| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 12 | | adverbTagCount | 1 | | adverbTags | | | dialogueSentences | 37 | | tagDensity | 0.324 | | leniency | 0.649 | | rawRatio | 0.083 | | effectiveRatio | 0.054 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1924 | | 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) | |
| 100.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1924 | | totalAiIsms | 0 | | found | (empty) | | highlights | (empty) | |
| 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 | 133 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 133 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 158 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 67 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1924 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 1 | | matches | | 0 | "The bone token in the evidence bag had been in his pocket, Hale said." |
| |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 40 | | wordCount | 1332 | | uniqueNames | 7 | | maxNameDensity | 1.2 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Hale" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Hale | 12 | | Quinn | 16 | | Eva | 8 | | Met | 1 | | Morris | 1 |
| | persons | | 0 | "Hale" | | 1 | "Quinn" | | 2 | "Eva" | | 3 | "Met" | | 4 | "Morris" |
| | places | | | globalScore | 0.899 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 92 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.52 | | wordCount | 1924 | | matches | | 0 | "not north but at the tunnel mouth, rock-steady" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 158 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 52 | | mean | 37 | | std | 25.54 | | cv | 0.69 | | sampleLengths | | 0 | 62 | | 1 | 48 | | 2 | 50 | | 3 | 41 | | 4 | 5 | | 5 | 20 | | 6 | 55 | | 7 | 58 | | 8 | 11 | | 9 | 55 | | 10 | 44 | | 11 | 4 | | 12 | 49 | | 13 | 4 | | 14 | 89 | | 15 | 2 | | 16 | 12 | | 17 | 28 | | 18 | 65 | | 19 | 12 | | 20 | 74 | | 21 | 61 | | 22 | 25 | | 23 | 5 | | 24 | 59 | | 25 | 80 | | 26 | 53 | | 27 | 1 | | 28 | 4 | | 29 | 3 | | 30 | 8 | | 31 | 40 | | 32 | 58 | | 33 | 78 | | 34 | 12 | | 35 | 7 | | 36 | 6 | | 37 | 13 | | 38 | 31 | | 39 | 67 | | 40 | 28 | | 41 | 66 | | 42 | 56 | | 43 | 12 | | 44 | 83 | | 45 | 23 | | 46 | 49 | | 47 | 43 | | 48 | 49 | | 49 | 38 |
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| 97.35% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 133 | | matches | | 0 | "been called" | | 1 | "been packed" | | 2 | "been used" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 186 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 158 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1335 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.01348314606741573 | | lyAdverbCount | 1 | | lyAdverbRatio | 0.000749063670411985 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 158 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 158 | | mean | 12.18 | | std | 10.45 | | cv | 0.858 | | sampleLengths | | 0 | 11 | | 1 | 17 | | 2 | 9 | | 3 | 25 | | 4 | 6 | | 5 | 27 | | 6 | 8 | | 7 | 2 | | 8 | 5 | | 9 | 8 | | 10 | 4 | | 11 | 13 | | 12 | 20 | | 13 | 5 | | 14 | 10 | | 15 | 13 | | 16 | 9 | | 17 | 9 | | 18 | 5 | | 19 | 16 | | 20 | 4 | | 21 | 6 | | 22 | 21 | | 23 | 4 | | 24 | 7 | | 25 | 17 | | 26 | 39 | | 27 | 19 | | 28 | 4 | | 29 | 7 | | 30 | 5 | | 31 | 50 | | 32 | 2 | | 33 | 20 | | 34 | 11 | | 35 | 11 | | 36 | 4 | | 37 | 18 | | 38 | 31 | | 39 | 2 | | 40 | 2 | | 41 | 20 | | 42 | 21 | | 43 | 23 | | 44 | 3 | | 45 | 2 | | 46 | 7 | | 47 | 13 | | 48 | 2 | | 49 | 5 |
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| 46.62% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 13 | | diversityRatio | 0.3291139240506329 | | totalSentences | 158 | | uniqueOpeners | 52 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 122 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 31 | | totalSentences | 122 | | matches | | 0 | "Her leather watch sat tight" | | 1 | "He looked up as her" | | 2 | "She ducked the tape" | | 3 | "She followed the platform past" | | 4 | "Her torch found a woman" | | 5 | "She tucked a curl behind" | | 6 | "She set the ledger on" | | 7 | "She reached into the satchel" | | 8 | "She stopped, glanced at the" | | 9 | "She knelt again." | | 10 | "It had sat and it" | | 11 | "She lifted the edge of" | | 12 | "She checked again." | | 13 | "He came over." | | 14 | "She sat back on her" | | 15 | "She went to the stall" | | 16 | "She did not pick it" | | 17 | "She looked at the table." | | 18 | "She stepped closer." | | 19 | "It steamed, and the steam" |
| | ratio | 0.254 | |
| 17.38% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 122 | | matches | | 0 | "The stairwell beneath Camden stank" | | 1 | "Quinn took the steps two" | | 2 | "Her leather watch sat tight" | | 3 | "Water ticked somewhere in the" | | 4 | "The beam found a rusted" | | 5 | "Someone had pulled them up." | | 6 | "Stalls occupied the platform in" | | 7 | "Canvas, bone, tarnished brass." | | 8 | "Jars lined one table, their" | | 9 | "A rack of coats hung" | | 10 | "Quinn kept the torch moving." | | 11 | "DS Hale crouched over a" | | 12 | "He looked up as her" | | 13 | "She ducked the tape" | | 14 | "Hale rocked back on his" | | 15 | "A man lay on his" | | 16 | "Wool coat, good shoes, no" | | 17 | "A dark bloom spread from" | | 18 | "Hale held up an evidence" | | 19 | "Quinn took the bag." |
| | ratio | 0.885 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 122 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 1 | | matches | | 0 | "She followed the platform past a table of stoppered bottles and a cage that held nothing she wanted to name." |
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| 83.33% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 12 | | uselessAdditionCount | 1 | | matches | | 0 | "Hale stood, knees cracking" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 37 | | tagDensity | 0.081 | | leniency | 0.162 | | rawRatio | 0 | | effectiveRatio | 0 | |