Run: 2026-06-01-01KT1HG1A65BVYA70CJ99ZKAY5

Run Details

Duration:
42.1s
Cost:
$0.062145
Provider:
Anthropic

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 came down in sheets, turning the Camden pavement into a black mirror that swallowed the streetlights and spat them back fractured. Detective Harlow Quinn ran with her coat plastered to her shoulders, water streaming off her cropped hair, her breath ragged in the cold. Ahead of her, twenty metres and closing, the man in the dark jacket cut left down an alley. "Herrera!" Her voice tore against the downpour. "Stop, police!" He didn't stop. They never did. She'd been watching the bar for three weeks. The Raven's Nest, that green neon sign bleeding through the Soho fog, the same faces drifting in and out at hours no honest business kept. Tonight she'd followed Tomás Herrera from the back door, the former paramedic with the steady hands and the lost licence, and the moment he'd clocked her reflection in a shop window he'd bolted like a man with something worse than a parking ticket to hide. Quinn hit the mouth of the alley and skidded on the slick stone. Her shoulder cracked against brick. She pushed off it without slowing. Herrera vaulted a low fence at the alley's end, his medallion flashing silver under a security light. Saint Christopher. Patron of travellers. She'd done her homework on him—Seville, the NHS, the quiet dismissal that the records buried three layers deep. "You're making this worse for yourself," she shouted, hauling herself over the fence. The metal bit her palms. He glanced back. Just once. Warm brown eyes, wide with something that wasn't quite fear—more like calculation, like a man deciding how much she could be allowed to see. Then he was gone, ducking through a gap between two derelict buildings she'd have sworn led nowhere. Quinn followed. The gap opened onto a stairwell. Old tilework, cracked and weeping moisture, a faded enamel sign half-stripped from the wall. CAMDEN. A Tube station. One of the dead ones, sealed and forgotten decades back, the kind that didn't appear on the maps the public got to see. Herrera's footsteps echoed down into the dark. She stopped at the lip of the stairs. Her watch read 11:52. Rain hammered the street behind her, ordinary and known. Below her the air pushed up warm and wrong, carrying smells she couldn't place—copper, woodsmoke, something floral and rotten underneath, like lilies left too long in a vase. Eighteen years on the job had taught her when a threshold meant more than a doorway. The hairs on her forearms stood up under the wet sleeve. DS Morris had felt this once. Three years ago. She'd seen it in his face the night before he vanished, that same animal awareness of standing at the edge of something the rule book didn't cover. He'd gone forward. She'd lost him. Quinn went forward. The stairs spiralled deeper than they had any right to. The tile gave way to bare earth shored with timber, then to walls that looked carved rather than built, smooth and seamless, lit by lamps that held no flame she could identify—just a cold green glow, the colour of that neon sign, pooling in iron cages. At the bottom, the platform of the dead station spread out before her, and Quinn's training failed to supply a single word for what she saw. Stalls. Dozens of them, packed along the curve of the platform and spilling onto the rails themselves, where no train had run in a lifetime. Canvas awnings strung between pillars. Tables heaped with glass jars that glowed and twitched, with bones laid out like cutlery, with bottles of liquid that moved against the tilt of their containers. A woman in a veil of black feathers weighed grey powder on a scale that floated above her palm. A man with too many fingers sold maps that redrew themselves as she watched. The crowd moved through it all—hooded, masked, some not wearing faces she could look at directly without her eyes sliding away. The noise hit her a beat late, as though the stairs had held it back: bartering voices in languages that scraped the inside of her skull, the chime of coins that weren't coins, a low constant hum beneath it all like a struck wire. A figure at the foot of the stairs put out a hand. "Token." It was a child's voice from beneath a hood that hid everything but a chin. A pale hand, palm up, waiting. "Detective Quinn, Metropolitan Police." She reached for the warrant card on instinct, the leather wallet she'd flashed ten thousand times. "I'm pursuing a—" "Token." The hand didn't waver. "No token, no Market. Those are the terms. You came down the stairs, Detective. You're already inside the terms." Cold spread through her gut. Across the platform, near a stall hung with hanging silver instruments, she caught the dark jacket, the curl of wet hair. Herrera, speaking fast to a broad woman behind the table, his hand pressed flat to his chest over the medallion. "That man." Quinn pointed. "He's a fugitive. I need to bring him in." "You need a token." The child cocked its hidden head. "Bone token. To enter, to leave. You have neither. So you have a choice." A faint amusement threaded the small voice. "Stay where you stand on the stairs, which belong to the upper world, and turn around. Walk back to the rain. Or step onto the platform, which belongs to us, and find a token before the moon turns, or stay until the Market moves and takes you with it." Quinn's hand found the cold weight of the service weapon under her coat. Useless. She understood that the way she understood the green light and the floating scale—not with her reason but with the older part of her that had been screaming since the top of the stairs. Herrera looked up. Found her across the crowd. And his face did something she didn't expect. It crumpled with something like pity. He shook his head once—*don't*—and turned to slip between two stalls, toward a dark archway at the far end of the platform. Morris had gone forward. Morris was gone. Quinn looked at the child's outstretched hand. She looked at the rain-cold stairs behind her, the ordinary night, the case she could close some other, safer way. She looked at Herrera's retreating back, the answer to three years of questions vanishing into the dark. Her watch ticked past midnight. She stepped down off the stairs and onto the platform.
created_at"2026-06-01T12:10:50.058Z" 6/1/2026, 12:10:50 PM
model"anthropic/claude-4.8-opus-20260528"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2783
moderation_latency435
generation_time41692
tokens_prompt1140
tokens_completion1678
native_tokens_prompt1574
native_tokens_completion2171
native_tokens_completion_images(null)
native_tokens_reasoning66
native_tokens_cached0
num_media_prompt(null)
num_input_audio_prompt(null)
num_media_completion0
num_search_results(null)
num_fetches(null)
web_search_engine(null)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"end_turn"
service_tier"default"
usage0.062145
router(null)
provider_responses
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endpoint_id"b0d3beaa-5ff7-4666-a749-b1bb2b14c916"
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is_byokfalse
latency1245
model_permaslug"anthropic/claude-4.8-opus-20260528"
provider_name"Anthropic"
status200
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request_id"req-1780315850-Cj3P7S0YufqAIQ35dFzV"
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api_type"completions"
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upstream_id"msg_01M3NLhdxYAQxzf5JHTUCtN4"
total_cost0.062145
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provider_name"Anthropic"
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Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags7
adverbTagCount0
adverbTags(empty)
dialogueSentences13
tagDensity0.538
leniency1
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1083
totalAiIsmAdverbs0
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)
76.92% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1083
totalAiIsms5
found
0
word"fractured"
count1
1
word"streaming"
count1
2
word"footsteps"
count1
3
word"echoed"
count1
4
word"weight"
count1
highlights
0"fractured"
1"streaming"
2"footsteps"
3"echoed"
4"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
narrationSentences78
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences78
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences84
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen49
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans1
markdownWords1
totalWords1077
ratio0.001
matches
0"don't"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions27
wordCount962
uniqueNames13
maxNameDensity0.83
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Camden1
Harlow1
Quinn8
Raven1
Nest1
Soho1
Tomás1
Herrera6
Christopher1
Seville1
Tube1
Morris3
Cold1
persons
0"Harlow"
1"Quinn"
2"Raven"
3"Tomás"
4"Herrera"
5"Christopher"
6"Morris"
places
0"Soho"
1"Seville"
2"Cold"
globalScore1
windowScore1
51.96% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences51
glossingSentenceCount2
matches
0"quite fear—more like calculation, like a man deciding how much she could be allowed to see"
1"something like pity"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.929
wordCount1077
matches
0"not with her reason but with the older part of her"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences84
matches
0"understood that the"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs37
mean29.11
std23.13
cv0.795
sampleLengths
046
118
29
36
478
524
640
718
829
917
102
1147
127
1349
1427
1542
163
1756
1826
1990
2065
2112
221
2321
2423
2524
2646
2713
2880
2948
3016
316
3222
337
3444
355
3610
96.27% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences78
matches
0"was gone"
1"was gone"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs154
matches(empty)
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount8
semicolonCount0
flaggedSentences7
totalSentences84
ratio0.083
matches
0"She'd done her homework on him—Seville, the NHS, the quiet dismissal that the records buried three layers deep."
1"Warm brown eyes, wide with something that wasn't quite fear—more like calculation, like a man deciding how much she could be allowed to see."
2"Below her the air pushed up warm and wrong, carrying smells she couldn't place—copper, woodsmoke, something floral and rotten underneath, like lilies left too long in a vase."
3"The tile gave way to bare earth shored with timber, then to walls that looked carved rather than built, smooth and seamless, lit by lamps that held no flame she could identify—just a cold green glow, the colour of that neon sign, pooling in iron cages."
4"The crowd moved through it all—hooded, masked, some not wearing faces she could look at directly without her eyes sliding away."
5"She understood that the way she understood the green light and the floating scale—not with her reason but with the older part of her that had been screaming since the top of the stairs."
6"He shook his head once—*don't*—and turned to slip between two stalls, toward a dark archway at the far end of the platform."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount970
adjectiveStacks0
stackExamples(empty)
adverbCount23
adverbRatio0.023711340206185566
lyAdverbCount2
lyAdverbRatio0.002061855670103093
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences84
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences84
mean12.82
std11.09
cv0.865
sampleLengths
023
123
218
37
42
53
63
78
825
945
1013
115
126
1317
142
153
1618
1713
185
193
202
2124
2217
232
246
2514
261
273
2823
297
308
314
329
3328
3416
3511
366
373
3827
393
403
413
4210
4346
4426
451
4624
475
4827
4919
81.35% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.5238095238095238
totalSentences84
uniqueOpeners44
46.95% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences71
matches
0"Then he was gone, ducking"
ratio0.014
90.42% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount23
totalSentences71
matches
0"Her voice tore against the"
1"He didn't stop."
2"They never did."
3"She'd been watching the bar"
4"Her shoulder cracked against brick."
5"She pushed off it without"
6"She'd done her homework on"
7"she shouted, hauling herself over"
8"He glanced back."
9"She stopped at the lip"
10"Her watch read 11:52."
11"She'd seen it in his"
12"He'd gone forward."
13"She'd lost him."
14"It was a child's voice"
15"She reached for the warrant"
16"She understood that the way"
17"It crumpled with something like"
18"He shook his head once—*don't*—and"
19"She looked at the rain-cold"
ratio0.324
58.59% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount57
totalSentences71
matches
0"The rain came down in"
1"Detective Harlow Quinn ran with"
2"Her voice tore against the"
3"He didn't stop."
4"They never did."
5"She'd been watching the bar"
6"The Raven's Nest, that green"
7"Tonight she'd followed Tomás Herrera"
8"Quinn hit the mouth of"
9"Her shoulder cracked against brick."
10"She pushed off it without"
11"Herrera vaulted a low fence"
12"Patron of travellers."
13"She'd done her homework on"
14"she shouted, hauling herself over"
15"The metal bit her palms."
16"He glanced back."
17"The gap opened onto a"
18"A Tube station."
19"Herrera's footsteps echoed down into"
ratio0.803
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences71
matches(empty)
ratio0
0.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences35
technicalSentenceCount7
matches
0"The rain came down in sheets, turning the Camden pavement into a black mirror that swallowed the streetlights and spat them back fractured."
1"Warm brown eyes, wide with something that wasn't quite fear—more like calculation, like a man deciding how much she could be allowed to see."
2"The tile gave way to bare earth shored with timber, then to walls that looked carved rather than built, smooth and seamless, lit by lamps that held no flame she…"
3"Tables heaped with glass jars that glowed and twitched, with bones laid out like cutlery, with bottles of liquid that moved against the tilt of their containers…"
4"The crowd moved through it all—hooded, masked, some not wearing faces she could look at directly without her eyes sliding away."
5"The noise hit her a beat late, as though the stairs had held it back: bartering voices in languages that scraped the inside of her skull, the chime of coins tha…"
6"She understood that the way she understood the green light and the floating scale—not with her reason but with the older part of her that had been screaming sin…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags7
uselessAdditionCount0
matches(empty)
73.08% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount1
fancyTags
0"she shouted (shout)"
dialogueSentences13
tagDensity0.077
leniency0.154
rawRatio1
effectiveRatio0.154
82.5177%