| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 16 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1369 | | totalAiIsmAdverbs | 0 | | found | (empty) | | highlights | (empty) | |
| 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) | |
| 0.00% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1369 | | totalAiIsms | 32 | | found | | 0 | | word | "practiced ease" | | count | 1 |
| | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | word | "down her spine" | | count | 1 |
| | 20 | | | 21 | | | 22 | | | 23 | |
| | highlights | | 0 | "practiced ease" | | 1 | "unraveling" | | 2 | "quickened" | | 3 | "pounding" | | 4 | "raced" | | 5 | "clandestine" | | 6 | "flickered" | | 7 | "beacon" | | 8 | "calculating" | | 9 | "scanned" | | 10 | "familiar" | | 11 | "measured" | | 12 | "could feel" | | 13 | "unspoken" | | 14 | "amiss" | | 15 | "depths" | | 16 | "resolve" | | 17 | "racing" | | 18 | "palpable" | | 19 | "down her spine" | | 20 | "determined" | | 21 | "unwavering" | | 22 | "footsteps" | | 23 | "echoing" |
| |
| 0.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 4 | | maxInWindow | 4 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
| | 1 | | label | "air was thick with" | | count | 1 |
| | 2 | | label | "sent a shiver through" | | count | 1 |
| | 3 | | label | "flicker of emotion" | | count | 1 |
|
| | highlights | | 0 | "eyes narrowed" | | 1 | "The air was thick with" | | 2 | "sent a shiver down" | | 3 | "a flash of fear" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 83 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 83 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 93 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 50 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1368 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 63 | | wordCount | 1166 | | uniqueNames | 15 | | maxNameDensity | 1.54 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Herrera" | | discoveredNames | | Harlow | 2 | | Quinn | 18 | | Tomás | 3 | | Herrera | 16 | | Morris | 1 | | Soho | 1 | | Raven | 3 | | Nest | 3 | | Veil | 5 | | Market | 5 | | Saint | 1 | | Christopher | 1 | | Tube | 1 | | Ciphers | 1 | | Detective | 2 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Morris" | | 5 | "Nest" | | 6 | "Saint" | | 7 | "Christopher" |
| | places | | | globalScore | 0.728 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 75 | | 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 | 1368 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 93 | | matches | (empty) | |
| 94.04% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 29 | | mean | 47.17 | | std | 22.6 | | cv | 0.479 | | sampleLengths | | 0 | 87 | | 1 | 84 | | 2 | 66 | | 3 | 79 | | 4 | 72 | | 5 | 62 | | 6 | 68 | | 7 | 60 | | 8 | 58 | | 9 | 21 | | 10 | 31 | | 11 | 9 | | 12 | 38 | | 13 | 48 | | 14 | 10 | | 15 | 66 | | 16 | 64 | | 17 | 58 | | 18 | 52 | | 19 | 39 | | 20 | 18 | | 21 | 62 | | 22 | 39 | | 23 | 53 | | 24 | 24 | | 25 | 21 | | 26 | 20 | | 27 | 14 | | 28 | 45 |
| |
| 96.81% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 2 | | totalSentences | 83 | | matches | | 0 | "been whispered" | | 1 | "were adorned" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 2 | | totalVerbs | 176 | | matches | | 0 | "was heading" | | 1 | "was bustling" |
| |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 93 | | ratio | 0.011 | | matches | | 0 | "She knew who he was—Tomás Herrera, a name that had been whispered in dark corners and hushed tones for months." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1174 | | adjectiveStacks | 1 | | stackExamples | | 0 | "former paramedic turned rogue" |
| | adverbCount | 22 | | adverbRatio | 0.018739352640545145 | | lyAdverbCount | 8 | | lyAdverbRatio | 0.0068143100511073255 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 93 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 93 | | mean | 14.71 | | std | 7.02 | | cv | 0.478 | | sampleLengths | | 0 | 14 | | 1 | 19 | | 2 | 20 | | 3 | 15 | | 4 | 19 | | 5 | 16 | | 6 | 20 | | 7 | 24 | | 8 | 24 | | 9 | 18 | | 10 | 12 | | 11 | 20 | | 12 | 16 | | 13 | 19 | | 14 | 21 | | 15 | 25 | | 16 | 14 | | 17 | 12 | | 18 | 19 | | 19 | 28 | | 20 | 9 | | 21 | 4 | | 22 | 15 | | 23 | 21 | | 24 | 15 | | 25 | 11 | | 26 | 20 | | 27 | 11 | | 28 | 20 | | 29 | 17 | | 30 | 23 | | 31 | 16 | | 32 | 14 | | 33 | 7 | | 34 | 12 | | 35 | 21 | | 36 | 25 | | 37 | 15 | | 38 | 6 | | 39 | 15 | | 40 | 16 | | 41 | 3 | | 42 | 6 | | 43 | 13 | | 44 | 14 | | 45 | 11 | | 46 | 9 | | 47 | 17 | | 48 | 11 | | 49 | 11 |
| |
| 40.32% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 9 | | diversityRatio | 0.26881720430107525 | | totalSentences | 93 | | uniqueOpeners | 25 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 80 | | matches | (empty) | | ratio | 0 | |
| 90.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 80 | | matches | | 0 | "Her boots splashed through puddles," | | 1 | "She tightened her grip on" | | 2 | "She knew who he was—Tomás" | | 3 | "It was a shortcut, one" | | 4 | "She quickened her pace, her" | | 5 | "Her mind raced, replaying the" | | 6 | "They had mentioned something about" | | 7 | "She rounded the corner, the" | | 8 | "It was a gateway." | | 9 | "She pushed through the throng" | | 10 | "Her focus was razor-sharp, her" | | 11 | "She reached the entrance to" | | 12 | "She scanned the room, her" | | 13 | "He fidgeted with the Saint" | | 14 | "She could feel the eyes" | | 15 | "She reached Herrera just as" | | 16 | "he said, his voice steady" | | 17 | "He knew the risks, but" | | 18 | "She had come too far" | | 19 | "she said, her voice a" |
| | ratio | 0.325 | |
| 16.25% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 71 | | totalSentences | 80 | | matches | | 0 | "The rain fell in sheets," | | 1 | "Detective Harlow Quinn’s breath came" | | 2 | "Her boots splashed through puddles," | | 3 | "She tightened her grip on" | | 4 | "Quinn’s eyes, sharp from years" | | 5 | "She knew who he was—Tomás" | | 6 | "A former paramedic turned rogue" | | 7 | "The suspect turned a corner," | | 8 | "It was a shortcut, one" | | 9 | "She quickened her pace, her" | | 10 | "Her mind raced, replaying the" | | 11 | "Tomás Herrera, usually so cautious" | | 12 | "They had mentioned something about" | | 13 | "A hidden black market that" | | 14 | "She rounded the corner, the" | | 15 | "The neon lights of The" | | 16 | "Quinn knew the bar well," | | 17 | "It was a gateway." | | 18 | "Herrera had disappeared into the" | | 19 | "She pushed through the throng" |
| | ratio | 0.888 | |
| 62.50% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 80 | | matches | | 0 | "If Herrera was heading there," |
| | ratio | 0.013 | |
| 83.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 60 | | technicalSentenceCount | 5 | | matches | | 0 | "Ahead, a dark figure darted into the shadows, moving with a practiced ease that belied any hint of panic." | | 1 | "A former paramedic turned rogue healer, he was the key to unraveling the tangled web of supernatural activities that had been plaguing the city." | | 2 | "A hidden black market that moved its location with the phases of the moon, catering to the darkest desires and needs of the supernatural community." | | 3 | "The only light came from flickering torches mounted on the walls, casting eerie shadows that danced along the stone surfaces." | | 4 | "The Veil Market was in full swing, stalls and booths lining the platform, their proprietors hawking everything from enchanted trinkets to banned alchemical subs…" |
| |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 3 | | matches | | 0 | "he said, his voice steady despite the tremor in his hands" | | 1 | "she said, her voice a quiet determination" | | 2 | "he asked, his voice soft but steady" |
| |
| 25.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 5 | | fancyCount | 2 | | fancyTags | | 0 | "he warned (warn)" | | 1 | "he admitted (admit)" |
| | dialogueSentences | 16 | | tagDensity | 0.313 | | leniency | 0.625 | | rawRatio | 0.4 | | effectiveRatio | 0.25 | |