| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 72.78% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1102 | | totalAiIsmAdverbs | 6 | | found | | 0 | | | 1 | | adverb | "deliberately" | | count | 1 |
| | 2 | | | 3 | |
| | highlights | | 0 | "really" | | 1 | "deliberately" | | 2 | "slightly" | | 3 | "very" |
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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) | |
| 77.31% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1102 | | totalAiIsms | 5 | | found | | | highlights | | 0 | "scanning" | | 1 | "footsteps" | | 2 | "weight" | | 3 | "flicker" |
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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 | 74 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 74 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 74 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 47 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1106 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 4 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 1106 | | uniqueNames | 11 | | maxNameDensity | 0.63 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 1 | | Herrera | 6 | | Raven | 1 | | Nest | 1 | | Oxford | 1 | | Street | 1 | | Morris | 4 | | Holborn | 1 | | English | 1 | | Tube | 1 | | Quinn | 7 |
| | persons | | 0 | "Herrera" | | 1 | "Raven" | | 2 | "Morris" | | 3 | "Quinn" |
| | places | | 0 | "Soho" | | 1 | "Oxford" | | 2 | "Street" |
| | globalScore | 1 | | windowScore | 1 | |
| 65.25% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 59 | | glossingSentenceCount | 2 | | matches | | 0 | "not quite a color she had a name for" | | 1 | "not quite a click, not quite a tone, not quite either of those things" | | 2 | "not quite a tone, not quite either of those things" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1106 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 74 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 40.96 | | std | 27.81 | | cv | 0.679 | | sampleLengths | | 0 | 59 | | 1 | 80 | | 2 | 7 | | 3 | 76 | | 4 | 55 | | 5 | 2 | | 6 | 67 | | 7 | 92 | | 8 | 54 | | 9 | 7 | | 10 | 59 | | 11 | 10 | | 12 | 92 | | 13 | 24 | | 14 | 41 | | 15 | 16 | | 16 | 43 | | 17 | 16 | | 18 | 71 | | 19 | 36 | | 20 | 45 | | 21 | 13 | | 22 | 9 | | 23 | 61 | | 24 | 46 | | 25 | 20 | | 26 | 5 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 74 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 176 | | matches | | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 74 | | ratio | 0.081 | | matches | | 0 | "She recognized him immediately — the olive skin, the short curly hair plastered now to his forehead, the way he moved with a kind of coiled watchfulness, scanning left and right before committing to a direction." | | 1 | "He had the gait of someone who expected to be followed — quick changes of pace, a casual glance into a darkened window that was really a mirror check." | | 2 | "Quinn lengthened her stride and turned the corner in time to see his jacket — dark green, hood still down despite the downpour — disappearing into the mouth of a side street." | | 3 | "Herrera's footsteps were audible over the rain, a quick slap against wet stone, and then he rounded a corner and the sound changed — less open, more enclosed, like a throat." | | 4 | "But three years ago, DS Morris had followed a thread that looked exactly like this one — thin, circumstantial, pulling toward something she didn't have a name for — and he hadn't come back from it." | | 5 | "Around its edges, just visible in the ambient light from the street above, she could see a faint luminescence — not quite a color she had a name for." |
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| 96.09% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1102 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 49 | | adverbRatio | 0.04446460980036298 | | lyAdverbCount | 17 | | lyAdverbRatio | 0.015426497277676952 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 74 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 74 | | mean | 14.95 | | std | 11.11 | | cv | 0.743 | | sampleLengths | | 0 | 21 | | 1 | 38 | | 2 | 2 | | 3 | 36 | | 4 | 22 | | 5 | 20 | | 6 | 7 | | 7 | 7 | | 8 | 23 | | 9 | 29 | | 10 | 17 | | 11 | 23 | | 12 | 32 | | 13 | 2 | | 14 | 5 | | 15 | 11 | | 16 | 20 | | 17 | 31 | | 18 | 13 | | 19 | 6 | | 20 | 15 | | 21 | 44 | | 22 | 7 | | 23 | 7 | | 24 | 36 | | 25 | 6 | | 26 | 12 | | 27 | 7 | | 28 | 8 | | 29 | 24 | | 30 | 11 | | 31 | 16 | | 32 | 10 | | 33 | 4 | | 34 | 13 | | 35 | 28 | | 36 | 47 | | 37 | 7 | | 38 | 2 | | 39 | 3 | | 40 | 12 | | 41 | 12 | | 42 | 29 | | 43 | 4 | | 44 | 7 | | 45 | 5 | | 46 | 5 | | 47 | 21 | | 48 | 5 | | 49 | 12 |
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| 53.15% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.3918918918918919 | | totalSentences | 74 | | uniqueOpeners | 29 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 70 | | matches | | 0 | "Just a small depression in" | | 1 | "Then she reached into the" | | 2 | "Somewhere in there, Herrera was" |
| | ratio | 0.043 | |
| 60.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 28 | | totalSentences | 70 | | matches | | 0 | "She recognized him immediately —" | | 1 | "She had his photograph on" | | 2 | "She gave him five seconds," | | 3 | "He was heading north, toward" | | 4 | "He had the gait of" | | 5 | "He was moving faster now." | | 6 | "Her baton was on her" | | 7 | "She had not called this" | | 8 | "She didn't have enough for" | | 9 | "She barely had enough for" | | 10 | "She came around the corner" | | 11 | "She could see the scuff" | | 12 | "She stood in the rain" | | 13 | "She thought about the bone" | | 14 | "She had looked it up" | | 15 | "She should call it in." | | 16 | "She thought about Morris." | | 17 | "She thought about the way" | | 18 | "She put her foot on" | | 19 | "She put her weight on" |
| | ratio | 0.4 | |
| 95.71% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 51 | | totalSentences | 70 | | matches | | 0 | "The rain came down in" | | 1 | "Quinn had been watching the" | | 2 | "She recognized him immediately —" | | 3 | "The scar on his left" | | 4 | "She had his photograph on" | | 5 | "She gave him five seconds," | | 6 | "He was heading north, toward" | | 7 | "Quinn fell into his wake" | | 8 | "He had the gait of" | | 9 | "Quinn lengthened her stride and" | | 10 | "He was moving faster now." | | 11 | "The side street was narrow" | | 12 | "Herrera's footsteps were audible over" | | 13 | "Quinn's hand found the weight" | | 14 | "Her baton was on her" | | 15 | "She had not called this" | | 16 | "The file on Herrera was" | | 17 | "She didn't have enough for" | | 18 | "She barely had enough for" | | 19 | "The coroner had ruled cardiac" |
| | ratio | 0.729 | |
| 100.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 3 | | totalSentences | 70 | | matches | | 0 | "Either he'd made her or" | | 1 | "To her left, a rusted" | | 2 | "By the time she reached" |
| | ratio | 0.043 | |
| 93.02% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 43 | | technicalSentenceCount | 3 | | matches | | 0 | "He had the gait of someone who expected to be followed — quick changes of pace, a casual glance into a darkened window that was really a mirror check." | | 1 | "The file on Herrera was thin and circumstantial: known associate of persons of interest, observed at three locations connected to an ongoing investigation, a fo…" | | 2 | "But three years ago, DS Morris had followed a thread that looked exactly like this one — thin, circumstantial, pulling toward something she didn't have a name f…" |
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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 | |