| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 39 | | tagDensity | 0.282 | | leniency | 0.564 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 96.88% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1600 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 34.38% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1600 | | totalAiIsms | 21 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | word | "mask of indifference" | | count | 1 |
| | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | |
| | highlights | | 0 | "measured" | | 1 | "footsteps" | | 2 | "silk" | | 3 | "lilt" | | 4 | "silence" | | 5 | "weight" | | 6 | "charged" | | 7 | "traced" | | 8 | "echo" | | 9 | "mask of indifference" | | 10 | "flickered" | | 11 | "electric" | | 12 | "vibrated" | | 13 | "unspoken" | | 14 | "wavered" | | 15 | "pulsed" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 119 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 3 | | narrationSentences | 119 | | filterMatches | (empty) | | hedgeMatches | | 0 | "seemed to" | | 1 | "tried to" | | 2 | "began to" |
| |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 147 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 73 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1600 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 9 | | unquotedAttributions | 0 | | matches | (empty) | |
| 86.77% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 32 | | wordCount | 1107 | | uniqueNames | 9 | | maxNameDensity | 1.26 | | worstName | "Silas" | | maxWindowNameDensity | 2 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Blackwood | 1 | | Prague | 3 | | Silas | 14 | | Maren | 9 | | Thames | 1 | | Cardiff | 1 |
| | persons | | 0 | "Raven" | | 1 | "Blackwood" | | 2 | "Silas" | | 3 | "Maren" |
| | places | | 0 | "Soho" | | 1 | "Prague" | | 2 | "Thames" | | 3 | "Cardiff" |
| | globalScore | 0.868 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 84 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.625 | | wordCount | 1600 | | matches | | 0 | "not as trophies but as warnings" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 147 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 49 | | mean | 32.65 | | std | 29.69 | | cv | 0.909 | | sampleLengths | | 0 | 110 | | 1 | 38 | | 2 | 22 | | 3 | 11 | | 4 | 92 | | 5 | 34 | | 6 | 58 | | 7 | 56 | | 8 | 23 | | 9 | 55 | | 10 | 2 | | 11 | 30 | | 12 | 12 | | 13 | 4 | | 14 | 17 | | 15 | 68 | | 16 | 3 | | 17 | 3 | | 18 | 30 | | 19 | 1 | | 20 | 8 | | 21 | 14 | | 22 | 33 | | 23 | 44 | | 24 | 4 | | 25 | 6 | | 26 | 18 | | 27 | 29 | | 28 | 58 | | 29 | 50 | | 30 | 30 | | 31 | 34 | | 32 | 12 | | 33 | 67 | | 34 | 79 | | 35 | 14 | | 36 | 81 | | 37 | 16 | | 38 | 23 | | 39 | 74 | | 40 | 19 | | 41 | 21 | | 42 | 22 | | 43 | 1 | | 44 | 12 | | 45 | 4 | | 46 | 7 | | 47 | 23 | | 48 | 128 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 119 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 190 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 147 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1111 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.017101710171017102 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0009000900090009 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 147 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 147 | | mean | 10.88 | | std | 10.14 | | cv | 0.931 | | sampleLengths | | 0 | 20 | | 1 | 25 | | 2 | 15 | | 3 | 12 | | 4 | 15 | | 5 | 23 | | 6 | 6 | | 7 | 10 | | 8 | 11 | | 9 | 11 | | 10 | 2 | | 11 | 4 | | 12 | 16 | | 13 | 8 | | 14 | 3 | | 15 | 8 | | 16 | 8 | | 17 | 16 | | 18 | 16 | | 19 | 20 | | 20 | 24 | | 21 | 15 | | 22 | 19 | | 23 | 15 | | 24 | 14 | | 25 | 10 | | 26 | 8 | | 27 | 11 | | 28 | 6 | | 29 | 6 | | 30 | 4 | | 31 | 8 | | 32 | 19 | | 33 | 6 | | 34 | 7 | | 35 | 10 | | 36 | 13 | | 37 | 2 | | 38 | 18 | | 39 | 10 | | 40 | 12 | | 41 | 6 | | 42 | 7 | | 43 | 2 | | 44 | 12 | | 45 | 10 | | 46 | 4 | | 47 | 4 | | 48 | 12 | | 49 | 4 |
| |
| 33.67% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 24 | | diversityRatio | 0.272108843537415 | | totalSentences | 147 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 114 | | matches | (empty) | | ratio | 0 | |
| 37.54% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 52 | | totalSentences | 114 | | matches | | 0 | "His left knee clicked, a" | | 1 | "He ignored the ache, focusing" | | 2 | "He looked up." | | 3 | "Her hair, once a riot" | | 4 | "She wore a coat of" | | 5 | "Her voice held the raw" | | 6 | "He had hung them not" | | 7 | "She didn't lean back." | | 8 | "Her hands rested on the" | | 9 | "She stared at a water" | | 10 | "He grabbed the bottle of" | | 11 | "He poured until the neck" | | 12 | "He slid it across the" | | 13 | "It stopped inches from her" | | 14 | "She used the strong hand" | | 15 | "It wasn't a greeting." | | 16 | "It was an autopsy." | | 17 | "He traced the rim of" | | 18 | "She closed her eyes, breathing" | | 19 | "She swirled the whiskey, watching" |
| | ratio | 0.456 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 106 | | totalSentences | 114 | | matches | | 0 | "The green neon sign bled" | | 1 | "Dust motes drifted in the" | | 2 | "Silas Blackwood moved behind the" | | 3 | "His left knee clicked, a" | | 4 | "He ignored the ache, focusing" | | 5 | "The silver signet ring on" | | 6 | "The bell above the door" | | 7 | "A measured toll, not the" | | 8 | "The sound sliced through the" | | 9 | "Silas paused, the rag hovering" | | 10 | "A cadence he recognized from" | | 11 | "Silas straightened his spine until" | | 12 | "He looked up." | | 13 | "Maren stood in the halo" | | 14 | "Time had carved her down" | | 15 | "The curve that once softened" | | 16 | "Her hair, once a riot" | | 17 | "The scar tissue along her" | | 18 | "She wore a coat of" | | 19 | "Her voice held the raw" |
| | ratio | 0.93 | |
| 43.86% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 114 | | matches | | 0 | "Now it betrayed her with" |
| | ratio | 0.009 | |
| 66.87% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 47 | | technicalSentenceCount | 5 | | matches | | 0 | "Dust motes drifted in the shafts of light, swirling around the ankles of barstools that had absorbed the sweat and secrets of the Soho night." | | 1 | "Her hair, once a riot of auburn that defied regulation, was shorn close to the skull." | | 2 | "Silas noted the tremor in her left hand, a fine vibration that hadn't been there during the Prague drop." | | 3 | "He walked around the counter, his limp pronounced, a hitch in the step that marked the rhythm of his arrival." | | 4 | "Beneath the sharp angles and the tremors, he saw the girl who had stood under the rain in Cardiff, laughing as she tried to steal his lighter." |
| |
| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 2 | | matches | | 0 | "Maren wrapped, her knuckles whitening" | | 1 | "Maren's voice sharpened, the rust burning away to reveal the steel beneath" |
| |
| 98.72% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 2 | | fancyTags | | 0 | "Maren laughed (laugh)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 39 | | tagDensity | 0.077 | | leniency | 0.154 | | rawRatio | 0.667 | | effectiveRatio | 0.103 | |