Run: 2026-10-09-01M4FZ1R5QZ1FXBFX76BFB3278

Run Details

Duration:
12.9s
Cost:
$0.000976
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 Greek Street into a black mirror, and every headlight that swept across it broke into long, bleeding streaks. Harlow Quinn ran with her coat open and her hand close to her hip, the leather watch on her left wrist knocking against her pulse. Ahead, a figure in a dark jacket cut left past the green neon of the Raven's Nest and vanished into the mouth of an alley. Tomás Herrera. Former paramedic, struck from the register, currently the only man in London who seemed to know how to keep the Nest's regulars alive after the things they had done to each other. She had watched him for six weeks. She had never once seen him run. She reached the alley and stopped, one shoulder to the brick, and listened. Water drummed on the bins. Somewhere a bottle rolled. Then footsteps, quick and uneven, heading not toward the main road but deeper into the lanes behind Soho, where the buildings leaned together like drunks and the streetlamps gave up altogether. "Tomás," she called, and her voice came back flat off the walls. "Stop. Police." The footsteps quickened. She went after him, boots slapping through standing water. Her breath came steady. Eighteen years had taught her that panic was a cost you paid in mistakes, and she had never intended to pay it. Three years ago she had knelt in a stairwell in Hackney beside DS Morris, her partner, and watched his eyes go to glass while the wound on his chest refused to bleed. No knife had done that. No gun. The coroner had written *unexplained* and closed the file, and she had kept the file open in her own head every day since. The lane ended at a chain-link fence and a rusted gate hanging from one hinge. Beyond it, a flight of stone steps descended into a pit of darkness. A Tube sign, cracked and furred with grime, still read *CAMDEN TOWN* in faded blue letters. Someone had chalked a small white symbol beside the entrance, a bone-shaped mark with a crescent through its center. Tomás stood at the bottom of the steps, half-turned, one hand pressed to his left forearm. Even at this distance she could see the pale ridge of the old knife scar through his sleeve. The Saint Christopher medallion at his throat caught what little light there was. "Go home, Harlow," he said. His accent softened her name into something almost tender. "You don't know what's down here." "Then tell me." "I can't. And you can't come." He glanced at the dark behind him, then back at her, and she saw real fear in his face, which was worse than anything she had expected. "It isn't a place for people who carry badges. It doesn't care about them." She took the first step down. The air changed immediately: colder, sweeter, thick with something like incense and wet iron. Somewhere below, music played, a single violin bowing a note that never resolved. Her hand found her radio and stopped. Who would she call? Her commander, who had already filed her concerns under *grief*? The uniforms who had laughed when she requested surveillance on a bar that kept no records and served no drinks anyone had paid for? "What's the entry requirement?" she asked. "Your little bone token?" Tomás's jaw tightened. He reached into his jacket and drew out a small, yellowed object strung on a cord. Even from the steps she could see it was carved, a fragment of something that had once been part of a human hand. He turned it over in his fingers, then, very deliberately, let it drop to the wet concrete at the bottom of the stairs. "You were never going to get in on a warrant," he said quietly. "And I was never going to let you follow me. So now you have to decide whether you want what's down there more than you want to go home." He stepped back into the dark. The violin swelled, and a warm, golden light bloomed far below, spilling up the stairwell like a held breath released. Quinn stood at the edge of the steps, rain running off the brim of her collar. Three years of answers waited in that light, or three years of graves. Her watch ticked against her wrist, steady as a heartbeat. She had never been a woman who looked away. She bent, scooped the bone token from the wet concrete, and closed her fist around it before she could think better of it. Then she descended into Camden.
created_at"2026-10-09T09:14:02.302Z" 10/9/2026, 9:14:02 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3023
moderation_latency(null)
generation_time12867
tokens_prompt888
tokens_completion1216
native_tokens_prompt1266
native_tokens_completion1699
native_tokens_completion_images(null)
native_tokens_reasoning310
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"
usage0.0009761
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791537242-k0bQ3F10jb4vhwoOv1tO"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791537242-0dwcZOxKK6EKBc6NhePl"
upstream_id"msg_011CfrQbuN8HdZ2GA9UWmVVL"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfrQbuN8HdZ2GA9UWmVVL"
is_byokfalse
latency663
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0009761
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
18.18% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags5
adverbTagCount1
adverbTags
0"he said quietly [quietly]"
dialogueSentences11
tagDensity0.455
leniency0.909
rawRatio0.2
effectiveRatio0.182
86.91% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount764
totalAiIsmAdverbs2
found
0
adverb"very"
count1
1
adverb"deliberately"
count1
highlights
0"very"
1"deliberately"
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)
67.28% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount764
totalAiIsms5
found
0
word"pulse"
count1
1
word"footsteps"
count2
2
word"quickened"
count1
3
word"resolved"
count1
highlights
0"pulse"
1"footsteps"
2"quickened"
3"resolved"
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
narrationSentences51
matches(empty)
30.81% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount2
narrationSentences51
filterMatches
0"watch"
hedgeMatches
0"seemed to"
1"tended to"
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
maxSentenceWordsSeen34
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans3
markdownWords4
totalWords764
ratio0.005
matches
0"unexplained"
1"CAMDEN TOWN"
2"grief"
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions5
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions19
wordCount682
uniqueNames15
maxNameDensity0.44
worstName"Tomás"
maxWindowNameDensity1
worstWindowName"Nest"
discoveredNames
Greek1
Street1
Quinn2
Raven1
Nest2
Herrera1
London1
Soho1
Hackney1
Morris1
Tube1
Saint1
Christopher1
Tomás3
Camden1
persons
0"Quinn"
1"Raven"
2"Nest"
3"Herrera"
4"Morris"
5"Saint"
6"Christopher"
7"Tomás"
places
0"Greek"
1"Street"
2"London"
3"Soho"
4"Hackney"
5"Camden"
globalScore1
windowScore1
84.21% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences38
glossingSentenceCount1
matches
0"something like incense and wet iron"
69.11% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords1.309
wordCount764
matches
0"not toward the main road but deeper into the lanes behind Soho"
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
totalParagraphs20
mean38.2
std24.28
cv0.636
sampleLengths
072
148
253
314
43
597
663
747
820
93
1047
1133
1245
1310
1465
1542
1626
1739
189
1928
98.38% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences51
matches
0"was carved"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs110
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences57
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount685
adjectiveStacks0
stackExamples(empty)
adverbCount22
adverbRatio0.032116788321167884
lyAdverbCount5
lyAdverbRatio0.0072992700729927005
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
mean13.4
std8.82
cv0.658
sampleLengths
022
125
225
32
432
57
67
713
85
94
1031
1112
122
133
149
154
1622
1732
185
192
2023
2115
2213
2316
2419
2516
2618
2713
285
299
306
313
3233
3314
346
3514
3613
377
384
3910
4024
416
424
433
4416
4523
4623
4713
4829
496
76.02% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.5087719298245614
totalSentences57
uniqueOpeners29
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences47
matches
0"Somewhere a bottle rolled."
1"Then footsteps, quick and uneven,"
2"Somewhere below, music played, a"
3"Then she descended into Camden."
ratio0.085
66.81% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount18
totalSentences47
matches
0"She had watched him for"
1"She had never once seen"
2"She reached the alley and"
3"she called, and her voice"
4"She went after him, boots"
5"Her breath came steady."
6"His accent softened her name"
7"He glanced at the dark"
8"She took the first step"
9"Her hand found her radio"
10"Her commander, who had already"
11"He reached into his jacket"
12"He turned it over in"
13"he said quietly"
14"He stepped back into the"
15"Her watch ticked against her"
16"She had never been a"
17"She bent, scooped the bone"
ratio0.383
100.00% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount33
totalSentences47
matches
0"The rain had turned Greek"
1"Harlow Quinn ran with her"
2"She had watched him for"
3"She had never once seen"
4"She reached the alley and"
5"Water drummed on the bins."
6"she called, and her voice"
7"The footsteps quickened."
8"She went after him, boots"
9"Her breath came steady."
10"The coroner had written *unexplained*"
11"The lane ended at a"
12"A Tube sign, cracked and"
13"Someone had chalked a small"
14"Tomás stood at the bottom"
15"The Saint Christopher medallion at"
16"His accent softened her name"
17"He glanced at the dark"
18"She took the first step"
19"The air changed immediately: colder,"
ratio0.702
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences47
matches
0"Even at this distance she"
1"Even from the steps she"
ratio0.043
91.84% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences28
technicalSentenceCount2
matches
0"Former paramedic, struck from the register, currently the only man in London who seemed to know how to keep the Nest's regulars alive after the things they had …"
1"Her commander, who had already filed her concerns under *grief*?"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags5
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount0
fancyTags(empty)
dialogueSentences11
tagDensity0.364
leniency0.727
rawRatio0
effectiveRatio0
89.6518%