Run: 2026-10-08-01M4E1F1ZBRHE6B1MCEGHHYP35

Run Details

Duration:
13.7s
Cost:
$0.001059
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 come in off the Thames at dusk and never really left. It hammered the pavement along Old Compton Street and turned the neon of the Soho bars into smeared color across the wet tarmac. Harlow Quinn kept her eyes on the figure ahead, a dark coat and a head of short curls, moving fast without running. He had seen her at the corner of Greek Street. He had known she was there before she'd made her move, and that was the first thing that had frightened her. The green sign above the Raven's Nest blurred past on her left. She didn't slow. Her shoes slapped water. Her breath came steady and measured, the way her training had taught it, though her left wrist, where the worn leather watch sat, ached with the cold. "Herrera," she called. "Stop, or I will shoot you in the back." He didn't stop. He glanced over his shoulder, and in the orange streetlight she saw the face clearly for the first time in six weeks of watching: warm brown eyes, olive skin gone grey with exhaustion, the pale ridge of an old knife scar running along the forearm he flung out to steady himself on a bollard. The silver medallion at his throat swung free of his collar. Saint Christopher, patron of travelers. She thought that was a joke someone might make about him, if they were the sort who made jokes. He cut left into Shaftesbury Avenue and she followed, lungs burning. Traffic hissed past. A bus lit the rain like a lantern and was gone. Quinn lost him for three seconds at the mouth of a narrow lane off Charing Cross Road, and in those three seconds her pulse did something she did not allow it to do on duty. Then she saw him again, halfway down an alley hemmed in by scaffolding and hoardings, and she set her jaw and went after him. Three years ago, Morris had been ahead of her on a stairwell very much like this one. She remembered the sound of his radio cutting out. She remembered the cold that had come up the stairs with no draught to explain it, and the way the light had bent, and the thing that had said her name in his voice. The coroner had called it exposure and then, when pressed, had stopped calling it anything. The file was closed. She had never closed it. The alley opened onto a bombed-out lot, or what had been one. Hoardings ran around three sides, plastered with peeling posters for a musical that had closed before she'd arrived in London. He was at the far corner, bent over a rusted grille set into the ground. He had pulled it up. Beneath it, a stair descended into darkness and a smell of wet stone and something sweeter underneath, like lilies left too long in water. "Don't," she said. Her torch found his face. "Tomás, I'm not asking again. Step away from the grate." He looked up at her. For a moment he seemed almost relieved. "You don't understand what's down there," he said. His accent was softer than his words. "I'm trying to keep you out of it." "Then come up and tell me." "They'll know you followed me. Once you're through, they won't let you back up the same way." He stepped backward onto the first stair and the dark took him to the waist, then the shoulders. She heard his shoes on the iron rungs, unhurried now, as if he had nothing left to outrun. Quinn stood at the lip of the opening with the rain running off her cropped hair and into her collar. Her radio hissed with nothing. She had not called it in. There was no backup coming, and no one in the Met would believe her if she tried to explain the look on Herrera's face or the scent rising off the stone. Her rational mind said to wait, to call the Serious Crime team, to seal the lot and let the people with proper protocols handle it. But her rational mind had been wrong once already, in a stairwell, and Morris had paid for that. She checked her watch. Nine forty-one. The rain did not care. Quinn knelt, switched her torch to her teeth, and set her hand on the cold iron rung. Then she went down after him, into the smell of lilies and the dark that was waiting for her, and did not let herself look back at the lights of the city she was leaving.
created_at"2026-10-08T15:17:46.616Z" 10/8/2026, 3:17:46 PM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3054
moderation_latency(null)
generation_time13685
tokens_prompt888
tokens_completion1096
native_tokens_prompt1266
native_tokens_completion1865
native_tokens_completion_images(null)
native_tokens_reasoning528
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"
usage0.0010591
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791472666-oDmlDsJv94iBw6RPy4Ex"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791472666-3iWgy5Dd2Xop2y7MmyK5"
upstream_id"msg_011CfpzXkvUFhHywQ3kBPtbZ"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpzXkvUFhHywQ3kBPtbZ"
is_byokfalse
latency909
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0010591
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)
dialogueSentences8
tagDensity0.375
leniency0.75
rawRatio0
effectiveRatio0
86.91% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount764
totalAiIsmAdverbs2
found
0
adverb"really"
count1
1
adverb"very"
count1
highlights
0"really"
1"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)
86.91% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount764
totalAiIsms2
found
0
word"measured"
count1
1
word"pulse"
count1
highlights
0"measured"
1"pulse"
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
narrationSentences50
matches(empty)
28.57% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount3
hedgeCount1
narrationSentences50
filterMatches
0"watch"
1"look"
hedgeMatches
0"tried to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences55
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen54
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords764
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions6
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions26
wordCount706
uniqueNames21
maxNameDensity0.57
worstName"Quinn"
maxWindowNameDensity1
worstWindowName"Street"
discoveredNames
Thames1
Old1
Compton1
Street2
Soho1
Quinn4
Greek1
Raven1
Nest1
Christopher1
Shaftesbury1
Avenue1
Charing1
Cross1
Road1
Morris2
London1
Met1
Herrera1
Serious1
Crime1
persons
0"Quinn"
1"Raven"
2"Nest"
3"Christopher"
4"Morris"
5"Herrera"
places
0"Thames"
1"Old"
2"Compton"
3"Street"
4"Soho"
5"Greek"
6"Shaftesbury"
7"Avenue"
8"Charing"
9"Cross"
10"Road"
11"London"
globalScore1
windowScore1
74.24% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences33
glossingSentenceCount1
matches
0"seemed almost relieved"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount764
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences55
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs17
mean44.94
std32.28
cv0.718
sampleLengths
090
146
212
392
484
584
676
718
812
923
106
1117
1236
1387
1418
1511
1652
98.25% Passive voice overuse
Target: ≤2% passive sentences
passiveCount1
totalSentences50
matches
0"was gone"
88.89% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs120
matches
0"was waiting"
1"was leaving"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences55
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount708
adjectiveStacks0
stackExamples(empty)
adverbCount15
adverbRatio0.0211864406779661
lyAdverbCount2
lyAdverbRatio0.002824858757062147
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences55
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences55
mean13.89
std10.5
cv0.756
sampleLengths
014
123
222
310
421
512
63
74
827
93
109
113
1254
1311
145
1519
1611
173
1811
1935
2024
2117
229
2334
2415
254
265
2712
2820
2915
305
3124
323
335
3410
355
367
378
387
398
406
4117
4218
4318
4420
455
466
4731
4825
4918
67.88% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats6
diversityRatio0.4727272727272727
totalSentences55
uniqueOpeners26
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences46
matches
0"Then she saw him again,"
1"Then she went down after"
ratio0.043
11.30% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount24
totalSentences46
matches
0"It hammered the pavement along"
1"He had seen her at"
2"He had known she was"
3"She didn't slow."
4"Her shoes slapped water."
5"Her breath came steady and"
6"He didn't stop."
7"He glanced over his shoulder,"
8"She thought that was a"
9"He cut left into Shaftesbury"
10"She remembered the sound of"
11"She remembered the cold that"
12"She had never closed it."
13"He was at the far"
14"He had pulled it up."
15"Her torch found his face."
16"He looked up at her."
17"His accent was softer than"
18"He stepped backward onto the"
19"She heard his shoes on"
ratio0.522
36.09% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount39
totalSentences46
matches
0"The rain had come in"
1"It hammered the pavement along"
2"Harlow Quinn kept her eyes"
3"He had seen her at"
4"He had known she was"
5"The green sign above the"
6"She didn't slow."
7"Her shoes slapped water."
8"Her breath came steady and"
9"He didn't stop."
10"He glanced over his shoulder,"
11"The silver medallion at his"
12"Saint Christopher, patron of travelers."
13"She thought that was a"
14"He cut left into Shaftesbury"
15"Traffic hissed past."
16"A bus lit the rain"
17"Quinn lost him for three"
18"She remembered the sound of"
19"She remembered the cold that"
ratio0.848
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences46
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences29
technicalSentenceCount1
matches
0"She heard his shoes on the iron rungs, unhurried now, as if he had nothing left to outrun."
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
totalTags3
fancyCount0
fancyTags(empty)
dialogueSentences8
tagDensity0.375
leniency0.75
rawRatio0
effectiveRatio0
85.9680%