| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 46 | | tagDensity | 0.261 | | leniency | 0.522 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.89% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1217 | | totalAiIsmAdverbs | 1 | | 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) | |
| 91.78% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1217 | | totalAiIsms | 2 | | 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 | 61 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 61 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 96 | | 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 | 1220 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 26 | | wordCount | 825 | | uniqueNames | 17 | | maxNameDensity | 0.61 | | worstName | "Tomás" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Harlow | 1 | | Quinn | 1 | | Tomás | 5 | | Herrera | 1 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Square | 1 | | Berwick | 1 | | Street | 1 | | Saint | 1 | | Christopher | 1 | | Camden | 1 | | Keys | 5 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Harlow" | | 3 | "Quinn" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Square" | | 7 | "Saint" | | 8 | "Christopher" | | 9 | "Camden" | | 10 | "Keys" |
| | places | | 0 | "Soho" | | 1 | "Charing" | | 2 | "Cross" | | 3 | "Road" | | 4 | "Berwick" | | 5 | "Street" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | 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 | 1220 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 96 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 61 | | mean | 20 | | std | 21.67 | | cv | 1.084 | | sampleLengths | | 0 | 24 | | 1 | 69 | | 2 | 25 | | 3 | 4 | | 4 | 18 | | 5 | 10 | | 6 | 3 | | 7 | 3 | | 8 | 51 | | 9 | 2 | | 10 | 13 | | 11 | 12 | | 12 | 7 | | 13 | 10 | | 14 | 37 | | 15 | 15 | | 16 | 14 | | 17 | 5 | | 18 | 99 | | 19 | 11 | | 20 | 7 | | 21 | 14 | | 22 | 9 | | 23 | 6 | | 24 | 99 | | 25 | 21 | | 26 | 2 | | 27 | 4 | | 28 | 11 | | 29 | 23 | | 30 | 3 | | 31 | 14 | | 32 | 7 | | 33 | 49 | | 34 | 4 | | 35 | 6 | | 36 | 9 | | 37 | 31 | | 38 | 1 | | 39 | 8 | | 40 | 7 | | 41 | 18 | | 42 | 20 | | 43 | 31 | | 44 | 26 | | 45 | 14 | | 46 | 23 | | 47 | 22 | | 48 | 6 | | 49 | 54 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 61 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 127 | | matches | | |
| 83.33% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 96 | | ratio | 0.021 | | matches | | 0 | "He cut off the high street, down a service ramp beside a railway arch, and stopped at a plywood hoarding stencilled DANGER — NETWORK RAIL — NO ACCESS." | | 1 | "Warm air rose out of the stairwell, thick with tallow smoke and river silt and something sweet underneath, and the sound came up with it — voices in too many languages, a fiddle, a hammer on metal, water dripping somewhere deep." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 684 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 9 | | adverbRatio | 0.013157894736842105 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 96 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 96 | | mean | 12.71 | | std | 9.52 | | cv | 0.749 | | sampleLengths | | 0 | 24 | | 1 | 28 | | 2 | 30 | | 3 | 5 | | 4 | 6 | | 5 | 25 | | 6 | 4 | | 7 | 18 | | 8 | 7 | | 9 | 3 | | 10 | 3 | | 11 | 3 | | 12 | 17 | | 13 | 21 | | 14 | 6 | | 15 | 7 | | 16 | 2 | | 17 | 13 | | 18 | 12 | | 19 | 7 | | 20 | 10 | | 21 | 31 | | 22 | 6 | | 23 | 15 | | 24 | 14 | | 25 | 5 | | 26 | 25 | | 27 | 8 | | 28 | 12 | | 29 | 39 | | 30 | 15 | | 31 | 11 | | 32 | 7 | | 33 | 14 | | 34 | 9 | | 35 | 6 | | 36 | 13 | | 37 | 28 | | 38 | 9 | | 39 | 21 | | 40 | 6 | | 41 | 22 | | 42 | 8 | | 43 | 13 | | 44 | 2 | | 45 | 4 | | 46 | 9 | | 47 | 2 | | 48 | 8 | | 49 | 15 |
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| 78.82% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 6 | | diversityRatio | 0.5104166666666666 | | totalSentences | 96 | | uniqueOpeners | 49 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 58 | | matches | | 0 | "Then she didn't." | | 1 | "Twice she closed the gap" | | 2 | "Twice he threw a turn" | | 3 | "Then the dark took him." | | 4 | "Then she unclipped her handcuffs," |
| | ratio | 0.086 | |
| 47.59% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 58 | | matches | | 0 | "She keyed her radio." | | 1 | "She crossed the street at" | | 2 | "She rounded the alley mouth" | | 3 | "He shoulder-checked a wheelie bin" | | 4 | "Her breath tore out in" | | 5 | "He hurled it over his" | | 6 | "He cut left behind the" | | 7 | "She keyed her radio between" | | 8 | "He took her through Soho" | | 9 | "His curls had gone flat" | | 10 | "She went over the fence" | | 11 | "He was coughing now, the" | | 12 | "He cut off the high" | | 13 | "His eyes cut back up" | | 14 | "She stopped at the edge" | | 15 | "He shook his head" | | 16 | "His palm came out flat." | | 17 | "It bounced once on the" | | 18 | "His voice dropped, and something" | | 19 | "He went down one step," |
| | ratio | 0.431 | |
| 54.83% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 47 | | totalSentences | 58 | | matches | | 0 | "The green neon above the" | | 1 | "Detective Harlow Quinn stood in" | | 2 | "Cashmere went on her list." | | 3 | "Everyone went on her list" | | 4 | "She keyed her radio." | | 5 | "She crossed the street at" | | 6 | "She rounded the alley mouth" | | 7 | "He shoulder-checked a wheelie bin" | | 8 | "A cat screamed off a" | | 9 | "Her breath tore out in" | | 10 | "He hurled it over his" | | 11 | "He cut left behind the" | | 12 | "She keyed her radio between" | | 13 | "He took her through Soho" | | 14 | "His curls had gone flat" | | 15 | "The Saint Christopher medallion bounced" | | 16 | "She went over the fence" | | 17 | "He was coughing now, the" | | 18 | "Camden came up out of" | | 19 | "He cut off the high" |
| | ratio | 0.81 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 58 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 41.67% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 2 | | matches | | 0 | "He hurled, hood flapping" | | 1 | "He was coughing now, the sprint folding into something uglier" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 1 | | fancyTags | | 0 | "He was coughing now (be cough)" |
| | dialogueSentences | 46 | | tagDensity | 0.087 | | leniency | 0.174 | | rawRatio | 0.25 | | effectiveRatio | 0.043 | |