| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1681 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 82.15% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1681 | | totalAiIsms | 6 | | found | | 0 | | | 1 | | word | "down her spine" | | count | 1 |
| | 2 | | | 3 | | | 4 | | | 5 | |
| | highlights | | 0 | "scanned" | | 1 | "down her spine" | | 2 | "weight" | | 3 | "silence" | | 4 | "echoed" | | 5 | "streaming" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 134 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 0 | | narrationSentences | 134 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 141 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 26 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1681 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 58 | | wordCount | 1639 | | uniqueNames | 15 | | maxNameDensity | 1.4 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Raven | 3 | | Nest | 3 | | Quinn | 23 | | Harlow | 1 | | Soho | 1 | | Tomás | 15 | | Herrera | 1 | | Saint | 1 | | Christopher | 1 | | Camden | 3 | | Morris | 2 | | Hackney | 1 | | Tube | 1 | | Veil | 1 | | Market | 1 |
| | persons | | 0 | "Raven" | | 1 | "Quinn" | | 2 | "Harlow" | | 3 | "Tomás" | | 4 | "Herrera" | | 5 | "Saint" | | 6 | "Christopher" | | 7 | "Morris" | | 8 | "Market" |
| | places | | 0 | "Soho" | | 1 | "Camden" | | 2 | "Hackney" | | 3 | "Veil" |
| | globalScore | 0.798 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 124 | | 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 | 1681 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 141 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 64.65 | | std | 50.32 | | cv | 0.778 | | sampleLengths | | 0 | 123 | | 1 | 103 | | 2 | 48 | | 3 | 4 | | 4 | 89 | | 5 | 93 | | 6 | 8 | | 7 | 99 | | 8 | 107 | | 9 | 6 | | 10 | 24 | | 11 | 9 | | 12 | 94 | | 13 | 100 | | 14 | 5 | | 15 | 13 | | 16 | 5 | | 17 | 112 | | 18 | 97 | | 19 | 150 | | 20 | 110 | | 21 | 5 | | 22 | 140 | | 23 | 111 | | 24 | 13 | | 25 | 13 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 134 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 260 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 141 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1643 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.010955569080949483 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.0006086427267194157 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 141 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 141 | | mean | 11.92 | | std | 5.05 | | cv | 0.423 | | sampleLengths | | 0 | 23 | | 1 | 21 | | 2 | 21 | | 3 | 20 | | 4 | 7 | | 5 | 8 | | 6 | 4 | | 7 | 19 | | 8 | 15 | | 9 | 14 | | 10 | 17 | | 11 | 8 | | 12 | 19 | | 13 | 19 | | 14 | 11 | | 15 | 13 | | 16 | 6 | | 17 | 12 | | 18 | 17 | | 19 | 4 | | 20 | 14 | | 21 | 17 | | 22 | 8 | | 23 | 14 | | 24 | 8 | | 25 | 10 | | 26 | 18 | | 27 | 10 | | 28 | 12 | | 29 | 13 | | 30 | 16 | | 31 | 13 | | 32 | 16 | | 33 | 13 | | 34 | 8 | | 35 | 12 | | 36 | 7 | | 37 | 10 | | 38 | 15 | | 39 | 9 | | 40 | 20 | | 41 | 9 | | 42 | 17 | | 43 | 19 | | 44 | 6 | | 45 | 11 | | 46 | 9 | | 47 | 15 | | 48 | 3 | | 49 | 10 |
| |
| 38.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 16 | | diversityRatio | 0.2624113475177305 | | totalSentences | 141 | | uniqueOpeners | 37 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 134 | | matches | (empty) | | ratio | 0 | |
| 79.70% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 47 | | totalSentences | 134 | | matches | | 0 | "Her cropped salt-and-pepper hair lay" | | 1 | "She counted the faces inside" | | 2 | "She counted the exits." | | 3 | "She checked the worn leather" | | 4 | "He wore a Saint Christopher" | | 5 | "He saw her on the" | | 6 | "He pushed out into the" | | 7 | "Her voice cut through traffic" | | 8 | "Her boots struck a drain" | | 9 | "She caught herself on a" | | 10 | "He slipped into the kitchen" | | 11 | "Her left wrist hit the" | | 12 | "She shouted the words and" | | 13 | "She had read the file" | | 14 | "He had taken the blade" | | 15 | "He crossed a side road" | | 16 | "Her chest burned." | | 17 | "Her boots filled with water" | | 18 | "She pulled her phone from" | | 19 | "She shoved the phone back" |
| | ratio | 0.351 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 127 | | totalSentences | 134 | | matches | | 0 | "Detective Harlow Quinn stood under" | | 1 | "The sign reflected in every" | | 2 | "Her cropped salt-and-pepper hair lay" | | 3 | "Brown eyes narrowed on the" | | 4 | "She counted the faces inside" | | 5 | "She counted the exits." | | 6 | "She checked the worn leather" | | 7 | "Silas poured a drink behind" | | 8 | "The bookshelf beside the toilets" | | 9 | "Quinn had watched the shelf" | | 10 | "The clique used the hidden" | | 11 | "He wore a Saint Christopher" | | 12 | "He saw her on the" | | 13 | "The bag slipped on his" | | 14 | "He pushed out into the" | | 15 | "Quinn left the eaves and" | | 16 | "Her voice cut through traffic" | | 17 | "Tomás cut through a gap" | | 18 | "Cardboard burst and oranges rolled" | | 19 | "Quinn vaulted the crates and" |
| | ratio | 0.948 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 134 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 93 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 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 | |