| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 93.31% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1494 | | totalAiIsmAdverbs | 2 | | 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) | |
| 26.37% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1494 | | totalAiIsms | 22 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | |
| | highlights | | 0 | "chill" | | 1 | "grave" | | 2 | "pulsed" | | 3 | "chaotic" | | 4 | "measured" | | 5 | "footsteps" | | 6 | "standard" | | 7 | "flicked" | | 8 | "echoed" | | 9 | "weight" | | 10 | "etch" | | 11 | "tracing" | | 12 | "silence" | | 13 | "vibrated" | | 14 | "etched" | | 15 | "dance" | | 16 | "perfect" | | 17 | "traced" |
| |
| 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 | 1 | | narrationSentences | 150 | | matches | | |
| 95.24% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 4 | | hedgeCount | 1 | | narrationSentences | 150 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 150 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1494 | | 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 | 36 | | wordCount | 1494 | | uniqueNames | 10 | | maxNameDensity | 0.94 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 2 | | Quinn | 14 | | Tube | 1 | | Mills | 7 | | Kowalski | 1 | | Camden | 2 | | Compass | 1 | | Shade | 1 | | London | 1 | | Eva | 6 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Mills" | | 3 | "Kowalski" | | 4 | "Compass" | | 5 | "Eva" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 110 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1494 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 150 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 35.57 | | std | 22.18 | | cv | 0.623 | | sampleLengths | | 0 | 90 | | 1 | 76 | | 2 | 22 | | 3 | 16 | | 4 | 52 | | 5 | 55 | | 6 | 6 | | 7 | 12 | | 8 | 35 | | 9 | 56 | | 10 | 17 | | 11 | 16 | | 12 | 29 | | 13 | 12 | | 14 | 37 | | 15 | 81 | | 16 | 41 | | 17 | 34 | | 18 | 10 | | 19 | 21 | | 20 | 29 | | 21 | 43 | | 22 | 65 | | 23 | 7 | | 24 | 28 | | 25 | 57 | | 26 | 48 | | 27 | 33 | | 28 | 17 | | 29 | 22 | | 30 | 63 | | 31 | 30 | | 32 | 15 | | 33 | 39 | | 34 | 64 | | 35 | 9 | | 36 | 19 | | 37 | 64 | | 38 | 33 | | 39 | 2 | | 40 | 62 | | 41 | 27 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 150 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 235 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 150 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1496 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 37 | | adverbRatio | 0.024732620320855617 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.007352941176470588 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 150 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 150 | | mean | 9.96 | | std | 6.09 | | cv | 0.612 | | sampleLengths | | 0 | 17 | | 1 | 15 | | 2 | 22 | | 3 | 15 | | 4 | 21 | | 5 | 8 | | 6 | 17 | | 7 | 7 | | 8 | 25 | | 9 | 19 | | 10 | 4 | | 11 | 18 | | 12 | 3 | | 13 | 6 | | 14 | 7 | | 15 | 21 | | 16 | 3 | | 17 | 13 | | 18 | 15 | | 19 | 16 | | 20 | 30 | | 21 | 9 | | 22 | 1 | | 23 | 5 | | 24 | 12 | | 25 | 7 | | 26 | 12 | | 27 | 4 | | 28 | 12 | | 29 | 12 | | 30 | 11 | | 31 | 19 | | 32 | 14 | | 33 | 5 | | 34 | 12 | | 35 | 9 | | 36 | 7 | | 37 | 2 | | 38 | 8 | | 39 | 4 | | 40 | 15 | | 41 | 12 | | 42 | 1 | | 43 | 1 | | 44 | 11 | | 45 | 9 | | 46 | 11 | | 47 | 1 | | 48 | 3 | | 49 | 15 |
| |
| 71.11% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.4666666666666667 | | totalSentences | 150 | | uniqueOpeners | 70 | |
| 71.94% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 139 | | matches | | 0 | "Instead she stepped around the" | | 1 | "Only a handful exist." | | 2 | "Too many contradictions." |
| | ratio | 0.022 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 139 | | matches | | 0 | "Her boots sent ripples across" | | 1 | "She swept her flashlight beam" | | 2 | "She said nothing." | | 3 | "We have a dead man" | | 4 | "She turned it over in" | | 5 | "She adjusted her glasses with" | | 6 | "She stopped at the tunnel" | | 7 | "Its needle vibrated now, almost" | | 8 | "She swept her light across" | | 9 | "She pressed her palm flat" | | 10 | "Her colleague shifted his weight," | | 11 | "We've got a team coming" | | 12 | "They'll confirm my take." | | 13 | "She recalled the last case" | | 14 | "She turned the compass slowly," | | 15 | "It skittered across the platform" | | 16 | "We have a corpse with" | | 17 | "She lifted the edge with" | | 18 | "They know these markets." | | 19 | "They know how to force" |
| | ratio | 0.187 | |
| 67.91% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 109 | | totalSentences | 139 | | matches | | 0 | "Detective Harlow Quinn dropped from" | | 1 | "Her boots sent ripples across" | | 2 | "The platform stretched out like" | | 3 | "Vials of shimmering liquid mixed" | | 4 | "She swept her flashlight beam" | | 5 | "The body lay at the" | | 6 | "Quinn knelt, her movements crisp" | | 7 | "The gash across the victim's" | | 8 | "The skin had already taken" | | 9 | "Footsteps scraped behind her." | | 10 | "Detective Mills emerged from behind" | | 11 | "Throat slit for a bad" | | 12 | "Valuables stripped from the other" | | 13 | "Quinn straightened, her sharp jaw" | | 14 | "She said nothing." | | 15 | "The air carried traces of" | | 16 | "Eva Kowalski hovered at the" | | 17 | "The timing is all wrong." | | 18 | "Mills let out a short" | | 19 | "We have a dead man" |
| | ratio | 0.784 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 139 | | matches | (empty) | | ratio | 0 | |
| 88.74% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 66 | | technicalSentenceCount | 5 | | matches | | 0 | "Her boots sent ripples across a puddle that reflected the harsh glare of portable floodlights." | | 1 | "Forensics found fibers under the nails that match the stall canvas." | | 2 | "She recalled the last case that had ended in unexplained loss, her partner gone without a body or a clear motive." | | 3 | "The needle settled into a perfect line, trembling only slightly now as if satisfied." | | 4 | "It slid inward with a soft grind of stone on stone, revealing a narrow gap that breathed out colder air laced with the same ozone scent." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |