| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 7 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.137 | | leniency | 0.275 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1318 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 81.03% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1318 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "flicked" | | 1 | "footsteps" | | 2 | "tinge" | | 3 | "etched" |
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| 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 | 124 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 124 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 170 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 49 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1328 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 10 | | unquotedAttributions | 0 | | matches | (empty) | |
| 16.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 68 | | wordCount | 817 | | uniqueNames | 16 | | maxNameDensity | 2.57 | | worstName | "Quinn" | | maxWindowNameDensity | 4.5 | | worstWindowName | "Quinn" | | discoveredNames | | Northern | 1 | | Quinn | 21 | | Constable | 2 | | Tube | 1 | | Camden | 1 | | Town | 1 | | Davies | 8 | | Kowalski | 1 | | Eva | 14 | | Veil | 3 | | Compass | 2 | | Footsteps | 2 | | Anjali | 1 | | Patel | 8 | | Compasses | 1 | | Morris | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Constable" | | 2 | "Davies" | | 3 | "Kowalski" | | 4 | "Eva" | | 5 | "Footsteps" | | 6 | "Anjali" | | 7 | "Patel" | | 8 | "Morris" |
| | places | | 0 | "Tube" | | 1 | "Camden" | | 2 | "Town" | | 3 | "Compass" |
| | globalScore | 0.215 | | windowScore | 0.167 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 55 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.753 | | wordCount | 1328 | | matches | | 0 | "not north but directly at the man's open mouth" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 170 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 98 | | mean | 13.55 | | std | 12.26 | | cv | 0.905 | | sampleLengths | | 0 | 24 | | 1 | 6 | | 2 | 46 | | 3 | 1 | | 4 | 12 | | 5 | 44 | | 6 | 17 | | 7 | 14 | | 8 | 7 | | 9 | 2 | | 10 | 24 | | 11 | 4 | | 12 | 39 | | 13 | 21 | | 14 | 9 | | 15 | 25 | | 16 | 23 | | 17 | 19 | | 18 | 2 | | 19 | 4 | | 20 | 22 | | 21 | 3 | | 22 | 16 | | 23 | 31 | | 24 | 6 | | 25 | 33 | | 26 | 14 | | 27 | 4 | | 28 | 6 | | 29 | 3 | | 30 | 1 | | 31 | 2 | | 32 | 6 | | 33 | 30 | | 34 | 2 | | 35 | 4 | | 36 | 21 | | 37 | 19 | | 38 | 25 | | 39 | 2 | | 40 | 4 | | 41 | 2 | | 42 | 33 | | 43 | 3 | | 44 | 3 | | 45 | 1 | | 46 | 30 | | 47 | 11 | | 48 | 21 | | 49 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 124 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 137 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 0 | | flaggedSentences | 2 | | totalSentences | 170 | | ratio | 0.012 | | matches | | 0 | "Only two sets of footprints broke the dust around the body - Davies's boot prints and the paramedics'." | | 1 | "A faint mark on the tongue - not a tattoo - a sigil, charred into flesh, still smoking at the edge." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 606 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.0165016501650165 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 170 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 170 | | mean | 7.81 | | std | 7.84 | | cv | 1.004 | | sampleLengths | | 0 | 5 | | 1 | 19 | | 2 | 6 | | 3 | 13 | | 4 | 6 | | 5 | 27 | | 6 | 1 | | 7 | 2 | | 8 | 9 | | 9 | 1 | | 10 | 18 | | 11 | 8 | | 12 | 2 | | 13 | 5 | | 14 | 11 | | 15 | 8 | | 16 | 2 | | 17 | 7 | | 18 | 14 | | 19 | 7 | | 20 | 2 | | 21 | 24 | | 22 | 4 | | 23 | 39 | | 24 | 5 | | 25 | 1 | | 26 | 1 | | 27 | 2 | | 28 | 2 | | 29 | 2 | | 30 | 7 | | 31 | 1 | | 32 | 9 | | 33 | 3 | | 34 | 22 | | 35 | 4 | | 36 | 3 | | 37 | 4 | | 38 | 2 | | 39 | 10 | | 40 | 9 | | 41 | 10 | | 42 | 2 | | 43 | 4 | | 44 | 22 | | 45 | 3 | | 46 | 16 | | 47 | 3 | | 48 | 5 | | 49 | 2 |
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| 53.85% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3727810650887574 | | totalSentences | 169 | | uniqueOpeners | 63 | |
| 74.07% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 90 | | matches | | 0 | "Only two sets of footprints" | | 1 | "All pointed at the dead" |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 18 | | totalSentences | 90 | | matches | | 0 | "It smelled of ozone and" | | 1 | "Her worn leather watch caught" | | 2 | "She touched the back of" | | 3 | "Her gaze swept the platform." | | 4 | "Her hand went to her" | | 5 | "She did not touch the" | | 6 | "She studied the black eyes," | | 7 | "She took in Eva and" | | 8 | "She looked at the man" | | 9 | "His coat lay flat, no" | | 10 | "His shoes were clean." | | 11 | "She walked the perimeter, boots" | | 12 | "She pulled a pen from" | | 13 | "It fell into it." | | 14 | "It smelled of stone before" | | 15 | "She recognized the shape." | | 16 | "She had seen it photographed" | | 17 | "She kept her eyes on" |
| | ratio | 0.2 | |
| 32.22% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 77 | | totalSentences | 90 | | matches | | 0 | "The Northern line hummed overhead." | | 1 | "A train rattled past somewhere" | | 2 | "Harlow Quinn ducked under the" | | 3 | "The Constable on duty lifted" | | 4 | "The air down here bit" | | 5 | "It smelled of ozone and" | | 6 | "Her worn leather watch caught" | | 7 | "The crime scene spread across" | | 8 | "Strings of amber bulbs swung" | | 9 | "Something in a cage chittered." | | 10 | "Chalk sigils marked the concrete," | | 11 | "PC Davies hovered by the" | | 12 | "Davies flicked his eyes to" | | 13 | "Quinn crouched beside the victim." | | 14 | "She touched the back of" | | 15 | "Quinn tilted the head." | | 16 | "The eyelids lifted." | | 17 | "The irises were black." | | 18 | "A murmur rippled from the" | | 19 | "A woman in a patched" |
| | ratio | 0.856 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 90 | | matches | (empty) | | ratio | 0 | |
| 77.92% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 22 | | technicalSentenceCount | 2 | | matches | | 0 | "The casing showed a patina of verdigris at the edges, the face etched with protective sigils that caught the bulb light." | | 1 | "DC Anjali Patel came down the stairs, brisk, coat buttoned, already holding a coffee that steamed in the cold." |
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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 | 4 | | fancyCount | 1 | | fancyTags | | 0 | "Eva whispered (whisper)" |
| | dialogueSentences | 51 | | tagDensity | 0.078 | | leniency | 0.157 | | rawRatio | 0.25 | | effectiveRatio | 0.039 | |