| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 1 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 7 | | tagDensity | 0.143 | | leniency | 0.286 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 100.00% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1289 | | 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) | |
| 72.85% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1289 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "stark" | | 1 | "etched" | | 2 | "calibrated" | | 3 | "charged" | | 4 | "echoes" | | 5 | "shattered" |
| |
| 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 | 0 | | narrationSentences | 34 | | matches | (empty) | |
| 58.82% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 34 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 41 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 112 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1289 | | 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 | 26 | | wordCount | 898 | | uniqueNames | 10 | | maxNameDensity | 0.89 | | worstName | "Harlow" | | maxWindowNameDensity | 2 | | worstWindowName | "Harlow" | | discoveredNames | | Camden | 2 | | Metropolitan | 1 | | Police | 1 | | Harlow | 8 | | Oxford | 1 | | London | 1 | | Eva | 7 | | Liam | 1 | | Morris | 3 | | Met | 1 |
| | persons | | 0 | "Metropolitan" | | 1 | "Harlow" | | 2 | "Eva" | | 3 | "Liam" | | 4 | "Morris" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 34 | | 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 | 1289 | | matches | (empty) | |
| 85.37% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 41 | | matches | | |
| 98.32% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 8 | | mean | 161.13 | | std | 79.61 | | cv | 0.494 | | sampleLengths | | 0 | 142 | | 1 | 168 | | 2 | 45 | | 3 | 83 | | 4 | 138 | | 5 | 163 | | 6 | 321 | | 7 | 229 |
| |
| 74.30% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 3 | | totalSentences | 34 | | matches | | 0 | "is cuffed" | | 1 | "been locked" | | 2 | "being linked" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 142 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 41 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 723 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 13 | | adverbRatio | 0.017980636237897647 | | lyAdverbCount | 3 | | lyAdverbRatio | 0.004149377593360996 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 41 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 41 | | mean | 31.44 | | std | 19.32 | | cv | 0.615 | | sampleLengths | | 0 | 17 | | 1 | 26 | | 2 | 34 | | 3 | 25 | | 4 | 26 | | 5 | 7 | | 6 | 7 | | 7 | 36 | | 8 | 26 | | 9 | 30 | | 10 | 27 | | 11 | 27 | | 12 | 22 | | 13 | 24 | | 14 | 21 | | 15 | 23 | | 16 | 18 | | 17 | 36 | | 18 | 6 | | 19 | 61 | | 20 | 16 | | 21 | 61 | | 22 | 23 | | 23 | 33 | | 24 | 26 | | 25 | 34 | | 26 | 31 | | 27 | 16 | | 28 | 30 | | 29 | 112 | | 30 | 26 | | 31 | 49 | | 32 | 47 | | 33 | 27 | | 34 | 16 | | 35 | 14 | | 36 | 80 | | 37 | 44 | | 38 | 34 | | 39 | 37 | | 40 | 34 |
| |
| 45.53% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.3170731707317073 | | totalSentences | 41 | | uniqueOpeners | 13 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 34 | | matches | (empty) | | ratio | 0 | |
| 90.59% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 11 | | totalSentences | 34 | | matches | | 0 | "She yanks the frayed crime" | | 1 | "She had tracked a string" | | 2 | "She steps into the main" | | 3 | "She tucks a stray strand" | | 4 | "She tosses it at Eva’s" | | 5 | "She leans in to examine" | | 6 | "She picks up one of" | | 7 | "She had spent three years" | | 8 | "She had built a case" | | 9 | "She had thought they were" | | 10 | "She turns back to Eva," |
| | ratio | 0.324 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 32 | | totalSentences | 34 | | matches | | 0 | "Harlow’s boot crunches over shards" | | 1 | "She yanks the frayed crime" | | 2 | "The uniform posted at the" | | 3 | "This is the walled off" | | 4 | "She had tracked a string" | | 5 | "The body turned up three" | | 6 | "She steps into the main" | | 7 | "The first body she sees" | | 8 | "A girl is cuffed to" | | 9 | "She tucks a stray strand" | | 10 | "The two uniforms processing the" | | 11 | "The uniforms don’t question her," | | 12 | "Harlow stops a few feet" | | 13 | "Eva’s voice is steady, sharp," | | 14 | "Harlow pulls a small plastic" | | 15 | "She tosses it at Eva’s" | | 16 | "Eva leans forward, the cuffs" | | 17 | "Harlow crosses the hall to" | | 18 | "Eva is right, the wound" | | 19 | "She leans in to examine" |
| | ratio | 0.941 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 34 | | matches | (empty) | | ratio | 0 | |
| 0.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 30 | | technicalSentenceCount | 6 | | matches | | 0 | "The two uniforms processing the scene glance up as Harlow approaches, and she waves them toward the narrow service tunnel that leads to the active Camden statio…" | | 1 | "Harlow pulls a small plastic evidence bag from her coat, the bag holding a chipped fox bone etched with a tiny black sigil, the same type of bone token that ser…" | | 2 | "Eva leans forward, the cuffs on her wrists clinking against the turnstile, her green eyes flashing." | | 3 | "Eva is right, the wound around the blade doesn’t have any of the bruising that would come from a forceful stab, the skin around it is pale, unbroken save for th…" | | 4 | "Eva tucks another strand of hair behind her ear, her face draining of all colour as the compasses on the stall all stop spinning at once, their needles snapping…" | | 5 | "A low hum rises from the tunnel, the same frequency that makes her teeth rattle in her skull, the hum of the master compass, powering up, calling whatever it ha…" |
| |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 1 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |