Run: 2026-02-19-01KHTR10HBWM69FBMSGZSDZTFQ

Run Details

Duration:
21.0s
Cost:
$0.008258

Model input

System
You are an expert fiction writer. Always keep the following rules in mind: - Write in past tense and use General English spelling, grammar, and colloquialisms/slang. - Write in active voice - Always follow the "show, don't tell" principle. - Avoid adverbs and cliches and overused/commonly used phrases. Aim for fresh and original descriptions. - Convey events and story through dialogue. - Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. - Skip "he/she said said" dialogue tags and convey people's actions or face expressions through their speech - Avoid mushy dialog and descriptions, have dialogue always continue the action, never stall or add unnecessary fluff. Vary the descriptions to not repeat yourself. - Put dialogue on its own paragraph to separate scene and action. - Reduce indicators of uncertainty like "trying" or "maybe" When writing text: - NEVER conclude the scene on your own, follow the beat instructions very closely. - NEVER end with foreshadowing. - NEVER write further than what I prompt you with. - AVOID imagining possible endings, NEVER deviate from the instructions. - STOP EARLY if the continuation contains what was required in the instructions. You do not need to fill out the full amount of words possible.
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <codex> <character id="4416" name="Detective Harlow Quinn" aliases="Quinn, Detective Quinn, Harlow" pronouns="she/her" species="Human" storyRole="Antagonist"> <eyeColor>brown</eyeColor> <hair>closely cropped salt-and-pepper</hair> <height>5'9"</height> <age>41</age> <jawline>sharp jaw</jawline> <watch>worn leather watch on her left wrist</watch> <bearing>military precision</bearing> <description>A Metropolitan Police detective with 18 years of decorated service who suspects the clique is involved in criminal activity.</description> <backstory>She lost her partner, DS Morris, during a case three years ago under unexplained circumstances that had supernatural origins she doesn't yet understand.</backstory> </character> <character id="4417" name="Tomás Herrera" aliases="Tommy, Tomás, Herrera" pronouns="he/him" species="Human" storyRole="Support"> <eyeColor>warm brown</eyeColor> <hair>short curly dark brown</hair> <height>5'10"</height> <age>29</age> <skinTone>olive skin</skinTone> <scar>scar running along his left forearm from a knife attack</scar> <medallion>Saint Christopher medallion around his neck</medallion> <birthplace>Seville, Spain</birthplace> <description>A former paramedic who provides off-the-books medical care for the clique.</description> <backstory>Born in Seville, Spain. He moved to London to work for the NHS but lost his license after administering unauthorized treatments to supernatural patients.</backstory> </character> <location id="854" name="The Raven's Nest" aliases="the Nest, Raven, Silas' bar" realm="Earth"> <signColor>distinctive green neon sign above the entrance</signColor> <secretRoom>hidden back room accessible through a bookshelf, used for clandestine meetings</secretRoom> <description>A dimly lit bar in Soho, London. The walls are covered with old maps and black-and-white photographs.</description> </location> <location id="856" name="The Veil Market" aliases="Veil Market, the Market, underground market" realm="Earth"> <currentLocation>abandoned Tube station beneath Camden</currentLocation> <entryRequirement>bone token</entryRequirement> <schedule>moves locations every full moon</schedule> <description>A hidden supernatural black market that sells enchanted goods, banned alchemical substances, and information.</description> </location> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the thriller genre. A detective is pursuing a suspect through rain-soaked city streets at night. The chase takes them from the street level down into an underground market. The detective must decide whether to follow the suspect into unfamiliar and potentially dangerous territory. Use the codex entries above as your world reference for characters and setting. Only include the prose of the scene without any markdown or commentary. </instructions>

Model response

AI
The rain fell in sheets, turning the pavement into a slick mirror that fractured the neon glow of Soho’s nightlife. Harlow Quinn’s boots splashed through puddles, her breath coming in sharp, controlled bursts. The suspect—some wiry kid with a hood pulled low—darted ahead, weaving between late-night stragglers and delivery cyclists. He had the desperate speed of someone who knew the city’s veins better than his own. Quinn’s fingers twitched near her sidearm, but she didn’t draw. Not yet. The kid wasn’t armed, not that she could see. Just a runner. A messenger, maybe. But messengers carried things people didn’t want found. Ahead, the green neon sign of *The Raven’s Nest* flickered, its glow bleeding into the downpour. The kid skidded around the corner, vanishing into the alley beside the bar. Quinn followed, her pulse steady despite the burn in her lungs. The alley stank of wet trash and stale beer. A fire escape ladder dangled just out of reach, but the kid didn’t climb. Instead, he crouched beside a rusted service door, fingers scrambling at the lock. Quinn slowed, her hand resting on the grip of her gun. "Met Police. Step away from the door." The kid froze. Then, without turning, he laughed—a wet, rattling sound. "You’re gonna wanna let this one go, Detective." She took another step. "Not happening. Hands where I can see them." The lock clicked. The door swung open, revealing a narrow staircase descending into darkness. The kid bolted down the steps before Quinn could react. She cursed, holstering her weapon. No way she was letting him disappear into whatever hellhole waited below. The stairs were slick, the air thick with the scent of damp stone and something older—copper, maybe, or ozone. The kid’s footsteps echoed ahead, but the darkness swallowed him whole. Quinn pulled out her phone, thumbing the flashlight app. The beam cut through the gloom, illuminating peeling paint and graffiti that wasn’t in any language she recognized. At the bottom, a tunnel stretched left and right. The kid’s footsteps had faded. Quinn exhaled, listening. A distant hum vibrated through the walls—machinery? Voices? She turned left, following the sound. The tunnel opened into a cavernous space, the ceiling lost in shadow. Stalls lined the walls, their wares hidden under tarps or locked behind rusted cages. A few figures moved in the dim light, their shapes wrong—too tall, too thin, their limbs bending at angles that made Quinn’s stomach clench. One turned, its face a smooth, featureless oval. It tilted its head, then melted back into the crowd. The kid was nowhere in sight. A woman with silver-streaked braids stepped into Quinn’s path, her smile too wide, too many teeth. "Lost, are we?" Quinn’s hand drifted toward her gun again. "I’m looking for someone." The woman’s grin didn’t waver. "Aren’t we all?" She held out a palm. "Token." Quinn blinked. "What?" The woman’s fingers twitched. "No token, no passage. Rules are rules." Behind her, the crowd shifted, murmuring. Quinn’s pulse spiked. She didn’t have a token. She didn’t even know what the hell a token was. But the kid was here somewhere, and she wasn’t leaving without answers. She reached into her pocket, pulled out her badge. "Metropolitan Police. Step aside." The woman’s smile faltered. The crowd stilled. Then, from the shadows, a voice—raspy, amused. "Oh, Detective. You really shouldn’t have followed him down here." Quinn turned. The kid stood a few paces away, his hood thrown back now, his face pale in the dim light. Behind him, a man with a scarred face and a Saint Christopher medallion leaned against a stall, arms crossed. Tomás Herrera. Former paramedic. Off-the-books medic for the clique Quinn had been watching for months. Her fingers curled into fists. "You’re under arrest." Herrera pushed off the stall, his expression unreadable. "For what, exactly?" "Obstruction. Conspiracy." Quinn’s voice was steel. "Murder." The crowd murmured again, louder this time. Herrera didn’t flinch. "You’re out of your depth, Detective." The kid shifted, his gaze darting between them. "Tommy—" Herrera held up a hand. "She’s not leaving." Quinn’s jaw tightened. She could feel the weight of the crowd, the wrongness of the air. She was alone down here. No backup. No witnesses. But she wasn’t backing down. She took a step forward. "Try and stop me."
total_cost0.008258

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences19
tagDensity0.053
leniency0.105
rawRatio0
effectiveRatio0
93.09% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount724
totalAiIsmAdverbs1
found
0
adverb"really"
count1
highlights
0"really"
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)
10.22% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount724
totalAiIsms13
found
0
word"fractured"
count1
1
word"flickered"
count1
2
word"pulse"
count2
3
word"footsteps"
count2
4
word"echoed"
count1
5
word"gloom"
count1
6
word"vibrated"
count1
7
word"stomach"
count1
8
word"unreadable"
count1
9
word"could feel"
count1
10
word"weight"
count1
highlights
0"fractured"
1"flickered"
2"pulse"
3"footsteps"
4"echoed"
5"gloom"
6"vibrated"
7"stomach"
8"unreadable"
9"could feel"
10"weight"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches0
maxInWindow0
found(empty)
highlights(empty)
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences76
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount0
narrationSentences76
filterMatches(empty)
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences93
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen25
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords3
totalWords713
ratio0.004
matches
0"The Raven’s Nest"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions1
matches
0"Then, without turning, he laughed—a wet, rattling sound."
23.82% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions24
wordCount634
uniqueNames6
maxNameDensity2.52
worstName"Quinn"
maxWindowNameDensity4
worstWindowName"Quinn"
discoveredNames
Soho1
Quinn16
Raven1
Saint1
Christopher1
Herrera4
persons
0"Quinn"
1"Raven"
2"Saint"
3"Christopher"
4"Herrera"
places
0"Soho"
globalScore0.238
windowScore0.333
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences45
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount713
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences93
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs30
mean23.77
std19.86
cv0.836
sampleLengths
066
135
276
318
419
512
641
757
831
968
106
1119
1211
1314
143
1511
1636
1713
1824
1940
2015
218
2211
237
2416
259
268
2725
285
299
100.00% Passive voice overuse
Target: ≤2% passive sentences
passiveCount0
totalSentences76
matches(empty)
16.51% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount3
totalVerbs109
matches
0"was letting"
1"wasn’t leaving"
2"wasn’t backing"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount7
semicolonCount0
flaggedSentences6
totalSentences93
ratio0.065
matches
0"The suspect—some wiry kid with a hood pulled low—darted ahead, weaving between late-night stragglers and delivery cyclists."
1"Then, without turning, he laughed—a wet, rattling sound."
2"The stairs were slick, the air thick with the scent of damp stone and something older—copper, maybe, or ozone."
3"A distant hum vibrated through the walls—machinery?"
4"A few figures moved in the dim light, their shapes wrong—too tall, too thin, their limbs bending at angles that made Quinn’s stomach clench."
5"Then, from the shadows, a voice—raspy, amused."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount645
adjectiveStacks0
stackExamples(empty)
adverbCount20
adverbRatio0.031007751937984496
lyAdverbCount0
lyAdverbRatio0
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences93
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences93
mean7.67
std5.12
cv0.668
sampleLengths
020
113
217
316
410
52
69
73
83
98
1016
1113
1211
139
1414
1513
1611
177
183
198
208
214
228
233
2411
2510
265
2712
2819
2911
309
3118
329
335
343
357
361
376
3812
3914
4024
418
4210
436
4416
453
467
474
485
498
60.57% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats7
diversityRatio0.40860215053763443
totalSentences93
uniqueOpeners38
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences68
matches
0"Just a runner."
1"Instead, he crouched beside a"
2"Then, without turning, he laughed—a"
3"Then, from the shadows, a"
ratio0.059
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount13
totalSentences68
matches
0"He had the desperate speed"
1"She took another step."
2"She cursed, holstering her weapon."
3"She turned left, following the"
4"It tilted its head, then"
5"She held out a palm"
6"She didn’t have a token."
7"She didn’t even know what"
8"She reached into her pocket,"
9"Her fingers curled into fists."
10"She could feel the weight"
11"She was alone down here."
12"She took a step forward."
ratio0.191
70.29% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount53
totalSentences68
matches
0"The rain fell in sheets,"
1"Harlow Quinn’s boots splashed through"
2"The suspect—some wiry kid with"
3"He had the desperate speed"
4"Quinn’s fingers twitched near her"
5"The kid wasn’t armed, not"
6"A messenger, maybe."
7"The kid skidded around the"
8"Quinn followed, her pulse steady"
9"The alley stank of wet"
10"A fire escape ladder dangled"
11"Quinn slowed, her hand resting"
12"The kid froze."
13"She took another step."
14"The lock clicked."
15"The door swung open, revealing"
16"The kid bolted down the"
17"She cursed, holstering her weapon."
18"The stairs were slick, the"
19"The kid’s footsteps echoed ahead,"
ratio0.779
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences68
matches(empty)
ratio0
40.82% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences28
technicalSentenceCount4
matches
0"The rain fell in sheets, turning the pavement into a slick mirror that fractured the neon glow of Soho’s nightlife."
1"He had the desperate speed of someone who knew the city’s veins better than his own."
2"The beam cut through the gloom, illuminating peeling paint and graffiti that wasn’t in any language she recognized."
3"A few figures moved in the dim light, their shapes wrong—too tall, too thin, their limbs bending at angles that made Quinn’s stomach clench."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
80.5110%