| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 3 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 824 | | 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) | |
| 45.39% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 824 | | totalAiIsms | 9 | | found | | | highlights | | 0 | "rhythmic" | | 1 | "loomed" | | 2 | "footsteps" | | 3 | "echoed" | | 4 | "footfall" | | 5 | "velvet" | | 6 | "pulsed" | | 7 | "flickered" |
| |
| 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 | 56 | | matches | (empty) | |
| 91.84% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 56 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 57 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 35 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 824 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 812 | | uniqueNames | 13 | | maxNameDensity | 0.99 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Quinn" | | discoveredNames | | Greek | 1 | | Street | 1 | | Mondeo | 1 | | Glock | 3 | | Raven | 1 | | Nest | 1 | | Soho | 1 | | Camden | 1 | | Northern | 1 | | Line | 1 | | Tube | 1 | | Morris | 1 | | Quinn | 8 |
| | persons | | 0 | "Glock" | | 1 | "Raven" | | 2 | "Morris" | | 3 | "Quinn" |
| | places | | 0 | "Greek" | | 1 | "Street" | | 2 | "Soho" | | 3 | "Camden" | | 4 | "Northern" |
| | globalScore | 1 | | windowScore | 1 | |
| 95.65% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 46 | | glossingSentenceCount | 1 | | matches | | 0 | "feathers that seemed to move without wind" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 824 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 57 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 24 | | mean | 34.33 | | std | 30.29 | | cv | 0.882 | | sampleLengths | | 0 | 80 | | 1 | 72 | | 2 | 101 | | 3 | 3 | | 4 | 58 | | 5 | 17 | | 6 | 18 | | 7 | 61 | | 8 | 58 | | 9 | 2 | | 10 | 11 | | 11 | 97 | | 12 | 56 | | 13 | 4 | | 14 | 41 | | 15 | 19 | | 16 | 9 | | 17 | 26 | | 18 | 18 | | 19 | 4 | | 20 | 34 | | 21 | 17 | | 22 | 6 | | 23 | 12 |
| |
| 99.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 56 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 134 | | matches | (empty) | |
| 92.73% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 1 | | flaggedSentences | 1 | | totalSentences | 57 | | ratio | 0.018 | | matches | | 0 | "Military precision was not a choice anymore; it lived in her bones, the only rhythm her body trusted when the hunt turned live." |
| |
| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 816 | | adjectiveStacks | 2 | | stackExamples | | 0 | "small leather-bound package," | | 1 | "cold pressed hard against her" |
| | adverbCount | 24 | | adverbRatio | 0.029411764705882353 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.004901960784313725 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 57 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 57 | | mean | 14.46 | | std | 8.65 | | cv | 0.599 | | sampleLengths | | 0 | 9 | | 1 | 14 | | 2 | 32 | | 3 | 25 | | 4 | 25 | | 5 | 24 | | 6 | 23 | | 7 | 17 | | 8 | 16 | | 9 | 27 | | 10 | 15 | | 11 | 26 | | 12 | 3 | | 13 | 7 | | 14 | 21 | | 15 | 11 | | 16 | 19 | | 17 | 2 | | 18 | 8 | | 19 | 7 | | 20 | 18 | | 21 | 16 | | 22 | 18 | | 23 | 17 | | 24 | 2 | | 25 | 8 | | 26 | 19 | | 27 | 13 | | 28 | 15 | | 29 | 11 | | 30 | 2 | | 31 | 11 | | 32 | 10 | | 33 | 26 | | 34 | 35 | | 35 | 12 | | 36 | 14 | | 37 | 18 | | 38 | 25 | | 39 | 13 | | 40 | 4 | | 41 | 30 | | 42 | 11 | | 43 | 4 | | 44 | 15 | | 45 | 6 | | 46 | 3 | | 47 | 3 | | 48 | 4 | | 49 | 19 |
| |
| 75.44% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.47368421052631576 | | totalSentences | 57 | | uniqueOpeners | 27 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 53 | | matches | | 0 | "Then he vanished." | | 1 | "Then she saw him." | | 2 | "Then the lanterns died." |
| | ratio | 0.057 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 13 | | totalSentences | 53 | | matches | | 0 | "She had burned three weeks" | | 1 | "Her right hand stayed near" | | 2 | "She had first spotted him" | | 3 | "She had followed him north" | | 4 | "His footsteps echoed below, rapid" | | 5 | "She descended two steps at" | | 6 | "Her boots echoed now, a" | | 7 | "She had chased one running" | | 8 | "He handed her a small" | | 9 | "She stepped from shadow, her" | | 10 | "she called, sharp and clear" | | 11 | "She was no longer the" | | 12 | "She was the bait, and" |
| | ratio | 0.245 | |
| 73.21% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 41 | | totalSentences | 53 | | matches | | 0 | "Quinn's boots struck wet cobblestones" | | 1 | "Rain sheeted across her face," | | 2 | "The hooded figure ahead had" | | 3 | "She had burned three weeks" | | 4 | "Her right hand stayed near" | | 5 | "The worn leather watch on" | | 6 | "Military precision was not a" | | 7 | "The suspect rounded a corner" | | 8 | "Quinn pushed harder, her trench" | | 9 | "She had first spotted him" | | 10 | "She had followed him north" | | 11 | "The next he dropped into" | | 12 | "Quinn skidded to a halt," | | 13 | "The gap was barely shoulder-width," | | 14 | "Water dripped somewhere deep with" | | 15 | "His footsteps echoed below, rapid" | | 16 | "Quinn thumbed the safety on" | | 17 | "Iron stairs spiralled downward, bolted" | | 18 | "She descended two steps at" | | 19 | "The air shifted with every" |
| | ratio | 0.774 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 53 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 11 | | matches | | 0 | "The hooded figure ahead had gained fifteen metres since turning off Greek Street, weaving through overturned bins and parked delivery scooters with the panic of…" | | 1 | "Her right hand stayed near her hip, where her service Glock rested in leather that had moulded to her shape over eighteen years of duty." | | 2 | "She had followed him north through streets that grew rougher, away from tourists and into Camden's skeletal industrial spine, where shuttered warehouses loomed …" | | 3 | "The gap was barely shoulder-width, and it led downward into a darkness that smelled of rust and dead electricity." | | 4 | "Iron stairs spiralled downward, bolted to crumbling concrete, into blackness that swallowed the rain's noise entirely." | | 5 | "Her boots echoed now, a sharp click-clack that announced her presence, but she pressed on." | | 6 | "Merchants in long dark coats haggled over goods spread across cloth, enchanted amulets that pulsed with faint light, vials of liquid that glowed sick blue, bund…" | | 7 | "Quinn pressed against a tiled pillar, hidden in shadow, her heart hammering with a violence that surprised her." | | 8 | "This was the territory she had suspected, the hidden heart that linked back to DS Morris, dead three years in circumstances that still refused logic." | | 9 | "The hooded figure stood near the centre, speaking with a tall woman whose hair shone like polished silver, her coat woven from black feathers that seemed to mov…" | | 10 | "And her suspect pulled back his hood to reveal eyes that were entirely black, reflecting her like dark mirrors." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | 0 | "a voice whispered (whisper)" |
| | dialogueSentences | 3 | | tagDensity | 0.667 | | leniency | 1 | | rawRatio | 0.5 | | effectiveRatio | 0.5 | |