| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 18 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 49 | | tagDensity | 0.367 | | leniency | 0.735 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 86.83% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1139 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (16 tracked, −20% each) | | codexExemptions | | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 38.54% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1139 | | totalAiIsms | 14 | | found | | | highlights | | 0 | "familiar" | | 1 | "flickered" | | 2 | "scanning" | | 3 | "intensity" | | 4 | "etched" | | 5 | "weight" | | 6 | "measured" | | 7 | "silence" | | 8 | "absolutely" | | 9 | "traced" |
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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 | 121 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 121 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 154 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 3 | | markdownWords | 3 | | totalWords | 1131 | | ratio | 0.003 | | matches | | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 20 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 29 | | wordCount | 866 | | uniqueNames | 14 | | maxNameDensity | 0.92 | | worstName | "Silas" | | maxWindowNameDensity | 2 | | worstWindowName | "Silas" | | discoveredNames | | Raven | 1 | | Nest | 1 | | Tottenham | 1 | | October | 1 | | Sybil | 1 | | Soho | 1 | | Silas | 8 | | Blackwood | 1 | | Rory | 5 | | Tom | 4 | | God | 1 | | Cardiff | 2 | | Polish | 1 | | Tim | 1 |
| | persons | | 0 | "Raven" | | 1 | "Nest" | | 2 | "Sybil" | | 3 | "Silas" | | 4 | "Blackwood" | | 5 | "Rory" | | 6 | "Tom" | | 7 | "Tim" |
| | places | | 0 | "Tottenham" | | 1 | "Soho" | | 2 | "Cardiff" | | 3 | "Polish" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 45 | | 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 | 1131 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 154 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 74 | | mean | 15.28 | | std | 16.07 | | cv | 1.051 | | sampleLengths | | 0 | 86 | | 1 | 6 | | 2 | 56 | | 3 | 1 | | 4 | 74 | | 5 | 39 | | 6 | 21 | | 7 | 3 | | 8 | 9 | | 9 | 46 | | 10 | 4 | | 11 | 4 | | 12 | 3 | | 13 | 18 | | 14 | 6 | | 15 | 9 | | 16 | 18 | | 17 | 14 | | 18 | 5 | | 19 | 22 | | 20 | 6 | | 21 | 4 | | 22 | 27 | | 23 | 7 | | 24 | 6 | | 25 | 6 | | 26 | 14 | | 27 | 8 | | 28 | 11 | | 29 | 23 | | 30 | 13 | | 31 | 8 | | 32 | 71 | | 33 | 11 | | 34 | 6 | | 35 | 14 | | 36 | 3 | | 37 | 12 | | 38 | 10 | | 39 | 4 | | 40 | 7 | | 41 | 7 | | 42 | 14 | | 43 | 5 | | 44 | 16 | | 45 | 6 | | 46 | 24 | | 47 | 20 | | 48 | 27 | | 49 | 6 |
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| 99.46% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 121 | | matches | | 0 | "being summoned" | | 1 | "was crooked" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 155 | | matches | | |
| 12.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 7 | | totalSentences | 154 | | ratio | 0.045 | | matches | | 0 | "The Raven’s Nest hummed with that familiar low-grade chaos—the clink of glasses, the murmur of low conversation, the occasional burst of laughter from the corner where a couple of regulars debated Tottenham’s chances this season." | | 1 | "Not years later, not here, not now—definitely not in Soho, not in Silas Blackwood’s territory." | | 2 | "The auburn streaks in his hair had sharpened to iron, the limp in his left leg just a slight alteration to his posture—like he now carried the past in the way he supported his weight." | | 3 | "His gaze flickered over her—shoulder-length black hair, bright blue eyes that still held the sharpness of a blade, a scar on her wrist like a secret." | | 4 | "The bartender, a wiry man named Tom, glanced their way, then away—sensing the temperature drop." | | 5 | "She looked at him then, really looked—at the lines around his eyes, the way his beard framed a jaw that had once been square and now stood like a weathered cliff." | | 6 | "A couple at the end of the bar started arguing in Polish—sharp, fast, dissolving into nervous laughter." |
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| 97.95% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 614 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 26 | | adverbRatio | 0.04234527687296417 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.009771986970684038 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 154 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 154 | | mean | 7.34 | | std | 6.92 | | cv | 0.942 | | sampleLengths | | 0 | 35 | | 1 | 18 | | 2 | 33 | | 3 | 6 | | 4 | 3 | | 5 | 15 | | 6 | 38 | | 7 | 1 | | 8 | 19 | | 9 | 35 | | 10 | 20 | | 11 | 2 | | 12 | 9 | | 13 | 3 | | 14 | 15 | | 15 | 3 | | 16 | 7 | | 17 | 4 | | 18 | 17 | | 19 | 3 | | 20 | 9 | | 21 | 9 | | 22 | 26 | | 23 | 3 | | 24 | 3 | | 25 | 5 | | 26 | 4 | | 27 | 2 | | 28 | 2 | | 29 | 2 | | 30 | 1 | | 31 | 2 | | 32 | 1 | | 33 | 15 | | 34 | 6 | | 35 | 3 | | 36 | 6 | | 37 | 9 | | 38 | 9 | | 39 | 2 | | 40 | 7 | | 41 | 5 | | 42 | 3 | | 43 | 2 | | 44 | 3 | | 45 | 9 | | 46 | 10 | | 47 | 6 | | 48 | 4 | | 49 | 1 |
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| 64.94% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 10 | | diversityRatio | 0.42857142857142855 | | totalSentences | 154 | | uniqueOpeners | 66 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 5 | | totalSentences | 89 | | matches | | 0 | "Just exhaled again, shallow, measured." | | 1 | "Instead, she said," | | 2 | "Just a little." | | 3 | "Just a crumpled photo." | | 4 | "Just an inch." |
| | ratio | 0.056 | |
| 44.72% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 39 | | totalSentences | 89 | | matches | | 0 | "She didn’t expect to see" | | 1 | "His face had etched deeper" | | 2 | "His signet ring glinted under" | | 3 | "She could leave." | | 4 | "She could turn, step back" | | 5 | "He exhaled through his nose," | | 6 | "She walked over." | | 7 | "He looked up when she" | | 8 | "His gaze flickered over her—shoulder—length" | | 9 | "He didn’t smile." | | 10 | "She almost laughed." | | 11 | "He chuckled, low and rough," | | 12 | "She’d forgotten how he used" | | 13 | "She wrapped her hands around" | | 14 | "He swirled his own drink" | | 15 | "She looked at him then," | | 16 | "He wasn’t the same man" | | 17 | "He wasn’t the man who’d" | | 18 | "He was older." | | 19 | "She said nothing." |
| | ratio | 0.438 | |
| 77.98% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 89 | | matches | | 0 | "The Raven’s Nest hummed with" | | 1 | "The green neon sign outside" | | 2 | "Rory pushed through the door," | | 3 | "She didn’t expect to see" | | 4 | "His face had etched deeper" | | 5 | "The auburn streaks in his" | | 6 | "His signet ring glinted under" | | 7 | "The door swung shut behind" | | 8 | "She could leave." | | 9 | "She could turn, step back" | | 10 | "Something older than caution rooted" | | 11 | "Silas didn’t turn immediately." | | 12 | "He exhaled through his nose," | | 13 | "She walked over." | | 14 | "The floorboards creaked underfoot, each" | | 15 | "He looked up when she" | | 16 | "His gaze flickered over her—shoulder—length" | | 17 | "He didn’t smile." | | 18 | "The bartender, a wiry man" | | 19 | "She almost laughed." |
| | ratio | 0.764 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 89 | | matches | (empty) | | ratio | 0 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 3 | | matches | | 0 | "But there he was, leaning against the far end of the bar, one elbow resting on the polished mahogany, his hazel eyes scanning the room with the quiet intensity …" | | 1 | "He wasn’t the man who’d slipped her five hundred quid and a fake passport the night she fled Cardiff." | | 2 | "Her uncle Tim, who’d taught her how to skip stones on the river." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 18 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 15 | | fancyCount | 2 | | fancyTags | | 0 | "she lied (lie)" | | 1 | "she whispered (whisper)" |
| | dialogueSentences | 49 | | tagDensity | 0.306 | | leniency | 0.612 | | rawRatio | 0.133 | | effectiveRatio | 0.082 | |