| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 5 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 15 | | tagDensity | 0.333 | | leniency | 0.667 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1232 | | 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) | |
| 71.59% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1232 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "gleaming" | | 1 | "footsteps" | | 2 | "grave" | | 3 | "flickered" | | 4 | "velvet" | | 5 | "lilt" | | 6 | "shattered" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 1 | | maxInWindow | 1 | | found | | 0 | | label | "eyes widened/narrowed" | | count | 1 |
|
| | highlights | | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 139 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 1 | | narrationSentences | 139 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 149 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 30 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1226 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 0 | | matches | (empty) | |
| 77.27% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 51 | | wordCount | 1100 | | uniqueNames | 19 | | maxNameDensity | 1.45 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Tomás" | | discoveredNames | | Camden | 1 | | Quinn | 1 | | Herrera | 1 | | Saint | 2 | | Christopher | 2 | | Raven | 1 | | Nest | 1 | | Tube | 1 | | Morris | 4 | | London | 1 | | Tomás | 11 | | Blitz | 1 | | Veil | 1 | | Market | 1 | | Harlow | 16 | | Spanish | 1 | | Seville | 1 | | Scar | 1 | | Three | 3 |
| | persons | | 0 | "Camden" | | 1 | "Quinn" | | 2 | "Herrera" | | 3 | "Saint" | | 4 | "Christopher" | | 5 | "Raven" | | 6 | "Nest" | | 7 | "Morris" | | 8 | "Tomás" | | 9 | "Harlow" |
| | places | | | globalScore | 0.773 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 85 | | glossingSentenceCount | 1 | | matches | | 0 | "sounded like grinding gravel" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1226 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 149 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 36 | | mean | 34.06 | | std | 24.74 | | cv | 0.727 | | sampleLengths | | 0 | 84 | | 1 | 5 | | 2 | 45 | | 3 | 19 | | 4 | 71 | | 5 | 40 | | 6 | 3 | | 7 | 15 | | 8 | 35 | | 9 | 8 | | 10 | 62 | | 11 | 61 | | 12 | 5 | | 13 | 61 | | 14 | 77 | | 15 | 3 | | 16 | 70 | | 17 | 48 | | 18 | 42 | | 19 | 22 | | 20 | 31 | | 21 | 29 | | 22 | 22 | | 23 | 15 | | 24 | 53 | | 25 | 72 | | 26 | 6 | | 27 | 44 | | 28 | 26 | | 29 | 66 | | 30 | 13 | | 31 | 12 | | 32 | 6 | | 33 | 38 | | 34 | 11 | | 35 | 6 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 139 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 199 | | matches | | |
| 46.98% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 6 | | semicolonCount | 0 | | flaggedSentences | 5 | | totalSentences | 149 | | ratio | 0.034 | | matches | | 0 | "He moved like a man who had carried stretchers through riots—economical, terrified." | | 1 | "The green neon of a distant sign—The Raven's Nest—bled into the storm behind her, fading." | | 2 | "Stencil markings read CAMDEN STATION WORKS—DO NOT ENTER." | | 3 | "The smell that rose from it was not London—too old, too sweet, like fruit left to rot in a church." | | 4 | "A medallion of bone hung at his own throat—the entry token." |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1112 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 15 | | adverbRatio | 0.013489208633093525 | | lyAdverbCount | 0 | | lyAdverbRatio | 0 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 149 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 149 | | mean | 8.23 | | std | 5.24 | | cv | 0.637 | | sampleLengths | | 0 | 7 | | 1 | 20 | | 2 | 20 | | 3 | 20 | | 4 | 12 | | 5 | 5 | | 6 | 5 | | 7 | 3 | | 8 | 24 | | 9 | 9 | | 10 | 4 | | 11 | 5 | | 12 | 3 | | 13 | 9 | | 14 | 5 | | 15 | 2 | | 16 | 5 | | 17 | 5 | | 18 | 14 | | 19 | 14 | | 20 | 12 | | 21 | 6 | | 22 | 4 | | 23 | 11 | | 24 | 5 | | 25 | 9 | | 26 | 6 | | 27 | 9 | | 28 | 3 | | 29 | 8 | | 30 | 3 | | 31 | 5 | | 32 | 7 | | 33 | 3 | | 34 | 15 | | 35 | 7 | | 36 | 13 | | 37 | 8 | | 38 | 5 | | 39 | 8 | | 40 | 9 | | 41 | 9 | | 42 | 8 | | 43 | 8 | | 44 | 10 | | 45 | 5 | | 46 | 15 | | 47 | 10 | | 48 | 8 | | 49 | 8 |
| |
| 60.40% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.3825503355704698 | | totalSentences | 149 | | uniqueOpeners | 57 | |
| 76.92% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 130 | | matches | | 0 | "Then the door sighed open." | | 1 | "Somewhere below, Tomás's footsteps slapped" | | 2 | "Instead, amber light flickered from" |
| | ratio | 0.023 | |
| 90.77% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 42 | | totalSentences | 130 | | matches | | 0 | "It drummed against awnings and" | | 1 | "He clutched a canvas medical" | | 2 | "She pushed off the wall." | | 3 | "He saw her." | | 4 | "She cut a diagonal across" | | 5 | "He moved like a man" | | 6 | "Her breath came steady." | | 7 | "He vaulted a chain-link fence." | | 8 | "His left forearm snagged on" | | 9 | "He didn't falter." | | 10 | "Her voice shredded the rain." | | 11 | "He kept moving." | | 12 | "She had lost sight of" | | 13 | "She had lost sight of" | | 14 | "He skidded around a brick" | | 15 | "She landed in a crouch," | | 16 | "They had closed the investigation" | | 17 | "She drew her baton." | | 18 | "Her shoes scraped on decades" | | 19 | "She caught the railing." |
| | ratio | 0.323 | |
| 44.62% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 108 | | totalSentences | 130 | | matches | | 0 | "Rain had scrubbed Camden clean" | | 1 | "It drummed against awnings and" | | 2 | "Harlow Quinn stood beneath the" | | 3 | "A drop slid from the" | | 4 | "Tomás Herrera emerged." | | 5 | "Olive skin, dark curls plastered" | | 6 | "He clutched a canvas medical" | | 7 | "Harlow's sharp jaw tightened." | | 8 | "She pushed off the wall." | | 9 | "He saw her." | | 10 | "Harlow's boots struck the pavement." | | 11 | "Puddle water erupted in geysers." | | 12 | "She cut a diagonal across" | | 13 | "Tomás bolted left, down a" | | 14 | "He moved like a man" | | 15 | "Harlow matched him stride for" | | 16 | "Her breath came steady." | | 17 | "He vaulted a chain-link fence." | | 18 | "His left forearm snagged on" | | 19 | "The scar along it split" |
| | ratio | 0.831 | |
| 38.46% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 130 | | matches | | 0 | "If she let Tomás vanish" |
| | ratio | 0.008 | |
| 65.64% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 37 | | technicalSentenceCount | 4 | | matches | | 0 | "He moved like a man who had carried stretchers through riots—economical, terrified." | | 1 | "A woman with milk-white eyes weighed something purple and fibrous on a scale that tipped without weights." | | 2 | "A man in a top hat whispered to a fox that sat upon his shoulder, its snout smeared red." | | 3 | "The warehouse walls had bent inward, as though something massive had inhaled the brick." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 5 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |