| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | |
| 94.59% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1848 | | totalAiIsmAdverbs | 2 | | found | | 0 | | adverb | "deliberately" | | count | 1 |
| | 1 | |
| | highlights | | 0 | "deliberately" | | 1 | "sharply" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 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 | 1848 | | totalAiIsms | 40 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | | 14 | | | 15 | | | 16 | | | 17 | | | 18 | | | 19 | | | 20 | | | 21 | | | 22 | | | 23 | | | 24 | |
| | highlights | | 0 | "measured" | | 1 | "perfect" | | 2 | "pulse" | | 3 | "traced" | | 4 | "standard" | | 5 | "aligned" | | 6 | "magnetic" | | 7 | "etched" | | 8 | "resonance" | | 9 | "structure" | | 10 | "crystalline" | | 11 | "scanned" | | 12 | "echoed" | | 13 | "calibrated" | | 14 | "weight" | | 15 | "predictable" | | 16 | "processed" | | 17 | "constructed" | | 18 | "framework" | | 19 | "calculated" | | 20 | "aftermath" | | 21 | "parameters" | | 22 | "mechanical" | | 23 | "systematic" | | 24 | "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 | 0 | | narrationSentences | 336 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 336 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 336 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 20 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1848 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 2 | | unquotedAttributions | 2 | | matches | | 0 | "You expect me to believe a controlled group operates in a flooded tube station, Vance called out." | | 1 | "You fixate on trinkets while actual threats slip through your fingers, Vance snapped." |
| |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 45 | | wordCount | 1848 | | uniqueNames | 15 | | maxNameDensity | 0.65 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Marcus | 1 | | Vance | 7 | | Metropolitan | 1 | | Police | 1 | | Greek | 1 | | London | 1 | | Morris | 1 | | Veil | 2 | | Market | 3 | | Masonic | 1 | | You | 7 | | Extraction | 3 | | Grid | 3 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Marcus" | | 3 | "Vance" | | 4 | "Police" | | 5 | "Morris" | | 6 | "Market" | | 7 | "You" | | 8 | "Extraction" | | 9 | "Grid" |
| | places | | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 144 | | 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 | 1848 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 336 | | matches | (empty) | |
| 89.15% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 19 | | mean | 97.26 | | std | 44.94 | | cv | 0.462 | | sampleLengths | | 0 | 87 | | 1 | 73 | | 2 | 77 | | 3 | 60 | | 4 | 77 | | 5 | 47 | | 6 | 95 | | 7 | 41 | | 8 | 116 | | 9 | 105 | | 10 | 59 | | 11 | 145 | | 12 | 56 | | 13 | 142 | | 14 | 61 | | 15 | 200 | | 16 | 159 | | 17 | 75 | | 18 | 173 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 336 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 378 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 336 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1849 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 19 | | adverbRatio | 0.010275824770146024 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.007030827474310438 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 336 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 336 | | mean | 5.5 | | std | 2.77 | | cv | 0.504 | | sampleLengths | | 0 | 9 | | 1 | 11 | | 2 | 9 | | 3 | 9 | | 4 | 15 | | 5 | 4 | | 6 | 4 | | 7 | 6 | | 8 | 20 | | 9 | 10 | | 10 | 3 | | 11 | 6 | | 12 | 7 | | 13 | 8 | | 14 | 7 | | 15 | 10 | | 16 | 4 | | 17 | 9 | | 18 | 9 | | 19 | 6 | | 20 | 7 | | 21 | 3 | | 22 | 7 | | 23 | 4 | | 24 | 3 | | 25 | 3 | | 26 | 7 | | 27 | 2 | | 28 | 2 | | 29 | 10 | | 30 | 9 | | 31 | 8 | | 32 | 1 | | 33 | 5 | | 34 | 8 | | 35 | 11 | | 36 | 5 | | 37 | 5 | | 38 | 8 | | 39 | 4 | | 40 | 5 | | 41 | 6 | | 42 | 8 | | 43 | 4 | | 44 | 10 | | 45 | 8 | | 46 | 2 | | 47 | 6 | | 48 | 6 | | 49 | 6 |
| |
| 98.51% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 5 | | diversityRatio | 0.7380952380952381 | | totalSentences | 336 | | uniqueOpeners | 248 | |
| 33.67% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 297 | | matches | | 0 | "Only serious players cross the" | | 1 | "Away from true north." | | 2 | "Only authorized operatives entered the" |
| | ratio | 0.01 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 36 | | totalSentences | 297 | | matches | | 0 | "Her boots struck the tiled" | | 1 | "She checked the worn leather" | | 2 | "They pointed flashlights at scorch" | | 3 | "She traced the line of" | | 4 | "Her thumb brushed the tile" | | 5 | "He wiped rain from his" | | 6 | "We call it a burn" | | 7 | "She rose and paced the" | | 8 | "She crouched and lifted a" | | 9 | "You expect me to believe" | | 10 | "We close the file with" | | 11 | "She reached out and tapped" | | 12 | "She noted the placement relative" | | 13 | "She recognized the style." | | 14 | "They formed a barrier diagram." | | 15 | "She ran a palm along" | | 16 | "She returned to the central" | | 17 | "You fixate on trinkets while" | | 18 | "We process the site systematically." | | 19 | "Your instincts favor conspiracy because" |
| | ratio | 0.121 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 204 | | totalSentences | 297 | | matches | | 0 | "Water seeped through cracked plaster" | | 1 | "Detective Harlow Quinn stepped over" | | 2 | "Her boots struck the tiled" | | 3 | "This place defied routine." | | 4 | "The air tasted metallic." | | 5 | "Ozone clung to the damp" | | 6 | "Emergency bulbs cast sickly yellow" | | 7 | "She checked the worn leather" | | 8 | "The city above operated on" | | 9 | "Quinn adjusted her coat collar" | | 10 | "They pointed flashlights at scorch" | | 11 | "Tape covered ventilation grates." | | 12 | "Canvas drop cloths lay folded" | | 13 | "Everything sat undisturbed since dispatch" | | 14 | "Quinn knelt beside the central" | | 15 | "A man in a tailored" | | 16 | "Lips stained silver." | | 17 | "Chest rose and fell in" | | 18 | "Pulse weak but present." | | 19 | "She traced the line of" |
| | ratio | 0.687 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 297 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 27 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 0 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |