| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.25 | | leniency | 0.5 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 95.47% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1104 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 32.07% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1104 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "familiar" | | 1 | "weight" | | 2 | "oppressive" | | 3 | "flickered" | | 4 | "gloom" | | 5 | "wavered" | | 6 | "scanning" | | 7 | "glint" | | 8 | "etched" | | 9 | "pulse" | | 10 | "quickened" | | 11 | "macabre" | | 12 | "raced" | | 13 | "pulsed" | | 14 | "whisper" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "hung in the air" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 120 | | matches | (empty) | |
| 95.24% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 3 | | narrationSentences | 120 | | filterMatches | | | hedgeMatches | | |
| 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 | 1097 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 73.08% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 35 | | wordCount | 910 | | uniqueNames | 10 | | maxNameDensity | 1.54 | | worstName | "Whitaker" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Whitaker" | | discoveredNames | | Tube | 1 | | Camden | 1 | | Harlow | 1 | | Quinn | 7 | | Tom | 1 | | Whitaker | 14 | | Morris | 5 | | Veil | 2 | | Market | 2 | | Met | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Tom" | | 4 | "Whitaker" | | 5 | "Morris" | | 6 | "Met" |
| | places | (empty) | | globalScore | 0.731 | | windowScore | 0.833 | |
| 79.58% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 71 | | glossingSentenceCount | 2 | | matches | | 0 | "lines that seemed to shift under the light" | | 1 | "as if reacting to her presence" |
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| 17.68% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 2 | | per1kWords | 1.823 | | wordCount | 1097 | | matches | | 0 | "not in a fist, but as if he’d been clutching something" | | 1 | "Not in any case file, but in Morris’s notes" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 141 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 25.51 | | std | 17.43 | | cv | 0.683 | | sampleLengths | | 0 | 61 | | 1 | 62 | | 2 | 1 | | 3 | 37 | | 4 | 34 | | 5 | 36 | | 6 | 5 | | 7 | 7 | | 8 | 46 | | 9 | 60 | | 10 | 5 | | 11 | 21 | | 12 | 11 | | 13 | 50 | | 14 | 6 | | 15 | 58 | | 16 | 6 | | 17 | 29 | | 18 | 28 | | 19 | 15 | | 20 | 27 | | 21 | 9 | | 22 | 27 | | 23 | 8 | | 24 | 37 | | 25 | 40 | | 26 | 13 | | 27 | 32 | | 28 | 12 | | 29 | 33 | | 30 | 47 | | 31 | 5 | | 32 | 24 | | 33 | 39 | | 34 | 13 | | 35 | 15 | | 36 | 30 | | 37 | 8 | | 38 | 8 | | 39 | 43 | | 40 | 10 | | 41 | 24 | | 42 | 15 |
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| 87.72% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 120 | | matches | | 0 | "been tossed" | | 1 | "was torn" | | 2 | "were curled" | | 3 | "been placed" | | 4 | "was etched" | | 5 | "been flipped" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 160 | | matches | (empty) | |
| 61.80% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 7 | | semicolonCount | 0 | | flaggedSentences | 4 | | totalSentences | 141 | | ratio | 0.028 | | matches | | 0 | "The face was etched with symbols she didn’t recognise—sharp, angular lines that seemed to shift under the light." | | 1 | "And the symbols—faint, but there—carved into the grout." | | 2 | "And this—this token—was the key to getting in." | | 3 | "A whisper of sound—voices, maybe, or the rustle of something alive—drifted out." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 923 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 25 | | adverbRatio | 0.027085590465872156 | | lyAdverbCount | 2 | | lyAdverbRatio | 0.0021668472372697724 | |
| 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 | 7.78 | | std | 5.02 | | cv | 0.646 | | sampleLengths | | 0 | 12 | | 1 | 15 | | 2 | 14 | | 3 | 20 | | 4 | 13 | | 5 | 12 | | 6 | 23 | | 7 | 9 | | 8 | 2 | | 9 | 3 | | 10 | 1 | | 11 | 3 | | 12 | 14 | | 13 | 11 | | 14 | 9 | | 15 | 9 | | 16 | 10 | | 17 | 6 | | 18 | 9 | | 19 | 6 | | 20 | 16 | | 21 | 4 | | 22 | 1 | | 23 | 6 | | 24 | 3 | | 25 | 5 | | 26 | 2 | | 27 | 5 | | 28 | 5 | | 29 | 14 | | 30 | 5 | | 31 | 5 | | 32 | 5 | | 33 | 3 | | 34 | 9 | | 35 | 8 | | 36 | 11 | | 37 | 10 | | 38 | 7 | | 39 | 18 | | 40 | 6 | | 41 | 3 | | 42 | 2 | | 43 | 3 | | 44 | 11 | | 45 | 7 | | 46 | 4 | | 47 | 7 | | 48 | 16 | | 49 | 8 |
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| 39.36% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 15 | | diversityRatio | 0.2907801418439716 | | totalSentences | 141 | | uniqueOpeners | 41 | |
| 93.46% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 107 | | matches | | 0 | "Just a man, lying as" | | 1 | "Just like the entry requirement" | | 2 | "Then she saw it." |
| | ratio | 0.028 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 29 | | totalSentences | 107 | | matches | | 0 | "She adjusted the worn leather" | | 1 | "His coat was torn, one" | | 2 | "She didn’t turn." | | 3 | "His torch wavered, the light" | | 4 | "She pried them open." | | 5 | "She stood, scanning the platform." | | 6 | "Her torch caught a glint" | | 7 | "She picked it up, turning" | | 8 | "She snapped the compass shut" | | 9 | "She moved to the edge" | | 10 | "She’d seen these markings before." | | 11 | "She’d read them, though." | | 12 | "He knelt, patting the man" | | 13 | "He pulled out a small," | | 14 | "She took it, turning it" | | 15 | "She didn’t answer." | | 16 | "It was the message." | | 17 | "She crouched beside the victim" | | 18 | "She stood, her voice low." | | 19 | "She gestured to the sigil" |
| | ratio | 0.271 | |
| 62.80% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 85 | | totalSentences | 107 | | matches | | 0 | "The abandoned Tube station beneath" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "The air hung thick, the" | | 3 | "She adjusted the worn leather" | | 4 | "A single bulb flickered overhead," | | 5 | "The beam of her torch" | | 6 | "A body lay sprawled near" | | 7 | "His coat was torn, one" | | 8 | "She didn’t turn." | | 9 | "The voice belonged to DS" | | 10 | "Whitaker was solid, dependable, but" | | 11 | "Whitaker said, stepping beside her" | | 12 | "His torch wavered, the light" | | 13 | "Quinn crouched, her sharp jaw" | | 14 | "The man’s fingers were curled," | | 15 | "She pried them open." | | 16 | "She stood, scanning the platform." | | 17 | "The station was a relic," | | 18 | "The body was too clean." | | 19 | "Her torch caught a glint" |
| | ratio | 0.794 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 107 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 2 | | matches | | 0 | "A single bulb flickered overhead, casting long shadows that danced like restless spirits." | | 1 | "The face was etched with symbols she didn’t recognise—sharp, angular lines that seemed to shift under the light." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 7 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 28 | | tagDensity | 0.071 | | leniency | 0.143 | | rawRatio | 0 | | effectiveRatio | 0 | |