| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 94.96% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 993 | | totalAiIsmAdverbs | 1 | | 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) | |
| 29.51% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 993 | | totalAiIsms | 14 | | found | | 0 | | | 1 | | | 2 | | | 3 | | word | "carried the weight" | | count | 1 |
| | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | |
| | highlights | | 0 | "treacherous" | | 1 | "pounding" | | 2 | "jaw clenched" | | 3 | "carried the weight" | | 4 | "flickered" | | 5 | "stark" | | 6 | "echoed" | | 7 | "etched" | | 8 | "footsteps" | | 9 | "streaming" | | 10 | "unreadable" | | 11 | "weight" | | 12 | "flicker" |
| |
| 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 | 2 | | narrationSentences | 77 | | matches | | 0 | "d with guilt" | | 1 | "r with surprise" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 77 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 82 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 993 | | 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 | 21 | | wordCount | 954 | | uniqueNames | 11 | | maxNameDensity | 0.84 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | London | 1 | | Harlow | 1 | | Quinn | 8 | | Raven | 1 | | Nest | 1 | | Soho | 2 | | Morris | 1 | | Camden | 3 | | Veil | 1 | | Market | 1 | | Tube | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Raven" | | 3 | "Morris" | | 4 | "Market" |
| | places | | 0 | "London" | | 1 | "Soho" | | 2 | "Camden" |
| | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 68 | | 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 | 993 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 82 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 27 | | mean | 36.78 | | std | 28.28 | | cv | 0.769 | | sampleLengths | | 0 | 82 | | 1 | 95 | | 2 | 5 | | 3 | 33 | | 4 | 10 | | 5 | 54 | | 6 | 56 | | 7 | 8 | | 8 | 9 | | 9 | 71 | | 10 | 55 | | 11 | 12 | | 12 | 7 | | 13 | 51 | | 14 | 9 | | 15 | 9 | | 16 | 7 | | 17 | 68 | | 18 | 52 | | 19 | 12 | | 20 | 77 | | 21 | 54 | | 22 | 5 | | 23 | 3 | | 24 | 46 | | 25 | 59 | | 26 | 44 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 77 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 173 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 82 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 961 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 18 | | adverbRatio | 0.018730489073881373 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.003121748178980229 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 82 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 82 | | mean | 12.11 | | std | 6.11 | | cv | 0.504 | | sampleLengths | | 0 | 18 | | 1 | 20 | | 2 | 20 | | 3 | 8 | | 4 | 16 | | 5 | 8 | | 6 | 23 | | 7 | 15 | | 8 | 28 | | 9 | 21 | | 10 | 5 | | 11 | 6 | | 12 | 11 | | 13 | 16 | | 14 | 10 | | 15 | 15 | | 16 | 9 | | 17 | 9 | | 18 | 9 | | 19 | 12 | | 20 | 12 | | 21 | 14 | | 22 | 15 | | 23 | 15 | | 24 | 8 | | 25 | 9 | | 26 | 16 | | 27 | 10 | | 28 | 33 | | 29 | 7 | | 30 | 5 | | 31 | 12 | | 32 | 15 | | 33 | 3 | | 34 | 12 | | 35 | 13 | | 36 | 6 | | 37 | 6 | | 38 | 7 | | 39 | 3 | | 40 | 9 | | 41 | 20 | | 42 | 10 | | 43 | 9 | | 44 | 9 | | 45 | 9 | | 46 | 7 | | 47 | 10 | | 48 | 19 | | 49 | 18 |
| |
| 80.08% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.4878048780487805 | | totalSentences | 82 | | uniqueOpeners | 40 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 95.68% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 74 | | matches | | 0 | "Her brown eyes locked on" | | 1 | "She had watched from across" | | 2 | "Her shout sliced through the" | | 3 | "He glanced back once, face" | | 4 | "His shoulder clipped a man" | | 5 | "She vaulted it cleanly, landing" | | 6 | "They burst onto a wider" | | 7 | "She saved her air and" | | 8 | "He righted himself fast, but" | | 9 | "She would not lose him." | | 10 | "They left Soho's brighter lights" | | 11 | "Her lungs protested now, each" | | 12 | "He staggered for two steps," | | 13 | "she called, voice raw" | | 14 | "He laughed, a bitter bark" | | 15 | "He skidded to a halt" | | 16 | "His footsteps faded quickly." | | 17 | "It moved every full moon," | | 18 | "Her hand drifted toward the" | | 19 | "She held it out, watching" |
| | ratio | 0.311 | |
| 20.81% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 65 | | totalSentences | 74 | | matches | | 0 | "Rain lashed the London streets," | | 1 | "Detective Harlow Quinn broke into" | | 2 | "The suspect stayed twenty yards" | | 3 | "Her brown eyes locked on" | | 4 | "Salt-and-pepper strands of hair plastered" | | 5 | "The sharp line of her" | | 6 | "The worn leather watch on" | | 7 | "This man had slipped out" | | 8 | "She had watched from across" | | 9 | "Her shout sliced through the" | | 10 | "He glanced back once, face" | | 11 | "His shoulder clipped a man" | | 12 | "the man yelled, but neither" | | 13 | "Quinn leaped the scattered tins," | | 14 | "The chase wound deeper into" | | 15 | "The suspect kicked an empty" | | 16 | "She vaulted it cleanly, landing" | | 17 | "They burst onto a wider" | | 18 | "Horns blared as the suspect" | | 19 | "Quinn matched the dangerous weave," |
| | ratio | 0.878 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 74 | | matches | (empty) | | ratio | 0 | |
| 69.97% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 49 | | technicalSentenceCount | 5 | | matches | | 0 | "Detective Harlow Quinn broke into a full run, her boots pounding through puddles that sprayed cold water up her legs." | | 1 | "The suspect stayed twenty yards ahead, a hooded figure cutting through clusters of drenched pedestrians who scattered like startled birds." | | 2 | "Memories of DS Morris flickered at the edges of her mind, the unexplained wounds on his body three years back, the reports that made no sense without invoking t…" | | 3 | "Still she ran, dodging a fallen bicycle locked to a post, leaping a low fence that bordered a small park." | | 4 | "Vendors called out in low tones, displaying vials that swirled with inner light and bundles of herbs that moved on their own." |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 2 | | matches | | 0 | "the man yelled, but neither runner paused" | | 1 | "she called, voice raw" |
| |
| 7.14% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 2 | | fancyCount | 1 | | fancyTags | | | dialogueSentences | 7 | | tagDensity | 0.286 | | leniency | 0.571 | | rawRatio | 0.5 | | effectiveRatio | 0.286 | |