| 51.85% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 2 | | adverbTags | | 0 | "the case files described only [only]" | | 1 | "Tomás said quietly [quietly]" |
| | dialogueSentences | 27 | | tagDensity | 0.407 | | leniency | 0.815 | | rawRatio | 0.182 | | effectiveRatio | 0.148 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 907 | | 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) | |
| 72.44% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 907 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "echoed" | | 1 | "footsteps" | | 2 | "flicked" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 109 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 109 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 125 | | 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 | 904 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.40% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 753 | | uniqueNames | 17 | | maxNameDensity | 1.99 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Raven | 1 | | Nest | 1 | | London | 1 | | Camden | 1 | | High | 1 | | Street | 1 | | Tube | 1 | | Morris | 1 | | Quinn | 15 | | Veil | 1 | | Market | 1 | | Footsteps | 1 | | Saint | 1 | | Christopher | 1 | | Herrera | 1 | | Tomás | 7 |
| | persons | | 0 | "Raven" | | 1 | "Morris" | | 2 | "Quinn" | | 3 | "Footsteps" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Herrera" | | 7 | "Tomás" |
| | places | | 0 | "Soho" | | 1 | "London" | | 2 | "Camden" | | 3 | "High" | | 4 | "Street" |
| | globalScore | 0.504 | | windowScore | 0.667 | |
| 60.71% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 56 | | glossingSentenceCount | 2 | | matches | | 0 | "Something like ozone and rotting flowers" | | 1 | "felt like ice and static electricity" |
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| 89.38% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 1.106 | | wordCount | 904 | | matches | | 0 | "Not this exact station, but somewhere beneath the city, somewhere the case files describ" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 125 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 21.02 | | std | 15.01 | | cv | 0.714 | | sampleLengths | | 0 | 59 | | 1 | 36 | | 2 | 41 | | 3 | 7 | | 4 | 27 | | 5 | 6 | | 6 | 54 | | 7 | 7 | | 8 | 51 | | 9 | 28 | | 10 | 19 | | 11 | 21 | | 12 | 18 | | 13 | 13 | | 14 | 21 | | 15 | 3 | | 16 | 36 | | 17 | 11 | | 18 | 12 | | 19 | 4 | | 20 | 38 | | 21 | 2 | | 22 | 24 | | 23 | 24 | | 24 | 19 | | 25 | 35 | | 26 | 10 | | 27 | 40 | | 28 | 8 | | 29 | 16 | | 30 | 8 | | 31 | 31 | | 32 | 9 | | 33 | 18 | | 34 | 15 | | 35 | 10 | | 36 | 34 | | 37 | 12 | | 38 | 9 | | 39 | 13 | | 40 | 47 | | 41 | 4 | | 42 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 109 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 122 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 125 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 755 | | adjectiveStacks | 1 | | stackExamples | | 0 | "ahead, bent double, gasping." |
| | adverbCount | 19 | | adverbRatio | 0.025165562913907286 | | lyAdverbCount | 9 | | lyAdverbRatio | 0.011920529801324504 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 125 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 125 | | mean | 7.23 | | std | 4.93 | | cv | 0.682 | | sampleLengths | | 0 | 14 | | 1 | 21 | | 2 | 12 | | 3 | 12 | | 4 | 17 | | 5 | 3 | | 6 | 1 | | 7 | 15 | | 8 | 8 | | 9 | 2 | | 10 | 15 | | 11 | 11 | | 12 | 4 | | 13 | 1 | | 14 | 7 | | 15 | 6 | | 16 | 6 | | 17 | 2 | | 18 | 1 | | 19 | 12 | | 20 | 6 | | 21 | 5 | | 22 | 10 | | 23 | 27 | | 24 | 7 | | 25 | 5 | | 26 | 7 | | 27 | 2 | | 28 | 11 | | 29 | 14 | | 30 | 6 | | 31 | 18 | | 32 | 9 | | 33 | 8 | | 34 | 9 | | 35 | 2 | | 36 | 4 | | 37 | 7 | | 38 | 8 | | 39 | 3 | | 40 | 1 | | 41 | 2 | | 42 | 7 | | 43 | 8 | | 44 | 9 | | 45 | 9 | | 46 | 8 | | 47 | 5 | | 48 | 7 | | 49 | 6 |
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| 56.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.384 | | totalSentences | 125 | | uniqueOpeners | 48 | |
| 37.04% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 90 | | matches | | 0 | "Then the screaming started." |
| | ratio | 0.011 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 90 | | matches | | 0 | "Her military training kept her" | | 1 | "He was wounded." | | 2 | "She gained three metres." | | 3 | "He reached the abandoned Tube" | | 4 | "She paused at the threshold." | | 5 | "Her partner Morris had died" | | 6 | "She'd reviewed those photographs for" | | 7 | "She knew what waited underground." | | 8 | "Her boots struck concrete in" | | 9 | "He pressed one hand against" | | 10 | "Her voice echoed off the" | | 11 | "His face was pale beneath" | | 12 | "He laughed, a wet, painful" | | 13 | "She'd heard whispers during her" | | 14 | "He wore surgical gloves stained" | | 15 | "His warm brown eyes flicked" | | 16 | "It glowed sickly green." | | 17 | "Her breath misted." | | 18 | "It felt like ice and" | | 19 | "She lunged forward, dragging the" |
| | ratio | 0.233 | |
| 37.78% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 76 | | totalSentences | 90 | | matches | | 0 | "The runner's blood left a" | | 1 | "Quinn tracked it across the" | | 2 | "Her military training kept her" | | 3 | "He was wounded." | | 4 | "The metallic scent mixing with" | | 5 | "The suspect ducked left onto" | | 6 | "The late-night crowd had thinned" | | 7 | "Quinn cut across the road," | | 8 | "She gained three metres." | | 9 | "He reached the abandoned Tube" | | 10 | "Quinn's hand moved to her" | | 11 | "The station had closed in" | | 12 | "The runner vanished into the" | | 13 | "She paused at the threshold." | | 14 | "Her partner Morris had died" | | 15 | "She'd reviewed those photographs for" | | 16 | "She knew what waited underground." | | 17 | "The blood trail continued down" | | 18 | "Her boots struck concrete in" | | 19 | "The air turned colder, thick" |
| | ratio | 0.844 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 23 | | technicalSentenceCount | 1 | | matches | | 0 | "Her boots struck concrete in a rhythm that matched her heartbeat." |
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| 34.09% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 2 | | matches | | 0 | "Quinn advanced, her weapon steady" | | 1 | "Tomás moved, his scarred left forearm visible as he raised his hands" |
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| 38.89% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 3 | | fancyTags | | 0 | "the case files described only (describe)" | | 1 | "He laughed (laugh)" | | 2 | "the runner whispered (whisper)" |
| | dialogueSentences | 27 | | tagDensity | 0.185 | | leniency | 0.37 | | rawRatio | 0.6 | | effectiveRatio | 0.222 | |