Run: 2026-10-09-01M4G1C5AMQWZAGJMXJP5M6CFD

Run Details

Duration:
13.6s
Cost:
$0.001079
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 turned Camden High Street into a river of neon reflections, and Harlow Quinn ran through it without caring how the water soaked through her collar. Ahead, a figure in a dark waxed jacket cut between a cluster of tourists and a shuttered kebab van. Short curly hair, plastered flat now. A quick, economical stride, the stride of a man who had once run toward people instead of away from them. Tomás Herrera. Former paramedic, struck off, currently a ghost who stitched up people the city's records didn't admit existed. She had watched him for six weeks. She had never once gotten close enough to hear him breathe. "Herrera! Police. Stop." He glanced back. Their eyes met across twenty metres of wet pavement, and he didn't stop. He ran harder. Quinn's left wrist ached with the cold, the worn leather strap of her watch slick against her skin. She checked it on instinct, the way she had checked it every night for three years, since the afternoon she'd stood in a flooded warehouse and watched Morris walk into a stretch of dark that should not have existed and never walk out. The watch had stopped that night for nearly an hour. She had never found out why. She had stopped asking. Herrera swerved off the main road into a side lane, past a boarded doorway, and then he was gone. She reached the corner in six strides and slowed, one hand on the brick, breathing hard. The lane was narrow and empty, lit by a single sodium lamp that buzzed and flickered. At the far end, a row of iron railings ran along a stretch of pavement that had no business being there. Beyond the railings sat a Tube entrance, its roundel long since stripped, its glass panels replaced by corrugated steel. Chalked across the steel in fresh white lettering was a symbol she had seen once before, scratched into the inside of a dead man's notebook: a small stylised bone, crossed through with a line. The steel door stood ajar. Warm air breathed out of the gap, smelling of woodsmoke, damp stone, and something sweeter underneath. Something like burnt sugar and copper. She pulled out her radio. The static answered her before the dispatcher did. "Control, Quinn. I'm at Camden Lock, north side, a disused Underground access. Suspect entered. Requesting backup to the grid reference." "Copy, Detective. Units are twelve minutes out. Do you have eyes on him?" She looked at the gap in the door. The darkness beyond it was not the simple dark of a tunnel. It seemed to hold depth, layers of it, like water. "Negative," she said. "Not yet." "Hold your position." She lowered the radio. Twelve minutes. In twelve minutes, Herrera could be three tunnels away, out through some other exit, and the chalk symbol would be painted over before the next shift. Quinn had spent eighteen years learning the difference between procedure and cowardice. Procedure said wait. Procedure kept you alive so you could file the report. But procedure had also sent Morris down a service corridor alone, because the order had come from above and no one above had mentioned what the corridor led to. She thought of his face in the last clear image she had of him, grinning at something she'd said, his coat dripping in the doorway of an evidence store. She pushed the steel door wider and stepped through. The air hit her like a warm hand. Stairs descended into amber light, worn smooth at the centre by a century of feet. At the bottom she could hear voices, a low murmur of commerce, a bell ringing somewhere, the scrape of a stall being unfolded. Her torch picked out tiled walls, faded advertisements for an Edwardian theatre, and a row of bones hung from iron hooks, each one tagged with a paper label written in a dozen different hands. Halfway down, a girl sat on the step with a wooden box on her knees. She looked up at Quinn with the flat, unhurried calm of someone who had seen many people arrive here and most of them leave in a hurry. "You'll need a token, love," the girl said. "Bone token. Nothing else gets you past the turnstile." Quinn's hand moved to her warrant card, then stopped. The card would mean nothing down here. She could already tell. "A man came through here a minute ago," Quinn said. "Curly hair. Wet jacket. Did he pay?" The girl's eyes flicked, very briefly, to the far end of the platform where the crowd thickened and shadows moved between the stalls. "He had a token," she said. "Old one. Carved with a saint on the back." Quinn exhaled slowly. Herrera had a way out, then, and a way in, and both of them ran through a place she had no map for. Above her, faint and distant, a siren began to climb through the rain. She could wait for it. Or she could go down. Quinn took the first step.
created_at"2026-10-09T09:54:40.601Z" 10/9/2026, 9:54:40 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency13605
moderation_latency(null)
generation_time13605
tokens_prompt888
tokens_completion1436
native_tokens_prompt1266
native_tokens_completion1905
native_tokens_completion_images(null)
native_tokens_reasoning305
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"
usage0.0010791
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791539680-oF9q6KlnPXIYIxaZxgqR"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791539680-U5GTH4zicOOLsuEHVvWA"
upstream_id"msg_011CfrTheCacBcty8ZsC7e7p"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfrTheCacBcty8ZsC7e7p"
is_byokfalse
latency828
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0010791
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
totalTags4
adverbTagCount0
adverbTags(empty)
dialogueSentences12
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
88.11% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount841
totalAiIsmAdverbs2
found
0
adverb"very"
count1
1
adverb"slowly"
count1
highlights
0"very"
1"slowly"
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)
88.11% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount841
totalAiIsms2
found
0
word"flickered"
count1
1
word"flicked"
count1
highlights
0"flickered"
1"flicked"
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
narrationSentences59
matches(empty)
70.22% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount2
narrationSentences59
filterMatches
0"watch"
hedgeMatches
0"seemed to"
1"began to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences67
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen43
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords841
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
83.33% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions20
wordCount757
uniqueNames9
maxNameDensity1.06
worstName"Quinn"
maxWindowNameDensity2.5
worstWindowName"Quinn"
discoveredNames
Camden1
High1
Street1
Harlow1
Quinn8
Herrera4
Morris2
Tube1
Edwardian1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Morris"
places
0"Camden"
1"High"
2"Street"
globalScore0.972
windowScore0.833
89.02% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences41
glossingSentenceCount1
matches
0"Something like burnt sugar and copper"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount841
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences67
matches
0"stopped that night"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs29
mean29
std25.57
cv0.882
sampleLengths
073
137
23
319
481
519
6106
727
813
920
1013
1130
125
133
1432
1554
1629
179
1880
1942
2017
2120
2217
2323
2415
2526
2613
2710
285
93.37% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences59
matches
0"was gone"
1"being unfolded"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs123
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences67
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount757
adjectiveStacks0
stackExamples(empty)
adverbCount21
adverbRatio0.02774108322324967
lyAdverbCount5
lyAdverbRatio0.0066050198150594455
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences67
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences67
mean12.55
std9.19
cv0.732
sampleLengths
028
119
26
320
42
517
67
711
83
93
1013
113
1218
1343
1410
156
164
1719
1816
1916
2021
2119
2234
235
2416
256
265
278
2820
2913
308
3112
3210
333
342
353
364
372
3826
3912
403
4110
4229
4329
449
458
4615
4723
4834
4915
81.09% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats4
diversityRatio0.5223880597014925
totalSentences67
uniqueOpeners35
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences54
matches(empty)
ratio0
79.26% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount19
totalSentences54
matches
0"She had watched him for"
1"She had never once gotten"
2"He glanced back."
3"Their eyes met across twenty"
4"He ran harder."
5"She checked it on instinct,"
6"She had never found out"
7"She had stopped asking."
8"She reached the corner in"
9"She pulled out her radio."
10"She looked at the gap"
11"It seemed to hold depth,"
12"She lowered the radio."
13"She thought of his face"
14"She pushed the steel door"
15"Her torch picked out tiled"
16"She looked up at Quinn"
17"She could already tell."
18"She could wait for it."
ratio0.352
71.11% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount42
totalSentences54
matches
0"The rain had turned Camden"
1"A quick, economical stride, the"
2"She had watched him for"
3"She had never once gotten"
4"He glanced back."
5"Their eyes met across twenty"
6"He ran harder."
7"Quinn's left wrist ached with"
8"She checked it on instinct,"
9"The watch had stopped that"
10"She had never found out"
11"She had stopped asking."
12"Herrera swerved off the main"
13"She reached the corner in"
14"The lane was narrow and"
15"The steel door stood ajar."
16"Something like burnt sugar and"
17"She pulled out her radio."
18"The static answered her before"
19"She looked at the gap"
ratio0.778
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences54
matches(empty)
ratio0
27.65% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences31
technicalSentenceCount5
matches
0"Former paramedic, struck off, currently a ghost who stitched up people the city's records didn't admit existed."
1"The lane was narrow and empty, lit by a single sodium lamp that buzzed and flickered."
2"At the far end, a row of iron railings ran along a stretch of pavement that had no business being there."
3"She thought of his face in the last clear image she had of him, grinning at something she'd said, his coat dripping in the doorway of an evidence store."
4"She looked up at Quinn with the flat, unhurried calm of someone who had seen many people arrive here and most of them leave in a hurry."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount0
fancyTags(empty)
dialogueSentences12
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
85.7093%