Run: 2026-10-09-01M4G133FZGDYBJF3JGTER4Y1G

Run Details

Duration:
13.4s
Cost:
$0.001037
Provider:
Claude Platform on AWS

Model input

System
You are an expert novelist with a gift for vivid, immersive prose. You write in tight third-person limited point of view with a strong narrative voice. Your prose is clean, precise, and engaging — you favor strong verbs, concrete details, and natural-sounding dialogue. You will be given a story bible (codex) containing canonical details about characters, locations, items, and lore. Use these details to ground your writing in the established world. Stay consistent with the codex. Write only the prose of the scene. Do not include titles, chapter headings, markdown formatting, or any commentary before or after the scene.
User/Human
<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> 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.

Model response

AI
The rain had settled into the kind of steady, indifferent fall that London reserved for its worst nights. Harlow Quinn kept her pace even along Greek Street, her shoes slapping the wet paving, the green neon of the Raven's Nest bleeding across the puddles behind her. Ahead, the man she had come to Soho to find moved like someone who had run from worse things than police. Tomás Herrera did not look back. He took the corner onto Old Compton Street with his collar up and one hand pressed flat against his chest, as though he were holding something in place. The Saint Christopher medallion would be swinging against his sternum with every stride. She had seen it in the file photographs, the scar on his left forearm pale in the flash of the camera. "Herrera," she called. "Stop. Nobody needs to get hurt tonight." He glanced over his shoulder, and for one second his face was open and frightened. Then he was gone again, cutting left into a service alley so narrow the bins nearly blocked it. Quinn followed. Her left wrist ached where the worn leather watch strap had soaked through. She ignored it. Eighteen years had taught her that discomfort was information, and that the body would tell you what the mind wanted to hide. Her mind wanted to turn around. Her body had already committed. The alley opened onto a loading yard, then a road, then the long slope of Camden's streets under the rain. Herrera was faster than his file suggested. Ex-paramedics, she thought, knew how to move through crowds and how to disappear into them. He vaulted a low wall and dropped onto the towpath beside the canal. Quinn took the wall more carefully, her boot finding purchase on wet brick, and landed hard enough to jar her knees. Across the water, the lock gates groaned. Brightly lit barges bobbed beneath strings of bulbs, the music from a late bar thudding softly over the surface. Herrera ran toward the crowd there and then veered sharply off the path, toward a brick arch half-hidden behind scaffolding. She reached it thirty seconds later and stopped. The arch sheltered a stairwell that should not have existed. Iron railings curled down into darkness, and at the bottom, where the stone should have ended, a faded tiled wall bore the ghost of a station name. The sign above it had been painted over. Someone had scratched a symbol into the tiles: a small circle with three lines bisecting it, like a bone seen end-on. Cold air rose from below. It carried the smell of wet stone, sweet smoke, something metallic and bitter underneath. Faint voices drifted up, rising and falling in several languages at once. Quinn pulled out her phone. No signal. Of course. She checked the time: 23:47. She keyed the radio on her hip and got only hiss. Three years ago, she had stood in a stairwell very like this one, in a building that had no business being where it was. She had gone down after DS Morris because he had told her to wait and she had not waited. She had come back up alone, and the report she filed had been a fiction written in careful, practiced language, because the truth had no column on any form she knew. Morris had simply been gone. His car had been found on Tottenham Court Road with the keys still in the ignition and the engine still warm. Nobody had ever explained that. Nobody at the Met had wanted her to try. Now a man she had been tracking for six weeks had walked straight down into the dark like it was the most natural thing in the world. She could call it in. Describe a stairwell that wasn't on any map, a fugitive who might be running toward something she could not name, a symbol she had never seen in any case file. They would send a car, then a second car, and by morning someone would have decided she had been drinking or sleeping badly, and her name would be on a review board. Or she could go down alone, unarmed in any way that mattered, into a place with no signal and no backup, a place that had already taken her partner. Quinn looked at the scratched symbol. Then at the darkness beneath it. Her jaw tightened, and she felt the old discipline settle over her like a coat. She descended the first step. The iron was slick under her palm. The second step rang softly, and somewhere far below, a voice stopped mid-sentence, as if listening. "Herrera," she said quietly, to nobody who could hear her, and went down anyway.
created_at"2026-10-09T09:49:43.813Z" 10/9/2026, 9:49:43 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2282
moderation_latency(null)
generation_time13398
tokens_prompt888
tokens_completion1285
native_tokens_prompt1266
native_tokens_completion1820
native_tokens_completion_images(null)
native_tokens_reasoning407
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"
usage0.0010366
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791539383-yynnAV9YAtNy1f4hwEns"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791539383-AYTnF1ES8QquBlMB1VWW"
upstream_id"msg_011CfrTKm9RT34SgQbCBghR2"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfrTKm9RT34SgQbCBghR2"
is_byokfalse
latency750
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0010366
cache_discount(null)
upstream_inference_cost0
provider_name"Claude Platform on AWS"
response_cache_source_id(null)
data_region"global"
workspace_id"97e315e5-d303-487d-83c1-83180e8a13d4"

Evaluation details

Result Evaluator Details Meta Data
0.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags2
adverbTagCount1
adverbTags
0"she said quietly [quietly]"
dialogueSentences3
tagDensity0.667
leniency1
rawRatio0.5
effectiveRatio0.5
68.23% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount787
totalAiIsmAdverbs5
found
0
adverb"carefully"
count1
1
adverb"softly"
count2
2
adverb"sharply"
count1
3
adverb"very"
count1
highlights
0"carefully"
1"softly"
2"sharply"
3"very"
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)
100.00% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount787
totalAiIsms0
found(empty)
highlights(empty)
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
narrationSentences56
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences56
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences57
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen32
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords787
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions26
wordCount778
uniqueNames18
maxNameDensity0.64
worstName"Quinn"
maxWindowNameDensity1
worstWindowName"Quinn"
discoveredNames
London1
Quinn5
Greek1
Street2
Raven1
Nest1
Soho1
Herrera3
Old1
Compton1
Saint1
Christopher1
Camden1
Morris2
Tottenham1
Court1
Road1
Met1
persons
0"Quinn"
1"Raven"
2"Nest"
3"Herrera"
4"Saint"
5"Christopher"
6"Camden"
7"Morris"
places
0"London"
1"Greek"
2"Street"
3"Soho"
4"Old"
5"Compton"
6"Tottenham"
7"Court"
8"Road"
globalScore1
windowScore1
91.86% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences43
glossingSentenceCount1
matches
0"as if listening"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount787
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences57
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs18
mean43.72
std27.03
cv0.618
sampleLengths
067
168
210
333
451
576
646
78
866
931
1025
11114
1227
1367
1429
1527
1628
1714
80.20% Passive voice overuse
Target: ≤2% passive sentences
passiveCount4
totalSentences56
matches
0"was gone"
1"been painted"
2"been gone"
3"been found"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs129
matches
0"were holding"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences57
ratio0
matches(empty)
95.84% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount782
adjectiveStacks0
stackExamples(empty)
adverbCount35
adverbRatio0.04475703324808184
lyAdverbCount10
lyAdverbRatio0.01278772378516624
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences57
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences57
mean13.81
std8.68
cv0.629
sampleLengths
018
128
221
36
428
513
621
73
87
915
1018
112
1213
133
1422
156
165
1720
187
1915
2013
2121
227
2319
2420
258
2610
2727
288
2921
305
3114
3212
335
342
352
365
3711
3824
3919
4031
415
4221
435
449
4527
465
4730
4832
4929
78.95% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.5263157894736842
totalSentences57
uniqueOpeners30
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences52
matches
0"Then he was gone again,"
1"Brightly lit barges bobbed beneath"
2"Then at the darkness beneath"
ratio0.058
66.15% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount20
totalSentences52
matches
0"He took the corner onto"
1"She had seen it in"
2"He glanced over his shoulder,"
3"Her left wrist ached where"
4"She ignored it."
5"Her mind wanted to turn"
6"Her body had already committed."
7"He vaulted a low wall"
8"She reached it thirty seconds"
9"It carried the smell of"
10"She checked the time: 23:47."
11"She keyed the radio on"
12"She had gone down after"
13"She had come back up"
14"His car had been found"
15"She could call it in."
16"They would send a car,"
17"Her jaw tightened, and she"
18"She descended the first step."
19"she said quietly, to nobody"
ratio0.385
85.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount39
totalSentences52
matches
0"The rain had settled into"
1"Harlow Quinn kept her pace"
2"Tomás Herrera did not look"
3"He took the corner onto"
4"The Saint Christopher medallion would"
5"She had seen it in"
6"He glanced over his shoulder,"
7"Her left wrist ached where"
8"She ignored it."
9"Her mind wanted to turn"
10"Her body had already committed."
11"The alley opened onto a"
12"Herrera was faster than his"
13"He vaulted a low wall"
14"Quinn took the wall more"
15"Herrera ran toward the crowd"
16"She reached it thirty seconds"
17"The arch sheltered a stairwell"
18"Iron railings curled down into"
19"The sign above it had"
ratio0.75
96.15% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences52
matches
0"Now a man she had"
ratio0.019
53.57% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences32
technicalSentenceCount4
matches
0"Harlow Quinn kept her pace even along Greek Street, her shoes slapping the wet paving, the green neon of the Raven's Nest bleeding across the puddles behind her…"
1"He took the corner onto Old Compton Street with his collar up and one hand pressed flat against his chest, as though he were holding something in place."
2"Describe a stairwell that wasn't on any map, a fugitive who might be running toward something she could not name, a symbol she had never seen in any case file."
3"The second step rang softly, and somewhere far below, a voice stopped mid-sentence, as if listening."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount0
fancyTags(empty)
dialogueSentences3
tagDensity0.667
leniency1
rawRatio0
effectiveRatio0
90.5320%