| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 2 | | tagDensity | 1 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1168 | | 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) | |
| 35.79% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1168 | | totalAiIsms | 15 | | found | | | highlights | | 0 | "weight" | | 1 | "echo" | | 2 | "symphony" | | 3 | "familiar" | | 4 | "comforting" | | 5 | "silence" | | 6 | "electric" | | 7 | "footsteps" | | 8 | "chaotic" | | 9 | "glint" | | 10 | "pulsed" | | 11 | "resolve" | | 12 | "glinting" | | 13 | "determined" |
| |
| 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 | 2 | | narrationSentences | 107 | | matches | | 0 | "t with panic" | | 1 | "t with fear" |
| |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 107 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 107 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 33 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1163 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 37 | | wordCount | 1160 | | uniqueNames | 15 | | maxNameDensity | 0.78 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 9 | | Tomás | 1 | | Herrera | 9 | | Charing | 1 | | Cross | 1 | | Road | 1 | | Saint | 1 | | Christopher | 1 | | Tube | 2 | | Town | 1 | | Morris | 4 | | Veil | 2 | | Market | 2 | | London | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tomás" | | 3 | "Herrera" | | 4 | "Saint" | | 5 | "Christopher" | | 6 | "Morris" |
| | places | | 0 | "Charing" | | 1 | "Cross" | | 2 | "Road" | | 3 | "Tube" | | 4 | "Town" | | 5 | "London" |
| | globalScore | 1 | | windowScore | 1 | |
| 88.27% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 81 | | glossingSentenceCount | 2 | | matches | | 0 | "looked like glowing moss and fine silver" | | 1 | "not quite right" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.86 | | wordCount | 1163 | | matches | | 0 | "not with fear now, but with a desperate kind of resolve" |
| |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 107 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 23 | | mean | 50.57 | | std | 28.89 | | cv | 0.571 | | sampleLengths | | 0 | 76 | | 1 | 78 | | 2 | 56 | | 3 | 12 | | 4 | 56 | | 5 | 40 | | 6 | 57 | | 7 | 35 | | 8 | 63 | | 9 | 41 | | 10 | 50 | | 11 | 41 | | 12 | 6 | | 13 | 153 | | 14 | 36 | | 15 | 63 | | 16 | 57 | | 17 | 41 | | 18 | 24 | | 19 | 33 | | 20 | 26 | | 21 | 79 | | 22 | 40 |
| |
| 88.87% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 5 | | totalSentences | 107 | | matches | | 0 | "was recessed" | | 1 | "were trapped" | | 2 | "was tipped" | | 3 | "was fired" | | 4 | "was made" |
| |
| 87.64% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 3 | | totalVerbs | 178 | | matches | | 0 | "was wading" | | 1 | "was cloying" | | 2 | "was running" |
| |
| 62.75% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 107 | | ratio | 0.028 | | matches | | 0 | "It smelled of damp earth, ozone, and something else—something floral and metallic, like dying orchids and blood." | | 1 | "A few heads turned—pale, elongated faces with too many joints in the neck—but the market’s rhythm absorbed her shout." | | 2 | "She saw a child—barely three feet tall, with a crown of tiny, feathered horns—tugging its mother toward a stall where luminous butterflies were trapped in glass jars." |
| |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1171 | | adjectiveStacks | 1 | | stackExamples | | 0 | "narrow, brick-lined corridor" |
| | adverbCount | 23 | | adverbRatio | 0.01964133219470538 | | lyAdverbCount | 6 | | lyAdverbRatio | 0.005123825789923143 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 107 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 107 | | mean | 10.87 | | std | 6.47 | | cv | 0.596 | | sampleLengths | | 0 | 10 | | 1 | 13 | | 2 | 19 | | 3 | 15 | | 4 | 19 | | 5 | 8 | | 6 | 13 | | 7 | 15 | | 8 | 9 | | 9 | 4 | | 10 | 11 | | 11 | 1 | | 12 | 13 | | 13 | 4 | | 14 | 14 | | 15 | 13 | | 16 | 23 | | 17 | 4 | | 18 | 2 | | 19 | 12 | | 20 | 3 | | 21 | 9 | | 22 | 13 | | 23 | 13 | | 24 | 18 | | 25 | 13 | | 26 | 19 | | 27 | 4 | | 28 | 4 | | 29 | 8 | | 30 | 3 | | 31 | 13 | | 32 | 2 | | 33 | 18 | | 34 | 13 | | 35 | 17 | | 36 | 4 | | 37 | 14 | | 38 | 12 | | 39 | 9 | | 40 | 4 | | 41 | 1 | | 42 | 8 | | 43 | 12 | | 44 | 17 | | 45 | 10 | | 46 | 11 | | 47 | 10 | | 48 | 10 | | 49 | 9 |
| |
| 52.34% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 4 | | diversityRatio | 0.3364485981308411 | | totalSentences | 107 | | uniqueOpeners | 36 | |
| 33.33% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 100 | | matches | | 0 | "Too tall, too thin, with" |
| | ratio | 0.01 | |
| 88.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 33 | | totalSentences | 100 | | matches | | 0 | "Her worn leather watch, a" | | 1 | "He ducked around a corner," | | 2 | "She recovered, her military training" | | 3 | "She shoved it down." | | 4 | "He cut across Charing Cross" | | 5 | "Her voice was a hoarse" | | 6 | "He didn’t slow." | | 7 | "She moved to the edge" | | 8 | "He didn’t look back." | | 9 | "He knew these tunnels." | | 10 | "Her radio crackled, a garbled" | | 11 | "She ignored it." | | 12 | "He wrenched it open." | | 13 | "She twisted the knob." | | 14 | "They never locked things they" | | 15 | "She pushed it open a" | | 16 | "It smelled of damp earth," | | 17 | "She pulled her firearm from" | | 18 | "She’d learned the hard way" | | 19 | "Her torch beam pierced the" |
| | ratio | 0.33 | |
| 10.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 90 | | totalSentences | 100 | | matches | | 0 | "Detective Harlow Quinn’s lungs burned," | | 1 | "Her worn leather watch, a" | | 2 | "He ducked around a corner," | | 3 | "Quinn’s foot skidded on a" | | 4 | "She recovered, her military training" | | 5 | "Morris would have been a" | | 6 | "The memory was a cold" | | 7 | "She shoved it down." | | 8 | "The suspect’s olive-skinned face, tight" | | 9 | "The paramedic who’d lost his" | | 10 | "The clique’s pet medic." | | 11 | "He cut across Charing Cross" | | 12 | "Quinn plunged after him, the" | | 13 | "The Saint Christopher medal around" | | 14 | "An old, closed station." | | 15 | "Her voice was a hoarse" | | 16 | "He didn’t slow." | | 17 | "The click of the turnstile" | | 18 | "Quinn vaulted it, landing hard" | | 19 | "The station was a tomb," |
| | ratio | 0.9 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 100 | | matches | (empty) | | ratio | 0 | |
| 66.33% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 56 | | technicalSentenceCount | 6 | | matches | | 0 | "The paramedic who’d lost his license for treating things that shouldn’t need treating." | | 1 | "He cut across Charing Cross Road, dodging a black cab that screamed its horn." | | 2 | "Her torch beam pierced the dark, illuminating a narrow, brick-lined corridor that sloped down at a steep angle." | | 3 | "Another unwrapped a bundle of dried leaves that sang a single, mournful note when exposed to the air." | | 4 | "She thought of Morris, of the unexplained energy signatures at his crime scene, of the witness who’d swore he saw something with wings climb the building next d…" | | 5 | "She moved after Herrera, her reflection a dark, determined shape in the polished black stone of a stall selling mirrors that showed other places." |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |