| 88.89% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "the figure stepped aside [aside]" |
| | dialogueSentences | 10 | | tagDensity | 0.9 | | leniency | 1 | | rawRatio | 0.111 | | effectiveRatio | 0.111 | |
| 86.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 723 | | 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) | |
| 17.01% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 723 | | totalAiIsms | 12 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | word | "down her spine" | | count | 1 |
| | 6 | | | 7 | | | 8 | | | 9 | | | 10 | |
| | highlights | | 0 | "glinting" | | 1 | "echoed" | | 2 | "jaw clenched" | | 3 | "flicked" | | 4 | "racing" | | 5 | "down her spine" | | 6 | "measured" | | 7 | "unspoken" | | 8 | "cacophony" | | 9 | "navigate" | | 10 | "treacherous" |
| |
| 33.33% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 3 | | maxInWindow | 3 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "jaw/fists clenched" | | count | 2 |
|
| | highlights | | 0 | "eyes widened" | | 1 | "jaw clenched" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 39 | | matches | | |
| 32.97% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 2 | | narrationSentences | 39 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 40 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 723 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 5 | | unquotedAttributions | 0 | | matches | (empty) | |
| 29.70% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 665 | | uniqueNames | 12 | | maxNameDensity | 2.41 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Soho | 2 | | Harlow | 1 | | Quinn | 16 | | Tomás | 7 | | Herrera | 1 | | Berwick | 1 | | Street | 1 | | Morris | 1 | | Veil | 1 | | Market | 1 | | Be | 1 | | Continued | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Morris" |
| | places | | 0 | "Soho" | | 1 | "Berwick" | | 2 | "Street" | | 3 | "Be" |
| | globalScore | 0.297 | | windowScore | 0.5 | |
| 0.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 37 | | glossingSentenceCount | 3 | | matches | | 0 | "hooded figure, seemingly carved from the sha" | | 1 | "not quite looking at her" | | 2 | "eyes that seemed to bore into Quinn's very soul before dipping to examine the envelope's contents" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 723 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 40 | | matches | (empty) | |
| 90.79% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 18 | | mean | 40.17 | | std | 18.79 | | cv | 0.468 | | sampleLengths | | 0 | 72 | | 1 | 56 | | 2 | 64 | | 3 | 58 | | 4 | 21 | | 5 | 62 | | 6 | 57 | | 7 | 29 | | 8 | 38 | | 9 | 17 | | 10 | 41 | | 11 | 49 | | 12 | 50 | | 13 | 18 | | 14 | 23 | | 15 | 30 | | 16 | 34 | | 17 | 4 |
| |
| 96.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 39 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 118 | | matches | (empty) | |
| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 3 | | semicolonCount | 4 | | flaggedSentences | 7 | | totalSentences | 40 | | ratio | 0.175 | | matches | | 0 | "Detective Harlow Quinn's eyes, narrowed against the downpour, locked onto the figure darting ahead – Tomás Herrera, her prime suspect in the string of unexplained occurrences linked to the elusive clique." | | 1 | "Quinn's sharp jaw clenched; she'd lose him if she didn't keep pace." | | 2 | "Quinn's gut tightened; this wasn't a sanctioned entrance." | | 3 | "Quinn's eyes widened; this was no ordinary black market." | | 4 | "Quinn's sharp jaw clenched in frustration; every second she stalled, Tomás slipped further away." | | 5 | "Her thoughts flashed back to DS Morris, her lost partner, and the unexplained circumstances of his death – circumstances that had led her to this hidden world." | | 6 | "Reaching into her pocket, Quinn's fingers closed around a small, unmarked envelope – a token she'd confiscated from a previous, unrelated case, its significance still unknown to her." |
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| 84.90% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 666 | | adjectiveStacks | 2 | | stackExamples | | 0 | "bustling, lantern-lit marketplace." | | 1 | "revealing piercing green eyes" |
| | adverbCount | 29 | | adverbRatio | 0.04354354354354354 | | lyAdverbCount | 12 | | lyAdverbRatio | 0.018018018018018018 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 40 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 40 | | mean | 18.08 | | std | 8.02 | | cv | 0.444 | | sampleLengths | | 0 | 18 | | 1 | 31 | | 2 | 23 | | 3 | 25 | | 4 | 12 | | 5 | 19 | | 6 | 22 | | 7 | 12 | | 8 | 30 | | 9 | 17 | | 10 | 8 | | 11 | 13 | | 12 | 20 | | 13 | 21 | | 14 | 30 | | 15 | 25 | | 16 | 7 | | 17 | 9 | | 18 | 9 | | 19 | 13 | | 20 | 26 | | 21 | 16 | | 22 | 13 | | 23 | 23 | | 24 | 15 | | 25 | 5 | | 26 | 12 | | 27 | 14 | | 28 | 27 | | 29 | 28 | | 30 | 9 | | 31 | 12 | | 32 | 29 | | 33 | 21 | | 34 | 18 | | 35 | 12 | | 36 | 11 | | 37 | 30 | | 38 | 34 | | 39 | 4 |
| |
| 100.00% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 0 | | diversityRatio | 0.6 | | totalSentences | 40 | | uniqueOpeners | 24 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 39 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 5 | | totalSentences | 39 | | matches | | 0 | "She took the corner tightly," | | 1 | "she bellowed down into the" | | 2 | "she tried, her voice firm" | | 3 | "Her thoughts flashed back to" | | 4 | "she whispered to herself, vanishing" |
| | ratio | 0.128 | |
| 36.92% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 33 | | totalSentences | 39 | | matches | | 0 | "Rain lashed down on the" | | 1 | "Detective Harlow Quinn's eyes, narrowed" | | 2 | "Quinn's military-honed bearing kept her" | | 3 | "Quinn's voice, hoarse from the" | | 4 | "Quinn plunged after, weaving past" | | 5 | "The smells of sizzling street" | | 6 | "Tomás abruptly veered off the" | | 7 | "Quinn's sharp jaw clenched; she'd" | | 8 | "She took the corner tightly," | | 9 | "A faded sign creaked in" | | 10 | "Quinn's gut tightened; this wasn't" | | 11 | "The detective's gaze flicked upwards," | | 12 | "This could be the break" | | 13 | "she bellowed down into the" | | 14 | "A shiver, not entirely from" | | 15 | "The air reeked of mold" | | 16 | "Quinn's eyes widened; this was" | | 17 | "Stalls touted everything from glowing," | | 18 | "The detective's training screamed at" | | 19 | "A hooded figure, seemingly carved" |
| | ratio | 0.846 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 39 | | matches | (empty) | | ratio | 0 | |
| 53.57% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 24 | | technicalSentenceCount | 3 | | matches | | 0 | "She took the corner tightly, her eyes adjusting to the alley's darkness just in time to see Tomás descending a set of grimy, wet stairs, almost invisible in the…" | | 1 | "Her thoughts flashed back to DS Morris, her lost partner, and the unexplained circumstances of his death – circumstances that had led her to this hidden world." | | 2 | "After an interminable moment, the figure nudged its hood back, revealing piercing green eyes that seemed to bore into Quinn's very soul before dipping to examin…" |
| |
| 13.89% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 2 | | matches | | 0 | "androgynous voice requested, not quite looking at her" | | 1 | "the figure murmured, voice dripping with an unspoken warning," |
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| 0.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 4 | | fancyTags | | 0 | "she bellowed (bellow)" | | 1 | "androgynous voice requested (request)" | | 2 | "the figure murmured (murmur)" | | 3 | "she whispered (whisper)" |
| | dialogueSentences | 10 | | tagDensity | 0.4 | | leniency | 0.8 | | rawRatio | 1 | | effectiveRatio | 0.8 | |