 | 1 | | 31 | 1 | | 32 | 8 | | 33 | 7 | | 34 | 3 | | 35 | 4 | | 36 | 22 | | 37 | 2 | | 38 | 5 | | 39 | 4 | | 40 | 2 | | 41 | 12 | | 42 | 22 | | 43 | 3 | | 44 | 14 | | 45 | 3 | | 46 | 2 | | 47 | 6 | | 48 | 4 | | 49 | 6 |
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| 69.04% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.44549763033175355 | | totalSentences | 211 | | uniqueOpeners | 94 | |
| 28.25% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 118 | | matches | | 0 | "Instead, the edges broke in" |
| | ratio | 0.008 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 118 | | matches | | 0 | "He froze at once." | | 1 | "Her grip stayed firm until" | | 2 | "She let go and stepped" | | 3 | "He stood by the smashed" | | 4 | "Her leather watch pressed against" | | 5 | "She crouched again." | | 6 | "She glanced up" | | 7 | "She walked past the body" | | 8 | "She crouched and picked one" | | 9 | "She held the shard near" | | 10 | "She set it down with" | | 11 | "She had seen enough nonsense" | | 12 | "She lowered the pouch." | | 13 | "She crossed to the victim" | | 14 | "She scraped a fleck onto" | | 15 | "His trouser knees were clean." | | 16 | "His left hand, stretched out," | | 17 | "He squatted with a grunt." | | 18 | "She continued before he could" | | 19 | "Their black steps vanished into" |
| | ratio | 0.22 | |
| 82.88% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 89 | | totalSentences | 118 | | matches | | 0 | "Detective Harlow Quinn caught the" | | 1 | "He froze at once." | | 2 | "Her grip stayed firm until" | | 3 | "She let go and stepped" | | 4 | "The air inside carried old" | | 5 | "The station map on the" | | 6 | "Someone had strung crime scene" | | 7 | "The tape looked absurd here," | | 8 | "Quinn crouched beside the compass" | | 9 | "Verdigris around the hinge." | | 10 | "The needle quivered, swung north," | | 11 | "He stood by the smashed" | | 12 | "Bell gave a small shrug." | | 13 | "Her leather watch pressed against" | | 14 | "The victim lay on the" | | 15 | "Blood had spread in a" | | 16 | "The pool should have settled" | | 17 | "She crouched again." | | 18 | "Bell saw where she was" | | 19 | "She glanced up" |
| | ratio | 0.754 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 118 | | matches | | 0 | "Whoever wrote them had skill." | | 1 | "As if someone had pressed" | | 2 | "Before Bell could demand who" |
| | ratio | 0.025 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 57 | | technicalSentenceCount | 3 | | matches | | 0 | "The air inside carried old grease, damp dust, and something sharper beneath it, a bitter mineral tang that did not belong in a derelict Tube station." | | 1 | "Someone had strung crime scene tape from a rusted barrier to an old advertising board for a West End show that had closed a decade ago." | | 2 | "At the foot of the left balustrade, half hidden by shadow, a line had been painted on the metal skirt in a glossy substance that looked black until her torch st…" |
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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 | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 70 | | tagDensity | 0.014 | | leniency | 0.029 | | rawRatio | 0 | | effectiveRatio | 0 | |