Run: 2026-10-09-01M4FY5E9ZEN25JXZNR712F1H1

Run Details

Duration:
12.6s
Cost:
$0.001016
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 green neon above the Raven's Nest bled into the rain, smearing across the wet pavement like something spilled. Harlow Quinn had her warrant card in one hand and her phone in the other, and she was already running when the back door of the bar banged open and Tomás Herrera came out of it at a flat sprint. She knew him from the surveillance photographs: the curly hair plastered to his forehead now, the Saint Christopher medallion swinging against his collarbone, his left sleeve pushed up to the elbow so the old knife scar showed white against olive skin. He did not look back. People who looked back were people who expected to be caught. "Metropolitan Police! Stop!" He turned into Bateman Street without slowing. Quinn cursed under her breath and followed, the soles of her boots skidding on painted lines and drain grates. Her breath came hard and even. Eighteen years of training had taught her to run the way she did everything else, with economy and no wasted motion, but the rain made every step a gamble, and the cold soaked through her coat collar in seconds. Soho fell away behind them. Shuttered shopfronts gave way to the blank brick of loading yards, then to the wider, emptier stretch of Tottenham Court Road where the traffic lights blinked amber over nobody. Tomás ran like a man who knew exactly where his feet would land. He vaulted a low railing, cut across a bus shelter, and disappeared around a corner so sharply that Quinn lost him for three long seconds before she caught the flicker of movement on the far side of the road. Her watch, the worn leather strap cold against her wrist, read 11:42. Her radio had gone silent an hour ago, which she had not yet decided to find suspicious. She pushed harder. The chase bent north, through Euston's dead-eyed streets and then up into Camden, where the night crowds had thinned to a few stragglers under dripping awnings. Tomás glanced over his shoulder once, and she saw his face clearly in the sodium light: not panic, but a grim calculation, as though he were measuring the distance between them and finding it acceptable. That was what unsettled her most. He turned off the main road onto a service lane that dead-ended at a chain-link fence and a hoarding plastered with faded posters for a concert no one remembered. Quinn slowed, her hand going to her hip. The fence sagged where someone had peeled back a section. Beyond it, the hoarding gave onto a stairwell descending into darkness, its iron railings furred with rust. An old Tube station entrance. The lettering above the arch had been painted over, but the tiles still showed through in patches, a pale mosaic beneath the grime. She stopped at the edge of the stairwell and listened. Water dripped somewhere below. Then, faintly, the sound of a door opening, and a murmur of voices that rose and fell like a crowd on the other side of a wall. Her partner had walked into a place like this three years ago. DS Morris had followed a lead into a basement in Walthamstow, radioed once that he was going in, and never radioed again. The reports had been written in careful, bloodless language. Missing. Presumed dead. The forensic team had found nothing at the scene except a scatter of small, yellowed objects that the lab could not identify and then, abruptly, stopped asking about. Quinn had kept one of them. It sat in an evidence bag in her desk drawer, and she had never once told anyone why. Her phone showed no signal. She tried the emergency line anyway, and the screen did not even flicker. Tomás had not come down here to escape her. He had come down here because he was going home, and she was the only one who did not already belong. She looked at the stairwell, then at the rain falling in silver threads through the lamplight, then at her own reflection in the dark glass of a broken ticket booth. A woman with a sharp jaw and cropped grey hair stared back, rain on her face, and for the first time in years she could not tell whether she was more afraid of going down or of going back. Quinn tightened her grip on her torch, took one breath, and started down the stairs.
created_at"2026-10-09T08:58:34.694Z" 10/9/2026, 8:58:34 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency12619
moderation_latency(null)
generation_time12619
tokens_prompt888
tokens_completion1252
native_tokens_prompt1266
native_tokens_completion1778
native_tokens_completion_images(null)
native_tokens_reasoning439
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"
usage0.0010156
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791536314-bJsX3pV4llKeTMfSYloU"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791536314-3D5QQM5VNGfBgqGIT2g3"
upstream_id"msg_011CfrPRXjjCPDBAYv4Udxty"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfrPRXjjCPDBAYv4Udxty"
is_byokfalse
latency919
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0010156
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
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags0
93.22% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount738
totalAiIsmAdverbs1
found
0
adverb"sharply"
count1
highlights
0"sharply"
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)
72.90% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount738
totalAiIsms4
found
0
word"flicker"
count2
1
word"unsettled"
count1
2
word"mosaic"
count1
highlights
0"flicker"
1"unsettled"
2"mosaic"
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
narrationSentences43
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences43
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences44
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen41
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords738
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions0
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions25
wordCount735
uniqueNames17
maxNameDensity0.82
worstName"Quinn"
maxWindowNameDensity1
worstWindowName"Quinn"
discoveredNames
Raven1
Nest1
Quinn6
Tomás4
Herrera1
Saint1
Christopher1
Bateman1
Street1
Tottenham1
Court1
Road1
Euston1
Camden1
Tube1
Morris1
Walthamstow1
persons
0"Raven"
1"Nest"
2"Quinn"
3"Tomás"
4"Herrera"
5"Saint"
6"Christopher"
7"Morris"
places
0"Bateman"
1"Street"
2"Tottenham"
3"Court"
4"Road"
5"Euston"
6"Camden"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences35
glossingSentenceCount0
matches(empty)
64.50% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords1.355
wordCount738
matches
0"not panic, but a grim calculation, as though he were measuring the distance"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences44
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs16
mean46.13
std27.93
cv0.606
sampleLengths
059
157
23
371
486
529
664
76
864
928
1041
1198
1218
1330
1469
1515
88.94% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences43
matches
0"been painted"
1"been written"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount4
totalVerbs110
matches
0"was already running"
1"were measuring"
2"was going"
3"was going"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences44
ratio0
matches(empty)
94.69% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount738
adjectiveStacks0
stackExamples(empty)
adverbCount34
adverbRatio0.04607046070460705
lyAdverbCount7
lyAdverbRatio0.009485094850948509
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences44
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences44
mean16.77
std11.9
cv0.71
sampleLengths
019
140
241
35
411
53
67
719
86
939
105
1129
1213
1339
1412
1517
163
1726
1835
196
2029
218
2210
2317
245
2523
2610
274
2827
2912
3022
319
321
332
3428
356
3618
375
3813
399
4021
4130
4239
4315
81.06% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.5
totalSentences44
uniqueOpeners22
81.30% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences41
matches
0"Then, faintly, the sound of"
ratio0.024
63.90% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount16
totalSentences41
matches
0"She knew him from the"
1"He did not look back."
2"He turned into Bateman Street"
3"Her breath came hard and"
4"He vaulted a low railing,"
5"Her watch, the worn leather"
6"Her radio had gone silent"
7"She pushed harder."
8"He turned off the main"
9"She stopped at the edge"
10"Her partner had walked into"
11"It sat in an evidence"
12"Her phone showed no signal."
13"She tried the emergency line"
14"He had come down here"
15"She looked at the stairwell,"
ratio0.39
8.78% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount37
totalSentences41
matches
0"The green neon above the"
1"Harlow Quinn had her warrant"
2"She knew him from the"
3"He did not look back."
4"People who looked back were"
5"He turned into Bateman Street"
6"Quinn cursed under her breath"
7"Her breath came hard and"
8"Soho fell away behind them."
9"Tomás ran like a man"
10"He vaulted a low railing,"
11"Her watch, the worn leather"
12"Her radio had gone silent"
13"She pushed harder."
14"The chase bent north, through"
15"Tomás glanced over his shoulder"
16"That was what unsettled her"
17"He turned off the main"
18"Quinn slowed, her hand going"
19"The fence sagged where someone"
ratio0.902
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences41
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences28
technicalSentenceCount1
matches
0"Tomás glanced over his shoulder once, and she saw his face clearly in the sodium light: not panic, but a grim calculation, as though he were measuring the dista…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
84.9766%