| 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 | 1391 | | 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) | |
| 60.46% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1391 | | totalAiIsms | 11 | | found | | | highlights | | 0 | "silence" | | 1 | "fluttered" | | 2 | "traced" | | 3 | "gloom" | | 4 | "familiar" | | 5 | "echoed" | | 6 | "rhythmic" | | 7 | "echo" | | 8 | "flickered" |
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| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "knuckles turned white" | | count | 1 |
| | 2 | | label | "air was thick with" | | count | 1 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "knuckles turned white" | | 2 | "The air was thick with" |
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| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 257 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 257 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 257 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1390 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 0 | | unquotedAttributions | 0 | | matches | (empty) | |
| 60.07% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 65 | | wordCount | 1390 | | uniqueNames | 13 | | maxNameDensity | 1.8 | | worstName | "Harlow" | | maxWindowNameDensity | 3 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Kowalski | 1 | | Morris | 4 | | Veil | 3 | | Compass | 1 | | Market | 4 | | Rite | 1 | | Unbinding | 1 | | Harlow | 25 | | Eva | 13 | | You | 7 | | Look | 3 |
| | persons | | 0 | "Kowalski" | | 1 | "Morris" | | 2 | "Market" | | 3 | "Harlow" | | 4 | "Eva" | | 5 | "You" |
| | places | | | globalScore | 0.601 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 94 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like a child's drawing of a star," |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.719 | | wordCount | 1390 | | matches | | 0 | "not walking, but gliding" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 257 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 70 | | mean | 19.86 | | std | 17.2 | | cv | 0.866 | | sampleLengths | | 0 | 56 | | 1 | 3 | | 2 | 33 | | 3 | 9 | | 4 | 52 | | 5 | 3 | | 6 | 24 | | 7 | 67 | | 8 | 4 | | 9 | 6 | | 10 | 17 | | 11 | 21 | | 12 | 62 | | 13 | 4 | | 14 | 35 | | 15 | 38 | | 16 | 5 | | 17 | 21 | | 18 | 43 | | 19 | 23 | | 20 | 2 | | 21 | 1 | | 22 | 42 | | 23 | 7 | | 24 | 38 | | 25 | 24 | | 26 | 3 | | 27 | 6 | | 28 | 4 | | 29 | 28 | | 30 | 54 | | 31 | 9 | | 32 | 20 | | 33 | 2 | | 34 | 19 | | 35 | 5 | | 36 | 4 | | 37 | 40 | | 38 | 14 | | 39 | 34 | | 40 | 1 | | 41 | 12 | | 42 | 21 | | 43 | 8 | | 44 | 38 | | 45 | 5 | | 46 | 6 | | 47 | 36 | | 48 | 2 | | 49 | 37 |
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| 98.44% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 257 | | matches | | 0 | "were dragged" | | 1 | "were lined" | | 2 | "is excluded" | | 3 | "is gone" | | 4 | "was shrouded" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 274 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 257 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1391 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.02228612508986341 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.006470165348670022 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 257 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 257 | | mean | 5.41 | | std | 3.72 | | cv | 0.688 | | sampleLengths | | 0 | 12 | | 1 | 15 | | 2 | 10 | | 3 | 19 | | 4 | 3 | | 5 | 5 | | 6 | 17 | | 7 | 11 | | 8 | 3 | | 9 | 2 | | 10 | 2 | | 11 | 2 | | 12 | 3 | | 13 | 9 | | 14 | 27 | | 15 | 13 | | 16 | 3 | | 17 | 3 | | 18 | 8 | | 19 | 4 | | 20 | 4 | | 21 | 5 | | 22 | 3 | | 23 | 17 | | 24 | 5 | | 25 | 3 | | 26 | 6 | | 27 | 9 | | 28 | 13 | | 29 | 11 | | 30 | 4 | | 31 | 6 | | 32 | 4 | | 33 | 5 | | 34 | 8 | | 35 | 2 | | 36 | 5 | | 37 | 7 | | 38 | 7 | | 39 | 4 | | 40 | 16 | | 41 | 3 | | 42 | 9 | | 43 | 6 | | 44 | 4 | | 45 | 5 | | 46 | 15 | | 47 | 3 | | 48 | 1 | | 49 | 2 |
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| 45.14% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 24 | | diversityRatio | 0.32684824902723736 | | totalSentences | 257 | | uniqueOpeners | 84 | |
| 80.91% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 206 | | matches | | 0 | "Only the outline and the" | | 1 | "Just the drip of water" | | 2 | "Then it is the clique." | | 3 | "Only the dark track ahead" | | 4 | "Too many limbs." |
| | ratio | 0.024 | |
| 91.84% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 66 | | totalSentences | 206 | | matches | | 0 | "She held it up to" | | 1 | "She adjusted her round glasses," | | 2 | "It is ash." | | 3 | "Her knees popped in the" | | 4 | "You see the pattern?" | | 5 | "It is not graffiti." | | 6 | "It is a containment ward." | | 7 | "She had seen this before." | | 8 | "She holstered her torch and" | | 9 | "It does not like this" | | 10 | "It likes the truth." | | 11 | "You said this market moves" | | 12 | "It requires a bone token" | | 13 | "You used Morris's token, did" | | 14 | "She walked toward the centre" | | 15 | "She ran a gloved hand" | | 16 | "She traced the pattern of" | | 17 | "They led toward the tunnel," | | 18 | "They walked into the dark." | | 19 | "They were dragged." |
| | ratio | 0.32 | |
| 57.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 166 | | totalSentences | 206 | | matches | | 0 | "Harlow scraped the black residue" | | 1 | "The substance flaked away like" | | 2 | "She held it up to" | | 3 | "The light caught the particulate" | | 4 | "This isn't soot." | | 5 | "Eva Kowalski shifted behind her." | | 6 | "The leather of her satchel" | | 7 | "She adjusted her round glasses," | | 8 | "It is ash." | | 9 | "Harlow stood up." | | 10 | "Her knees popped in the" | | 11 | "The station platform stretched into" | | 12 | "The air tasted of copper" | | 13 | "Eva pointed a trembling finger" | | 14 | "You see the pattern?" | | 15 | "It is not graffiti." | | 16 | "It is a containment ward." | | 17 | "Harlow stepped closer." | | 18 | "The marking looked like a" | | 19 | "She had seen this before." |
| | ratio | 0.806 | |
| 97.09% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 4 | | totalSentences | 206 | | matches | | 0 | "To the sigil." | | 1 | "If this is a ritual" | | 2 | "If the ward is broken," | | 3 | "If you hold it, you" |
| | ratio | 0.019 | |
| 96.77% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 31 | | technicalSentenceCount | 2 | | matches | | 0 | "The light caught the particulate matter, revealing a shimmer that defied the grime of the abandoned Camden Tube station." | | 1 | "Just the drip of water and the hum of energy that made her teeth ache." |
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| 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 | |