| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 92.23% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1287 | | 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) | |
| 41.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1287 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "tracing" | | 1 | "echoing" | | 2 | "stark" | | 3 | "electric" | | 4 | "weight" | | 5 | "methodical" | | 6 | "flicker" | | 7 | "constructed" | | 8 | "silence" | | 9 | "beacon" | | 10 | "echoed" | | 11 | "pulse" | | 12 | "resolve" |
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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 | 1 | | narrationSentences | 110 | | matches | | |
| 90.91% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 3 | | hedgeCount | 1 | | narrationSentences | 110 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 110 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1278 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1278 | | uniqueNames | 15 | | maxNameDensity | 0.78 | | worstName | "Tomás" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Camden | 1 | | Quinn | 3 | | Herrera | 1 | | Detective | 2 | | Northern | 1 | | Line | 1 | | Saint | 1 | | Christopher | 1 | | Veil | 1 | | Market | 1 | | Morris | 1 | | Tube | 1 | | Tomás | 10 | | Harlow | 5 | | You | 4 |
| | persons | | 0 | "Quinn" | | 1 | "Herrera" | | 2 | "Saint" | | 3 | "Christopher" | | 4 | "Market" | | 5 | "Morris" | | 6 | "Tomás" | | 7 | "Harlow" | | 8 | "You" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 93.18% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 88 | | glossingSentenceCount | 2 | | matches | | 0 | "felt like the night DS Morris vanished" | | 1 | "felt like lead" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.782 | | wordCount | 1278 | | matches | | 0 | "not of her, but of what she had allowed into the room with her presence" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 110 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 26 | | mean | 49.15 | | std | 32.5 | | cv | 0.661 | | sampleLengths | | 0 | 80 | | 1 | 13 | | 2 | 77 | | 3 | 76 | | 4 | 12 | | 5 | 68 | | 6 | 43 | | 7 | 9 | | 8 | 85 | | 9 | 15 | | 10 | 67 | | 11 | 73 | | 12 | 11 | | 13 | 88 | | 14 | 8 | | 15 | 58 | | 16 | 11 | | 17 | 77 | | 18 | 18 | | 19 | 79 | | 20 | 8 | | 21 | 88 | | 22 | 12 | | 23 | 79 | | 24 | 27 | | 25 | 96 |
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| 95.69% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 110 | | matches | | 0 | "buried" | | 1 | "been injured" | | 2 | "was terrified" |
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| 24.56% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 6 | | totalVerbs | 228 | | matches | | 0 | "was fleeing" | | 1 | "wasn’t just running" | | 2 | "was bartering" | | 3 | "were waking" | | 4 | "wasn't lying" | | 5 | "were losing" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 9 | | semicolonCount | 3 | | flaggedSentences | 9 | | totalSentences | 110 | | ratio | 0.082 | | matches | | 0 | "Tomás Herrera wasn’t just running; he was fleeing." | | 1 | "He pressed a smooth, ivory-colored object into a hollow indentation in the frame—a bone token." | | 2 | "She caught a glimpse of him—the Saint Christopher medallion catching the dim light, swinging wildly against his chest." | | 3 | "Beyond that door, the sounds of voices—too many, too fluid, layered like a choir gargling shards—drifted up." | | 4 | "The dust here felt different—gritty, like crushed gemstones mixed with ash." | | 5 | "She checked her weapon; the slide clicked back, smooth and reliable." | | 6 | "Most didn't look human; they wore shadows like garments, their faces obscured by shifting geometry." | | 7 | "He started to turn, but a massive shape—a shadow detached from the tunnel wall—slithered between them." | | 8 | "A hundred pairs of eyes—some yellow, some slit-pupiled, some nothing more than voids—turned toward her." |
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| 88.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1296 | | adjectiveStacks | 2 | | stackExamples | | 0 | "slick, ink-black mirrors." | | 1 | "heavy, silver-threaded bandages." |
| | adverbCount | 27 | | adverbRatio | 0.020833333333333332 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.004629629629629629 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 110 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 110 | | mean | 11.62 | | std | 5.64 | | cv | 0.485 | | sampleLengths | | 0 | 14 | | 1 | 11 | | 2 | 15 | | 3 | 20 | | 4 | 12 | | 5 | 8 | | 6 | 4 | | 7 | 9 | | 8 | 10 | | 9 | 15 | | 10 | 4 | | 11 | 19 | | 12 | 29 | | 13 | 12 | | 14 | 15 | | 15 | 15 | | 16 | 18 | | 17 | 16 | | 18 | 12 | | 19 | 16 | | 20 | 15 | | 21 | 15 | | 22 | 22 | | 23 | 12 | | 24 | 18 | | 25 | 13 | | 26 | 3 | | 27 | 6 | | 28 | 9 | | 29 | 17 | | 30 | 4 | | 31 | 20 | | 32 | 18 | | 33 | 17 | | 34 | 4 | | 35 | 4 | | 36 | 7 | | 37 | 8 | | 38 | 11 | | 39 | 11 | | 40 | 14 | | 41 | 12 | | 42 | 11 | | 43 | 15 | | 44 | 11 | | 45 | 23 | | 46 | 13 | | 47 | 11 | | 48 | 11 | | 49 | 3 |
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| 30.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 22 | | diversityRatio | 0.2909090909090909 | | totalSentences | 110 | | uniqueOpeners | 32 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 109 | | matches | (empty) | | ratio | 0 | |
| 7.16% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 58 | | totalSentences | 109 | | matches | | 0 | "Her salt-and-pepper hair plastered against" | | 1 | "She recognized the frantic gait," | | 2 | "You have no idea what’s" | | 3 | "She vaulted a low brick" | | 4 | "She ignored the warning." | | 5 | "She watched him slide through" | | 6 | "She vaulted the railing, ignoring" | | 7 | "Her boots struck metal stairs," | | 8 | "She descended past the reach" | | 9 | "He clawed at a heavy" | | 10 | "He pressed a smooth, ivory-colored" | | 11 | "She caught a glimpse of" | | 12 | "He vanished into the light," | | 13 | "She stood at the precipice" | | 14 | "It pulled at her." | | 15 | "It felt like the night" | | 16 | "She knelt, pressing her fingers" | | 17 | "She spotted a smear of" | | 18 | "She followed the blood trail," | | 19 | "She checked her weapon; the" |
| | ratio | 0.532 | |
| 47.16% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 90 | | totalSentences | 109 | | matches | | 0 | "Harlow Quinn planted her heels," | | 1 | "Her salt-and-pepper hair plastered against" | | 2 | "She recognized the frantic gait," | | 3 | "Tomás Herrera wasn’t just running;" | | 4 | "You have no idea what’s" | | 5 | "Harlow didn't respond, her hand" | | 6 | "She vaulted a low brick" | | 7 | "She ignored the warning." | | 8 | "She watched him slide through" | | 9 | "She vaulted the railing, ignoring" | | 10 | "Her boots struck metal stairs," | | 11 | "The air shifted down here," | | 12 | "She descended past the reach" | | 13 | "Tomás stood at the mouth" | | 14 | "He clawed at a heavy" | | 15 | "He pressed a smooth, ivory-colored" | | 16 | "The metal door hissed, vibrating" | | 17 | "Harlow pulled her watch from" | | 18 | "She caught a glimpse of" | | 19 | "He vanished into the light," |
| | ratio | 0.826 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 109 | | matches | | 0 | "If she turned back, she" | | 1 | "If she stayed, she entered" | | 2 | "If you don't talk, I" |
| | ratio | 0.028 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 68 | | technicalSentenceCount | 2 | | matches | | 0 | "It felt like the night DS Morris vanished, the same hum of static that raised the hair on her arms." | | 1 | "If she stayed, she entered a jurisdiction that didn't recognize her badge, her laws, or her life." |
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| 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 | |