| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 14 | | adverbTagCount | 1 | | adverbTags | | 0 | "Silas stood abruptly [abruptly]" |
| | dialogueSentences | 41 | | tagDensity | 0.341 | | leniency | 0.683 | | rawRatio | 0.071 | | effectiveRatio | 0.049 | |
| 93.11% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1452 | | totalAiIsmAdverbs | 2 | | 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) | |
| 44.90% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1452 | | totalAiIsms | 16 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | word | "carried the weight" | | count | 1 |
| | 15 | |
| | highlights | | 0 | "throb" | | 1 | "familiar" | | 2 | "chill" | | 3 | "warmth" | | 4 | "flicked" | | 5 | "perfect" | | 6 | "whisper" | | 7 | "sanctuary" | | 8 | "pulse" | | 9 | "weight" | | 10 | "echoed" | | 11 | "shattered" | | 12 | "tension" | | 13 | "footsteps" | | 14 | "carried the weight" | | 15 | "flickered" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "knuckles turned white" | | count | 1 |
|
| | highlights | | |
| 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 | 0 | | hedgeCount | 1 | | narrationSentences | 102 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 127 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1452 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 0 | | matches | (empty) | |
| 53.94% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 47 | | wordCount | 989 | | uniqueNames | 12 | | maxNameDensity | 1.92 | | worstName | "Silas" | | maxWindowNameDensity | 3 | | worstWindowName | "Silas" | | discoveredNames | | Soho | 2 | | Vienna | 1 | | Whitehall | 1 | | Silas | 19 | | Cyrillic | 1 | | Prague | 1 | | Mara | 13 | | Raven | 1 | | Nest | 1 | | Tic-toc | 2 | | Director | 4 | | Rory | 1 |
| | persons | | 0 | "Silas" | | 1 | "Mara" | | 2 | "Raven" | | 3 | "Nest" | | 4 | "Director" | | 5 | "Rory" |
| | places | | 0 | "Soho" | | 1 | "Vienna" | | 2 | "Whitehall" | | 3 | "Prague" |
| | globalScore | 0.539 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | 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 | 1452 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 127 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 45 | | mean | 32.27 | | std | 21.51 | | cv | 0.667 | | sampleLengths | | 0 | 54 | | 1 | 7 | | 2 | 65 | | 3 | 58 | | 4 | 22 | | 5 | 63 | | 6 | 38 | | 7 | 23 | | 8 | 3 | | 9 | 54 | | 10 | 20 | | 11 | 52 | | 12 | 30 | | 13 | 56 | | 14 | 30 | | 15 | 73 | | 16 | 89 | | 17 | 17 | | 18 | 30 | | 19 | 46 | | 20 | 25 | | 21 | 5 | | 22 | 55 | | 23 | 37 | | 24 | 6 | | 25 | 44 | | 26 | 17 | | 27 | 46 | | 28 | 6 | | 29 | 58 | | 30 | 57 | | 31 | 9 | | 32 | 25 | | 33 | 16 | | 34 | 4 | | 35 | 39 | | 36 | 6 | | 37 | 16 | | 38 | 15 | | 39 | 48 | | 40 | 16 | | 41 | 28 | | 42 | 16 | | 43 | 21 | | 44 | 7 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 102 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 167 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 127 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 993 | | adjectiveStacks | 1 | | stackExamples | | 0 | "quick, jagged little moons" |
| | adverbCount | 19 | | adverbRatio | 0.019133937562940583 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.008056394763343404 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 127 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 127 | | mean | 11.43 | | std | 8.15 | | cv | 0.712 | | sampleLengths | | 0 | 6 | | 1 | 24 | | 2 | 24 | | 3 | 7 | | 4 | 3 | | 5 | 20 | | 6 | 18 | | 7 | 24 | | 8 | 14 | | 9 | 21 | | 10 | 9 | | 11 | 14 | | 12 | 4 | | 13 | 10 | | 14 | 8 | | 15 | 11 | | 16 | 6 | | 17 | 17 | | 18 | 16 | | 19 | 13 | | 20 | 21 | | 21 | 3 | | 22 | 14 | | 23 | 9 | | 24 | 6 | | 25 | 8 | | 26 | 3 | | 27 | 6 | | 28 | 12 | | 29 | 11 | | 30 | 13 | | 31 | 1 | | 32 | 11 | | 33 | 7 | | 34 | 13 | | 35 | 5 | | 36 | 3 | | 37 | 11 | | 38 | 22 | | 39 | 11 | | 40 | 6 | | 41 | 15 | | 42 | 9 | | 43 | 33 | | 44 | 7 | | 45 | 16 | | 46 | 5 | | 47 | 6 | | 48 | 19 | | 49 | 26 |
| |
| 46.72% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3228346456692913 | | totalSentences | 127 | | uniqueOpeners | 41 | |
| 71.68% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 93 | | matches | | 0 | "Only a hollow exhaustion that" | | 1 | "Just a symbol embossed in" |
| | ratio | 0.022 | |
| 90.97% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 30 | | totalSentences | 93 | | matches | | 0 | "He completed the pour, the" | | 1 | "His left knee gave a" | | 2 | "He gripped the mahogany rail," | | 3 | "Her hair, once a cascade" | | 4 | "He cleared his throat, forcing" | | 5 | "Her hands rested on the" | | 6 | "She no longer wore the" | | 7 | "He kept his eyes on" | | 8 | "She reached into her coat" | | 9 | "His movement was fluid, muscle" | | 10 | "She slid it across the" | | 11 | "It made a click-clack against" | | 12 | "He had watched it go" | | 13 | "He had watched the safehouse" | | 14 | "He had watched Mara walk" | | 15 | "She winced, the action sharp" | | 16 | "She set the glass down," | | 17 | "He had surrounded himself with" | | 18 | "She reached into her coat" | | 19 | "She placed a small, heavy" |
| | ratio | 0.323 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 86 | | totalSentences | 93 | | matches | | 0 | "Silas's hand froze over the" | | 1 | "The amber stream of whiskey" | | 2 | "A voice cut through the" | | 3 | "Silas didn't turn." | | 4 | "He completed the pour, the" | | 5 | "His left knee gave a" | | 6 | "He gripped the mahogany rail," | | 7 | "The woman wore a charcoal" | | 8 | "Her hair, once a cascade" | | 9 | "The only thing that remained" | | 10 | "Hazels, sharp as cut glass," | | 11 | "Silas's voice rasped" | | 12 | "He cleared his throat, forcing" | | 13 | "Mara leaned forward" | | 14 | "Her hands rested on the" | | 15 | "The knuckles were scarred, the" | | 16 | "She no longer wore the" | | 17 | "He kept his eyes on" | | 18 | "Mara's gaze flicked to the" | | 19 | "A muscle jumped in her" |
| | ratio | 0.925 | |
| 53.76% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 93 | | matches | | 0 | "Now the past had walked" |
| | ratio | 0.011 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 38 | | technicalSentenceCount | 1 | | matches | | 0 | "She no longer wore the manicured claws that used to tap impatiently against files in Whitehall." |
| |
| 53.57% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 14 | | uselessAdditionCount | 2 | | matches | | 0 | "She set, the condensation leaving a perfect circle of moisture" | | 1 | "Silas stood abruptly, his stool screeching against the floorboards" |
| |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 41 | | tagDensity | 0.049 | | leniency | 0.098 | | rawRatio | 0.5 | | effectiveRatio | 0.049 | |