| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 11 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said softly [softly]" |
| | dialogueSentences | 25 | | tagDensity | 0.44 | | leniency | 0.88 | | rawRatio | 0.091 | | effectiveRatio | 0.08 | |
| 85.74% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1052 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | 0 | "sharply" | | 1 | "sweetly" | | 2 | "softly" |
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| 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) | |
| 9.70% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1052 | | totalAiIsms | 19 | | found | | | highlights | | 0 | "silk" | | 1 | "weight" | | 2 | "familiar" | | 3 | "jaw clenched" | | 4 | "etched" | | 5 | "flicked" | | 6 | "echo" | | 7 | "footsteps" | | 8 | "flicker" | | 9 | "silence" | | 10 | "porcelain" | | 11 | "glinting" | | 12 | "flickered" | | 13 | "pulsed" | | 14 | "echoed" | | 15 | "measured" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "jaw/fists clenched" | | count | 1 |
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| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 103 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 103 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 117 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 45 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 16 | | markdownWords | 29 | | totalWords | 1067 | | ratio | 0.027 | | matches | | 0 | "The Raven’s Nest" | | 1 | "“They’re not human. It wasn’t real.”" | | 2 | "stench" | | 3 | "restricted" | | 4 | "this" | | 5 | "The Veil Market." | | 6 | "Delusion," | | 7 | "Recognition." | | 8 | "ours" | | 9 | "your" | | 10 | "The Raven’s Nest" | | 11 | "this" | | 12 | "him" | | 13 | "saving" | | 14 | "bled" | | 15 | "The Raven’s Nest" |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 13 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 31 | | wordCount | 862 | | uniqueNames | 14 | | maxNameDensity | 0.81 | | worstName | "Quinn" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Soho | 1 | | Harlow | 2 | | Quinn | 7 | | Raven | 2 | | Morris | 4 | | Tomás | 6 | | Herrera | 1 | | Tube | 1 | | Veil | 1 | | Internal | 1 | | Affairs | 1 | | Saint | 1 | | Christopher | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Tomás" | | 5 | "Herrera" | | 6 | "Affairs" | | 7 | "Saint" | | 8 | "Christopher" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 57 | | 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 | 1067 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 117 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 42 | | mean | 25.4 | | std | 22.39 | | cv | 0.881 | | sampleLengths | | 0 | 84 | | 1 | 78 | | 2 | 64 | | 3 | 15 | | 4 | 9 | | 5 | 2 | | 6 | 60 | | 7 | 19 | | 8 | 6 | | 9 | 87 | | 10 | 3 | | 11 | 47 | | 12 | 22 | | 13 | 28 | | 14 | 6 | | 15 | 19 | | 16 | 4 | | 17 | 4 | | 18 | 27 | | 19 | 13 | | 20 | 39 | | 21 | 16 | | 22 | 15 | | 23 | 8 | | 24 | 23 | | 25 | 46 | | 26 | 30 | | 27 | 10 | | 28 | 16 | | 29 | 50 | | 30 | 25 | | 31 | 39 | | 32 | 10 | | 33 | 40 | | 34 | 21 | | 35 | 13 | | 36 | 10 | | 37 | 10 | | 38 | 3 | | 39 | 31 | | 40 | 4 | | 41 | 11 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 103 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 150 | | matches | | 0 | "was chasing " | | 1 | "was thinning" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 17 | | semicolonCount | 0 | | flaggedSentences | 15 | | totalSentences | 117 | | ratio | 0.128 | | matches | | 0 | "She barely registered the cold — only the figure ahead, darting between umbrellas and traffic, a smudge of dark fabric disappearing into the alley beside *The Raven’s Nest*." | | 1 | "Three years since Morris bled out in a basement that reeked of copper and burnt sage, whispering words that made no sense — *“They’re not human. It wasn’t real.”* And now this." | | 2 | "The man — Tomás Herrera, according to the warrant flagged *restricted* — turned sharply, coat flaring, and disappeared through a rusted metal door half-hidden beneath a tarp." | | 3 | "Quinn reached it, shoved — it gave with a groan." | | 4 | "The air smelled wrong — old earth, ozone, and something sweetly rotten, like overripe fruit left in a cellar." | | 5 | "The corridor opened into a cavernous space — an old Tube station, swallowed by time." | | 6 | "Figures moved in silence — cloaked, masked, some with eyes too large, too bright." | | 7 | "And the man she was chasing — Tomás — was just ahead, slipping through a curtained archway toward a stall stacked with old medical texts and copper instruments." | | 8 | "No face—just a smooth porcelain mask painted with a weeping eye." | | 9 | "Tomás was now speaking urgently with a stall vendor — a hunched woman with silver eyes." | | 10 | "A scar ran along his forearm — fresh, red, not yet healed." | | 11 | "Something flickered in his eyes — grief?" | | 12 | "A low rumble echoed through the tunnels — not trains." | | 13 | "She’d seen the photos — the symbols carved into the floor." | | 14 | "From the tunnels ahead, a whispering hum — like voices speaking in reverse." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 848 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 22 | | adverbRatio | 0.025943396226415096 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.009433962264150943 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 117 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 117 | | mean | 9.12 | | std | 7.81 | | cv | 0.856 | | sampleLengths | | 0 | 20 | | 1 | 21 | | 2 | 15 | | 3 | 28 | | 4 | 3 | | 5 | 18 | | 6 | 2 | | 7 | 32 | | 8 | 7 | | 9 | 6 | | 10 | 10 | | 11 | 10 | | 12 | 27 | | 13 | 10 | | 14 | 17 | | 15 | 9 | | 16 | 6 | | 17 | 2 | | 18 | 7 | | 19 | 2 | | 20 | 22 | | 21 | 19 | | 22 | 8 | | 23 | 2 | | 24 | 5 | | 25 | 4 | | 26 | 8 | | 27 | 9 | | 28 | 2 | | 29 | 6 | | 30 | 15 | | 31 | 5 | | 32 | 14 | | 33 | 22 | | 34 | 14 | | 35 | 8 | | 36 | 9 | | 37 | 3 | | 38 | 7 | | 39 | 1 | | 40 | 1 | | 41 | 5 | | 42 | 4 | | 43 | 1 | | 44 | 28 | | 45 | 11 | | 46 | 8 | | 47 | 3 | | 48 | 3 | | 49 | 11 |
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| 66.10% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.452991452991453 | | totalSentences | 117 | | uniqueOpeners | 53 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 82 | | matches | | 0 | "Only the echo of footsteps," | | 1 | "Then he unhooked the medallion" | | 2 | "Then, with measured steps, Detective" |
| | ratio | 0.037 | |
| 98.05% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 25 | | totalSentences | 82 | | matches | | 0 | "Her leather watch dug into" | | 1 | "She barely registered the cold" | | 2 | "She vaulted over a discarded" | | 3 | "She drew her service weapon," | | 4 | "Her phone had lost signal" | | 5 | "She advanced, low and steady," | | 6 | "She rounded a corner and" | | 7 | "She knew the name from" | | 8 | "She grabbed a hooded figure" | | 9 | "it whispered in two overlapping" | | 10 | "She shoved the figure aside." | | 11 | "She remembered the file." | | 12 | "She didn’t have one." | | 13 | "He reached for a small," | | 14 | "he said, voice low but" | | 15 | "she said, stepping forward" | | 16 | "She kept her gun raised" | | 17 | "He laughed, short and bitter." | | 18 | "Her finger hovered over the" | | 19 | "His bones had turned black" |
| | ratio | 0.305 | |
| 63.66% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 82 | | matches | | 0 | "Rain slashed sideways through the" | | 1 | "Detective Harlow Quinn cut through" | | 2 | "Her leather watch dug into" | | 3 | "She barely registered the cold" | | 4 | "Quinn didn’t slow." | | 5 | "She vaulted over a discarded" | | 6 | "The same symbols etched into" | | 7 | "The same whispers in back" | | 8 | "The same goddamn *stench* of" | | 9 | "The alley narrowed, reeking of" | | 10 | "The man — Tomás Herrera," | | 11 | "Quinn reached it, shoved —" | | 12 | "A narrow staircase plunged down" | | 13 | "She drew her service weapon," | | 14 | "The stairs ended in a" | | 15 | "The air smelled wrong —" | | 16 | "Her phone had lost signal" | | 17 | "A flicker of movement at" | | 18 | "She advanced, low and steady," | | 19 | "She rounded a corner and" |
| | ratio | 0.793 | |
| 60.98% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 82 | | matches | | 0 | "Before she could react, he" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 33 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 79.55% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 11 | | uselessAdditionCount | 1 | | matches | | 0 | "he said, voice low but clear" |
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| 30.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 9 | | fancyCount | 3 | | fancyTags | | 0 | "it whispered (whisper)" | | 1 | "Quinn shouted (shout)" | | 2 | "he muttered (mutter)" |
| | dialogueSentences | 25 | | tagDensity | 0.36 | | leniency | 0.72 | | rawRatio | 0.333 | | effectiveRatio | 0.24 | |