Run: 2026-10-08-01M4DAHFZZW7HH2455WG8YVANC

Run Details

Duration:
13.2s
Cost:
$0.001070
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 found the gap between her collar and her neck, and Harlow Quinn felt each cold bead track down her spine as she ran. Ahead, through the sheets of water slanting under the sodium lamps, Tomás Herrera moved like a man who knew exactly where his feet would land. He didn't look back. He didn't need to. He had been running from her for eleven minutes, and she had yet to close the gap by more than a few yards. They had left Soho behind at Shaftesbury Avenue. The Raven's Nest, its green neon sign bleeding across the wet pavement, had been the last place she could call familiar. Since then the streets had narrowed and bent, the Georgian fronts giving way to shuttered shopfronts, then to the blank backs of warehouses. Somewhere north of Euston Road the city had stopped being London in any way she recognised. Her left wrist ached. She ignored it, the way she ignored everything that wasn't the man in front of her. The leather strap of her watch was soaked through and clung to her skin. Eighteen years on the job had taught her to run without wasting breath, to keep her eyes on the target and her mind on the next junction. Morris had taught her that too, once, on a towpath in Hackney, laughing as she gasped at him to slow down. Three years since the towpath. Three years since the night Morris walked into a basement in Bethnal Green and walked out again, except that he hadn't walked out at all. The coroner's report said *unexplained*. The forensic team had said nothing, because there had been nothing to say. Quinn had said plenty, most of it to people who stopped listening the moment she mentioned the word *wrong*. Tomás Herrera's name had come up in the margins of that file, in a handwritten note she was fairly sure no one else had read. He cut left, hard, through a gap between two skips. Quinn followed, her shoulder scraping brick, and nearly lost her footing on the greasy cobbles. She caught herself on a railing and saw him vault a low wall and drop onto a rail cutting. Something about the way he landed made her stop. There was no sound from the far side. No traffic, no sirens, no distant hum of the city. The rain seemed to fall more quietly here, as if it, too, had been told to keep its voice down. She climbed the wall and dropped down beside the tracks. The cutting ran into the dark, and at its mouth, where the arch of an old Victorian portal had been bricked up and then broken open again, a light flickered. It was not electric. It was the colour of tallow, low and amber, and it pulsed as though breathing. Camden. The old Tube station, closed since the war. Every officer in the division knew it as a dead site, a place for squatters and rough sleepers and the occasional fire-damaged corpse. Quinn had seen the reports. What none of the reports mentioned was the sound now drifting up from below: voices, a dozen at least, bargaining in low tones. Laughter that did not sound entirely human. The clink of something metal, or glass, or bone. She drew her breath and went to the edge of the platform stairs. Herrera stood halfway down, one hand on the rusted rail, his face lit from below. He was breathing hard, but he was not afraid. His right hand rested against his chest, over the outline of the Saint Christopher medallion beneath his shirt, and the scar on his left forearm showed pale in the glow. "You shouldn't be here, Detective," he said. His accent softened the words. "Not without a token." "Then tell me where I can get one." "You can't. Not tonight." He glanced down into the amber dark, then back at her, and something in his expression shifted from warning to something closer to pity. "The Market moves every full moon. Tonight it's here. Tomorrow it will be somewhere else, and the people who go down those stairs without a bone token don't always come back up them." "Morris went down those stairs," Quinn said. Herrera said nothing. His silence was answer enough. Her radio was dead. She had not bothered to call it in; the signal had cut out somewhere around the Euston Road, and she had not wanted to waste a second on a sergeant who would ask for a grid reference she couldn't give. Her warrant card felt absurdly light in her pocket. Her service pistol sat heavy on her hip, and she understood with cold clarity that it would be worth nothing down there. Every rational instinct she had told her to step back, get to the street, call for backup that would arrive in forty minutes if it arrived at all. Procedure. Distance. Survival. But three years of nights had taught her that procedure had never once brought Morris back, and that the answers never waited on the surface. She looked at Herrera's scarred arm, then at the light pulsing beneath them. Somewhere in that dark was the thing that had taken her partner, and the man who had known its name and written it in a margin. Quinn pulled her collar tight against the rain, set her jaw, and took the first step down.
created_at"2026-10-08T08:37:09.256Z" 10/8/2026, 8:37:09 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2475
moderation_latency(null)
generation_time13175
tokens_prompt888
tokens_completion1461
native_tokens_prompt1266
native_tokens_completion1887
native_tokens_completion_images(null)
native_tokens_reasoning263
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"
usage0.0010701
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791448629-K0vcDsVcIFM6MzQsEfsL"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791448629-4d7PnO55BR7swkAtOtK9"
upstream_id"msg_011CfpTyrWgmx1RhN394pEAX"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpTyrWgmx1RhN394pEAX"
is_byokfalse
latency740
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0010701
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
totalTags3
adverbTagCount0
adverbTags(empty)
dialogueSentences6
tagDensity0.5
leniency1
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount904
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)
66.81% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount904
totalAiIsms6
found
0
word"down her spine"
count1
1
word"familiar"
count1
2
word"flickered"
count1
3
word"electric"
count1
4
word"pulsed"
count1
5
word"silence"
count1
highlights
0"down her spine"
1"familiar"
2"flickered"
3"electric"
4"pulsed"
5"silence"
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
narrationSentences60
matches(empty)
95.24% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences60
filterMatches
0"watch"
hedgeMatches
0"seemed to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences63
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen40
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans2
markdownWords2
totalWords904
ratio0.002
matches
0"unexplained"
1"wrong"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions35
wordCount845
uniqueNames21
maxNameDensity0.71
worstName"Quinn"
maxWindowNameDensity1
worstWindowName"Quinn"
discoveredNames
Harlow1
Quinn6
Tomás2
Herrera5
Soho1
Shaftesbury1
Avenue1
Raven1
Nest1
Georgian1
Euston2
Road2
London1
Hackney1
Morris3
Bethnal1
Green1
Victorian1
Tube1
Saint1
Christopher1
persons
0"Harlow"
1"Quinn"
2"Tomás"
3"Herrera"
4"Raven"
5"Morris"
6"Saint"
7"Christopher"
places
0"Soho"
1"Shaftesbury"
2"Avenue"
3"Euston"
4"Road"
5"London"
6"Bethnal"
globalScore1
windowScore1
91.86% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences43
glossingSentenceCount1
matches
0"as though breathing"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount904
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences63
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs20
mean45.2
std27.79
cv0.615
sampleLengths
082
168
282
392
453
538
659
776
813
954
1016
118
1261
137
148
1575
1631
1725
1839
1917
93.57% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences60
matches
0"been told"
1"been bricked"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs141
matches
0"was breathing"
97.51% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount1
flaggedSentences1
totalSentences63
ratio0.016
matches
0"She had not bothered to call it in; the signal had cut out somewhere around the Euston Road, and she had not wanted to waste a second on a sergeant who would ask for a grid reference she couldn't give."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount846
adjectiveStacks1
stackExamples
0"occasional fire-damaged corpse."
adverbCount27
adverbRatio0.031914893617021274
lyAdverbCount6
lyAdverbRatio0.0070921985815602835
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences63
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences63
mean14.35
std9.52
cv0.663
sampleLengths
026
125
24
34
423
58
621
723
816
94
1016
1114
1227
1321
145
1525
165
1713
1819
1925
2010
2115
2219
239
248
2510
2620
2710
2830
294
3015
311
328
3323
345
3523
367
379
3813
3915
409
4130
427
435
444
458
4628
4733
487
493
69.84% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats6
diversityRatio0.47619047619047616
totalSentences63
uniqueOpeners30
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences54
matches
0"Somewhere north of Euston Road"
1"Somewhere in that dark was"
ratio0.037
57.04% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount22
totalSentences54
matches
0"He didn't look back."
1"He didn't need to."
2"He had been running from"
3"They had left Soho behind"
4"Her left wrist ached."
5"She ignored it, the way"
6"He cut left, hard, through"
7"She caught herself on a"
8"She climbed the wall and"
9"It was not electric."
10"It was the colour of"
11"She drew her breath and"
12"He was breathing hard, but"
13"His right hand rested against"
14"His accent softened the words."
15"He glanced down into the"
16"His silence was answer enough."
17"Her radio was dead."
18"She had not bothered to"
19"Her warrant card felt absurdly"
ratio0.407
61.85% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount43
totalSentences54
matches
0"The rain had found the"
1"He didn't look back."
2"He didn't need to."
3"He had been running from"
4"They had left Soho behind"
5"The Raven's Nest, its green"
6"Her left wrist ached."
7"She ignored it, the way"
8"The leather strap of her"
9"Morris had taught her that"
10"The coroner's report said *unexplained*."
11"The forensic team had said"
12"Quinn had said plenty, most"
13"Tomás Herrera's name had come"
14"He cut left, hard, through"
15"Quinn followed, her shoulder scraping"
16"She caught herself on a"
17"Something about the way he"
18"The rain seemed to fall"
19"She climbed the wall and"
ratio0.796
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences54
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences34
technicalSentenceCount2
matches
0"She ignored it, the way she ignored everything that wasn't the man in front of her."
1"Quinn had said plenty, most of it to people who stopped listening the moment she mentioned the word *wrong*."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags3
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount0
fancyTags(empty)
dialogueSentences6
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
90.9239%