| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 89.34% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2345 | | totalAiIsmAdverbs | 5 | | found | | | highlights | | 0 | "slightly" | | 1 | "perfectly" | | 2 | "suddenly" | | 3 | "quickly" |
| |
| 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) | |
| 50.96% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2345 | | totalAiIsms | 23 | | found | | | highlights | | 0 | "shattered" | | 1 | "fractured" | | 2 | "standard" | | 3 | "traced" | | 4 | "pulse" | | 5 | "etched" | | 6 | "weight" | | 7 | "trembled" | | 8 | "disrupted" | | 9 | "gloom" | | 10 | "resolved" | | 11 | "vibrated" | | 12 | "whisper" | | 13 | "echoed" | | 14 | "familiar" | | 15 | "flickered" |
| |
| 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 | 419 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 3 | | narrationSentences | 419 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 419 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 19 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2345 | | ratio | 0 | | matches | (empty) | |
| 83.33% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 6 | | unquotedAttributions | 1 | | matches | | 0 | "Below, Briggs screamed." |
| |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 82 | | wordCount | 2345 | | uniqueNames | 18 | | maxNameDensity | 1.32 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 3 | | High | 2 | | Street | 1 | | Met | 1 | | Morris | 3 | | British | 1 | | Museum | 1 | | Briggs | 14 | | Singapore | 1 | | Quinn | 31 | | Harlow | 1 | | Blood | 3 | | Wrong | 3 | | You | 4 | | Cold | 3 | | One | 3 | | Do | 4 | | Eyes | 3 |
| | persons | | 0 | "Morris" | | 1 | "Museum" | | 2 | "Briggs" | | 3 | "Quinn" | | 4 | "Harlow" | | 5 | "Blood" | | 6 | "You" | | 7 | "Eyes" |
| | places | | 0 | "Camden" | | 1 | "High" | | 2 | "Street" | | 3 | "Met" | | 4 | "British" | | 5 | "Singapore" | | 6 | "Cold" |
| | globalScore | 0.839 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 187 | | glossingSentenceCount | 2 | | matches | | 0 | "sounded like ground bone" | | 1 | "sounded like wet leather" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2345 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 419 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 68 | | mean | 34.49 | | std | 22.27 | | cv | 0.646 | | sampleLengths | | 0 | 50 | | 1 | 51 | | 2 | 23 | | 3 | 17 | | 4 | 41 | | 5 | 9 | | 6 | 22 | | 7 | 74 | | 8 | 19 | | 9 | 29 | | 10 | 63 | | 11 | 13 | | 12 | 21 | | 13 | 81 | | 14 | 45 | | 15 | 30 | | 16 | 48 | | 17 | 69 | | 18 | 21 | | 19 | 60 | | 20 | 8 | | 21 | 33 | | 22 | 55 | | 23 | 9 | | 24 | 51 | | 25 | 15 | | 26 | 7 | | 27 | 8 | | 28 | 62 | | 29 | 29 | | 30 | 37 | | 31 | 4 | | 32 | 27 | | 33 | 56 | | 34 | 5 | | 35 | 57 | | 36 | 61 | | 37 | 22 | | 38 | 60 | | 39 | 6 | | 40 | 65 | | 41 | 23 | | 42 | 1 | | 43 | 37 | | 44 | 60 | | 45 | 31 | | 46 | 8 | | 47 | 62 | | 48 | 56 | | 49 | 59 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 419 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 444 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 419 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 2345 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 48 | | adverbRatio | 0.02046908315565032 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.007249466950959489 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 419 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 419 | | mean | 5.6 | | std | 3.15 | | cv | 0.563 | | sampleLengths | | 0 | 12 | | 1 | 11 | | 2 | 10 | | 3 | 3 | | 4 | 2 | | 5 | 6 | | 6 | 6 | | 7 | 9 | | 8 | 10 | | 9 | 11 | | 10 | 2 | | 11 | 3 | | 12 | 16 | | 13 | 16 | | 14 | 7 | | 15 | 3 | | 16 | 5 | | 17 | 5 | | 18 | 4 | | 19 | 10 | | 20 | 4 | | 21 | 3 | | 22 | 7 | | 23 | 4 | | 24 | 7 | | 25 | 6 | | 26 | 5 | | 27 | 4 | | 28 | 4 | | 29 | 3 | | 30 | 10 | | 31 | 5 | | 32 | 4 | | 33 | 7 | | 34 | 8 | | 35 | 3 | | 36 | 6 | | 37 | 3 | | 38 | 10 | | 39 | 8 | | 40 | 9 | | 41 | 16 | | 42 | 6 | | 43 | 1 | | 44 | 5 | | 45 | 7 | | 46 | 9 | | 47 | 3 | | 48 | 5 | | 49 | 7 |
| |
| 46.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 35 | | diversityRatio | 0.3317422434367542 | | totalSentences | 419 | | uniqueOpeners | 139 | |
| 55.25% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 6 | | totalSentences | 362 | | matches | | 0 | "Just dripping water and a" | | 1 | "Then the throat cut happened" | | 2 | "Probably stole it from the" | | 3 | "Too many limbs." | | 4 | "Only wet tearing remained." | | 5 | "Only heavy breathing remained." |
| | ratio | 0.017 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 103 | | totalSentences | 362 | | matches | | 0 | "He tapped his notebook against" | | 1 | "She traced the arc with" | | 2 | "He stood behind him and" | | 3 | "He turned, took the hit," | | 4 | "She pointed to the victims" | | 5 | "She leaned closer." | | 6 | "Her watch creaked as she" | | 7 | "She noted the verdigris staining" | | 8 | "He took a stab here" | | 9 | "You kill and you leave." | | 10 | "It was a display." | | 11 | "She tapped the glass face." | | 12 | "It refused to settle." | | 13 | "You want to tell me" | | 14 | "She turned the compass over." | | 15 | "She had seen that symbol" | | 16 | "She never closed the file." | | 17 | "She just buried it under" | | 18 | "She scraped a sample of" | | 19 | "It smelled of ozone and" |
| | ratio | 0.285 | |
| 64.97% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 286 | | totalSentences | 362 | | matches | | 0 | "Boots crunched on shattered glass" | | 1 | "Quinn stepped under the yellow" | | 2 | "The victim lay face down" | | 3 | "Throat slit from ear to" | | 4 | "The blood told a different" | | 5 | "The room smelled of wet" | | 6 | "Water dripped from a fractured" | | 7 | "This derelict telegraph exchange sat" | | 8 | "Briggs stood near the entrance," | | 9 | "He tapped his notebook against" | | 10 | "Blood drained into the grating." | | 11 | "Looks like a message." | | 12 | "Quinn ran a gloved finger" | | 13 | "She traced the arc with" | | 14 | "The angle was wrong." | | 15 | "The killer did not stand" | | 16 | "He stood behind him and" | | 17 | "Briggs clicked his tongue." | | 18 | "He turned, took the hit," | | 19 | "Gang tactics are getting creative." |
| | ratio | 0.79 | |
| 13.81% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 362 | | matches | | 0 | "Because this was not a" |
| | ratio | 0.003 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 2 | | matches | | 0 | "Briggs stood near the entrance, holding a torch that threw long shadows against the peeling plaster." | | 1 | "The same suitcase that contained a ledger full of impossible dates and a vial of black ash." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |