| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 19 | | tagDensity | 0.526 | | leniency | 1 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 92.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 645 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 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) | |
| 37.98% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 645 | | totalAiIsms | 8 | | found | | | highlights | | 0 | "perfect" | | 1 | "traced" | | 2 | "scanning" | | 3 | "scanned" | | 4 | "silence" | | 5 | "beacon" | | 6 | "crystal" |
| |
| 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 | 1 | | narrationSentences | 57 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 57 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 18.57% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 65 | | gibberishSentences | 8 | | adjustedGibberishSentences | 8 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 59 | | ratio | 0.123 | | matches | | 0 | "\"Let's call in Crim Pod. Find me<liu>the boots.<PEON>also need nearby security feeds. should</o<NoPEON>m, this way\"" | | 1 | "\"what w<PEON>r you thinking? manusce<LIU>ptive ending, but al<o<NoPEON>es clearly not su<LIU>icide.\" <olmoLight><liu> wondersabout the angle of youth Pi<o<NoPEON>conse</olmoDark>" | | 2 | "*Consistent minds,* Quinn mused <olmoGray><LIU>oloam thoughtsknowle<o<NoPEON>ing Mary Consistency actuar<o<NoPEON>CC records—super rec<o<NoPEON> SAC for 5 years, no criminal r<o<No…" | | 3 | "t keeps circle spaces, pic</oliange><LIU>lette or weapons beacon,” façade[oliange><o<olmoSefoldColored@section,\" es, no, no, don't get me</o<NoPEON>ing alike." | | 4 | "Brad</oliange><o<NoPEON>ing in all wri<o<NoPEON>ng maxi</oliange><oliange><NoPEON>t to see the</o<o<No<oliange><oliange><oliange>< oliangeline, \"consistent with fact</o<oliange><o<…" | | 5 | "<oliPurple><oluescrRE<Sbur>l trot de bsy in <oliange><oliange><oliange><oULUST-S>n ge <oLUST-ag<oLUST-tang><oliange><oliang <oliange><oliange><oliange><olmiStrength><oliange><olian…" | | 6 | "<oliang<oliangee</oR crystal hound, olivang>ng to the natively \u0014 ólHt _these</oliangesAmnliangereate ol Olive<oliangee>ngted obs<olivf<oliangeSw permit me to stf_srepresentech ,_st…" | | 7 | "from olistright</oliangand the hound,APos<AL Mild-maydfforence,<o,oliangeelent knowledge Several these<PEON> Claude O-ass’état</PRESERVE<IEL its and devidderonols<PEON>s. But o,stq…" |
| |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 7 | | markdownWords | 30 | | totalWords | 640 | | ratio | 0.047 | | matches | | 0 | "What exactly happened here?" | | 1 | "What'' complimented" | | 2 | "What secrets had she died taking with her?" | | 3 | "Consistent minds," | | 4 | "these</oliangesAmnliangereate ol Olive<oliangee>ngted obs<olivf<oliangeSw permit me to stf" | | 5 | "the||lectual world. hisi" | | 6 | "sassy<oliange!</oliange>Pat, _I-faced par-ats" |
| |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 7 | | unquotedAttributions | 0 | | matches | (empty) | |
| 54.26% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 22 | | wordCount | 470 | | uniqueNames | 10 | | maxNameDensity | 1.91 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 9 | | Simmons | 5 | | Greek | 1 | | Sigma | 1 | | Yard | 1 | | Mary | 1 | | Consistency | 1 | | Several | 1 | | Claude | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Simmons" | | 3 | "Mary" | | 4 | "Consistency" | | 5 | "Claude" |
| | places | (empty) | | globalScore | 0.543 | | windowScore | 0.667 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 41 | | 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 | 640 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 65 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 22 | | mean | 29.09 | | std | 20.97 | | cv | 0.721 | | sampleLengths | | 0 | 10 | | 1 | 46 | | 2 | 22 | | 3 | 28 | | 4 | 28 | | 5 | 15 | | 6 | 17 | | 7 | 33 | | 8 | 8 | | 9 | 16 | | 10 | 23 | | 11 | 18 | | 12 | 19 | | 13 | 15 | | 14 | 50 | | 15 | 52 | | 16 | 14 | | 17 | 58 | | 18 | 18 | | 19 | 102 | | 20 | 21 | | 21 | 27 |
| |
| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 57 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 73 | | matches | (empty) | |
| 10.99% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 1 | | flaggedSentences | 3 | | totalSentences | 65 | | ratio | 0.046 | | matches | | 0 | "Tagged with a symbol - Greek letter Sigma, but reversed." | | 1 | "*Consistent minds,* Quinn mused <olmoGray><LIU>oloam thoughtsknowle<o<NoPEON>ing Mary Consistency actuar<o<NoPEON>CC records—super rec<o<NoPEON> SAC for 5 years, no criminal r<o<NoPEON> recs beyond that.\" CrimSC Dornesviels added s<o<NoPEON>on the body.</olmoPurple><LIU>novariet</olmoPurple><o<NoPEON>ancee</o<o<NoPEON>d \"Not your average suicid<o<NoPEON> toloam.</OLUST_section,\" the-artboard.<o<No<olmoGrayProtected><ori><oliange>on</o<NoPEON>Curious...</oliange><o<NoPE<oliange><o<NoPEON>e. Th<o<NoPEON>n it was</oliange><o<o<NoPEON>to suicide. But<o<o<NoPEON>hephrase</o<NoPEON> of a possible</o<olly, Quinn finally broke the silence. \"I'll need you to run a registry check on the vic." | | 2 | "from olistright</oliangand the hound,APos<AL Mild-maydfforence,<o,oliangeelent knowledge Several these<PEON> Claude O-ass’état</PRESERVE<IEL its and devidderonols<PEON>s. But o,stqently<AL.Oivcupes a peculiar pole<oliange?in occu<oliangesm Satur,_the||lectual world. hisi_a<o n;iage_sassy<oliange!</oliange>Pat, _I-faced par-ats_Olhngng/ed _tion, _but<o<oli>" |
| |
| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 443 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 10 | | adverbRatio | 0.022573363431151242 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.01580135440180587 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 65 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 65 | | mean | 9.85 | | std | 8.17 | | cv | 0.83 | | sampleLengths | | 0 | 10 | | 1 | 14 | | 2 | 10 | | 3 | 11 | | 4 | 11 | | 5 | 11 | | 6 | 11 | | 7 | 9 | | 8 | 7 | | 9 | 6 | | 10 | 6 | | 11 | 5 | | 12 | 4 | | 13 | 7 | | 14 | 10 | | 15 | 2 | | 16 | 15 | | 17 | 7 | | 18 | 6 | | 19 | 4 | | 20 | 7 | | 21 | 11 | | 22 | 5 | | 23 | 3 | | 24 | 7 | | 25 | 5 | | 26 | 3 | | 27 | 8 | | 28 | 8 | | 29 | 8 | | 30 | 15 | | 31 | 8 | | 32 | 10 | | 33 | 7 | | 34 | 12 | | 35 | 3 | | 36 | 12 | | 37 | 11 | | 38 | 8 | | 39 | 8 | | 40 | 9 | | 41 | 8 | | 42 | 6 | | 43 | 13 | | 44 | 5 | | 45 | 6 | | 46 | 1 | | 47 | 2 | | 48 | 3 | | 49 | 1 |
| |
| 95.38% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 3 | | diversityRatio | 0.6153846153846154 | | totalSentences | 65 | | uniqueOpeners | 40 | |
| 65.36% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 1 | | totalSentences | 51 | | matches | | 0 | "Often that's all it took." |
| | ratio | 0.02 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 6 | | totalSentences | 51 | | matches | | 0 | "She crouched, eyeing the noose." | | 1 | "He traced the symbol" | | 2 | "She rolled the woman over." | | 3 | "She checked the rope bite" | | 4 | "It matched the noose in" | | 5 | "She snapped a photo of" |
| | ratio | 0.118 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 34 | | totalSentences | 51 | | matches | | 0 | "The body hung like a" | | 1 | "Detective Harlow Quinn paused at" | | 2 | "The corpse swayed gently, caught" | | 3 | "A woman in her mid-30s," | | 4 | "A tattoo of a raven" | | 5 | "Detective Simmons said from the" | | 6 | "Quinn circled the body, boots" | | 7 | "The old theatre had seen" | | 8 | "Drapes filthy, seats ripped, stage" | | 9 | "She crouched, eyeing the noose." | | 10 | "Quinn stood on a chair," | | 11 | "Simmons joined her, peering at" | | 12 | "He traced the symbol" | | 13 | "Quinn grunted in agreement, untying" | | 14 | "The body thumped to the" | | 15 | "She rolled the woman over." | | 16 | "Contusions on throat." | | 17 | "Simmons nodded, snapping photos with" | | 18 | "The crime scene AI whirred," | | 19 | "Simmons read from his pad" |
| | ratio | 0.667 | |
| 98.04% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 1 | | totalSentences | 51 | | matches | | 0 | "<oliang<oliangee</oR crystal hound, olivang>ng to" |
| | ratio | 0.02 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 12 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 75.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 1 | | matches | | 0 | "Quinn circled, boots crunching on debris" |
| |
| 97.37% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 3 | | fancyCount | 1 | | fancyTags | | 0 | "Quinn observed (observe)" |
| | dialogueSentences | 19 | | tagDensity | 0.158 | | leniency | 0.316 | | rawRatio | 0.333 | | effectiveRatio | 0.105 | |