Run: 2026-10-08-01M4DAHKAZS01ER5MFY4ENJZ38

Run Details

Duration:
13.5s
Cost:
$0.001078
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 Road into a black mirror, and every streetlamp shattered across it in long, trembling smears of amber. Harlow Quinn kept her eyes on the figure ahead. Tomás Herrera ran with the loose, economical stride of a man who had spent years sprinting toward people who were dying, and he did not look back. Water sheeted off the hood of his dark jacket. Once, under a bus shelter, the light caught the silver of a medallion at his throat, and then he was gone again into the dark. Her lungs burned. Eighteen years on the job had taught her to pace a pursuit, to save the sprint for the last fifty yards, but Herrera had been ahead of her since the pub on Chalk Farm, and every block he gained was a block she would have to make up on the way back. She pressed harder. The leather watch on her left wrist was slick and cold, and she could feel the seconds ticking against her pulse. "Metropolitan Police," she shouted, though her voice was swallowed by the hiss of tires and the roar of a night bus. "Stop. Now." He cut left, past a shuttered chemist and a graffitied hoarding, and she followed him into a narrow service lane that smelled of wet cardboard and diesel. Halfway down, he stopped so abruptly she nearly collided with him. For one suspended heartbeat, they stood ten feet apart, rain drumming on the corrugated roof above them. Herrera was breathing hard. The scar along his left forearm showed pale through his soaked sleeve. He was not looking at her. He was looking past her shoulder, toward the mouth of the lane, and his face had gone the colour of old paper. "Don't," he said. "Whatever you think you're doing. Don't." "You're under arrest for the assault at the Nest, and for the death of Daniel Pryce." She drew her sidearm and held it low, steady, two-handed as she had been trained. "Hands where I can see them." "Pryce was dead before I touched him." His accent thickened with urgency. "You want the truth, Detective, come with me. You want to get yourself killed, stay where you are." She heard it then, behind her. A soft, deliberate footfall. Then another. She turned her head by inches. A man stood at the lane's entrance, though she had not heard him approach. He wore a long coat that did not move in the wind, and his face was obscured by a pale mask carved from something that looked disturbingly like yellowed bone. He did not raise a weapon. He simply watched, the way a man watches a dog he has already decided not to bother with. Quinn's mind worked fast and clean, the way it always did when the stakes climbed. Three years ago, another dark lane. Another man with no face. Morris had gone down the steps of a dead railway station and had never come back up, and the official report had been written by people who clearly did not believe their own words. She had spent three years trying to find the thread that led back to that night. She was not going to let it slip now. "Who is he?" she asked. Herrera's jaw tightened. "A collector. He works for the Market." "What market?" The masked man tilted his head, almost courteously, and took a single step forward. Herrera flinched and turned, and with his back to her he dropped to one knee beside a rusted grate in the pavement. He pried at it with his fingers, and the grate came up with a shriek of metal that cut through the rain. Cold air rose out of the opening. It carried the smell of wet stone, old incense, and something sweeter underneath, like rotting lilies. Far below, faint and rhythmic, Quinn heard music. Not quite music. A chorus of voices humming a single unbroken note. "There's a station under Camden," Herrera said, looking up at her. His eyes were desperate and clear. "It's been shut since the war. The Market moves every full moon, but it's always there, under the old platforms. Nobody with a badge has ever come out of it. Nobody without a bone token gets in at all." He held up his hand. A small, yellowed disc hung from a cord between his fingers, carved with a spiral. "I have one. You don't." "Then you'll give me the token." "I'll give you a choice." He nodded toward the masked figure, who had stopped advancing and now stood with his head bowed, as though waiting for permission. "He won't follow us down. He never does. But he'll remember your face, and the Market will remember your name. Walk away now, and you walk away alive. Come with me, and you'll be asking questions with no way home." Quinn looked at the grate. At the stairwell swallowed by darkness. At Herrera's steady, frightened eyes, and the scar that had been earned, she suspected, in a place just like this. Morris would have gone down. That was the part that tore at her. He had always been the reckless one, the one who said that the best way to learn what scared people was to walk straight into what frightened them. She holstered her weapon, though her hand did not want to let it go. "Give me the token," she said. "And then you're going to tell me everything you know about my partner." Herrera hesitated, then pressed the bone disc into her palm. It was warm, as if it had been resting against a living body. Below them, the humming rose. Quinn stepped onto the first iron rung and started down.
created_at"2026-10-08T08:37:12.68Z" 10/8/2026, 8:37:12 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2669
moderation_latency(null)
generation_time13486
tokens_prompt888
tokens_completion1422
native_tokens_prompt1266
native_tokens_completion1903
native_tokens_completion_images(null)
native_tokens_reasoning182
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"
usage0.0010781
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791448632-z4nDRECFOY4pHIRwp0NW"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791448632-9hznj4MQoBUEGPr8G6C2"
upstream_id"msg_011CfpTz7BV6JgEk7utrWBBN"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpTz7BV6JgEk7utrWBBN"
is_byokfalse
latency739
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0010781
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
totalTags9
adverbTagCount0
adverbTags(empty)
dialogueSentences19
tagDensity0.474
leniency0.947
rawRatio0
effectiveRatio0
100.00% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount955
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)
73.82% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount955
totalAiIsms5
found
0
word"shattered"
count1
1
word"could feel"
count1
2
word"pulse"
count1
3
word"footfall"
count1
4
word"rhythmic"
count1
highlights
0"shattered"
1"could feel"
2"pulse"
3"footfall"
4"rhythmic"
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
emotionTells1
narrationSentences61
matches
0"d with urgency"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences61
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences71
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen52
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords955
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions7
unquotedAttributions0
matches(empty)
97.92% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions17
wordCount768
uniqueNames6
maxNameDensity1.04
worstName"Herrera"
maxWindowNameDensity2
worstWindowName"Herrera"
discoveredNames
Camden1
Road1
Quinn5
Herrera8
Chalk1
Farm1
persons
0"Quinn"
1"Herrera"
places
0"Camden"
1"Road"
2"Chalk"
3"Farm"
globalScore0.979
windowScore1
94.44% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences45
glossingSentenceCount1
matches
0"as though waiting for permission"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount955
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences71
matches
0"said that the"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs26
mean36.73
std27.68
cv0.754
sampleLengths
092
179
223
355
444
59
637
730
818
968
1085
115
1210
132
1458
1543
1681
176
1867
1931
2041
2114
2219
2323
245
2510
70.75% Passive voice overuse
Target: ≤2% passive sentences
passiveCount6
totalSentences61
matches
0"was gone"
1"was swallowed"
2"been trained"
3"was obscured"
4"been written"
5"been earned"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount4
totalVerbs130
matches
0"were dying"
1"was breathing"
2"was not looking"
3"was looking"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences71
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount769
adjectiveStacks0
stackExamples(empty)
adverbCount24
adverbRatio0.031209362808842653
lyAdverbCount6
lyAdverbRatio0.007802340702210663
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences71
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences71
mean13.45
std10.59
cv0.787
sampleLengths
022
19
227
39
425
53
652
73
821
921
102
1127
1211
1317
144
1512
166
1722
183
196
2031
216
2212
2318
246
254
262
276
2814
2930
306
3118
3215
336
345
3534
3616
379
385
393
407
412
4214
4322
4422
457
4616
478
483
499
84.51% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.5492957746478874
totalSentences71
uniqueOpeners39
58.48% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences57
matches
0"Once, under a bus shelter,"
ratio0.018
58.60% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount23
totalSentences57
matches
0"Her lungs burned."
1"She pressed harder."
2"she shouted, though her voice"
3"He cut left, past a"
4"He was not looking at"
5"He was looking past her"
6"She drew her sidearm and"
7"His accent thickened with urgency"
8"She heard it then, behind"
9"She turned her head by"
10"He wore a long coat"
11"He did not raise a"
12"He simply watched, the way"
13"She had spent three years"
14"She was not going to"
15"He pried at it with"
16"It carried the smell of"
17"His eyes were desperate and"
18"He held up his hand"
19"He nodded toward the masked"
ratio0.404
47.72% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount47
totalSentences57
matches
0"The rain had turned Camden"
1"Harlow Quinn kept her eyes"
2"Tomás Herrera ran with the"
3"Water sheeted off the hood"
4"Her lungs burned."
5"She pressed harder."
6"The leather watch on her"
7"she shouted, though her voice"
8"He cut left, past a"
9"Halfway down, he stopped so"
10"Herrera was breathing hard."
11"The scar along his left"
12"He was not looking at"
13"He was looking past her"
14"She drew her sidearm and"
15"His accent thickened with urgency"
16"She heard it then, behind"
17"A soft, deliberate footfall."
18"She turned her head by"
19"A man stood at the"
ratio0.825
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences57
matches(empty)
ratio0
10.58% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences27
technicalSentenceCount5
matches
0"Tomás Herrera ran with the loose, economical stride of a man who had spent years sprinting toward people who were dying, and he did not look back."
1"He cut left, past a shuttered chemist and a graffitied hoarding, and she followed him into a narrow service lane that smelled of wet cardboard and diesel."
2"He wore a long coat that did not move in the wind, and his face was obscured by a pale mask carved from something that looked disturbingly like yellowed bone."
3"At Herrera's steady, frightened eyes, and the scar that had been earned, she suspected, in a place just like this."
4"It was warm, as if it had been resting against a living body."
69.44% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags9
uselessAdditionCount1
matches
0"she shouted, though her voice was swallowed by the hiss of tires and the roar of a night bus"
97.37% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags5
fancyCount1
fancyTags
0"she shouted (shout)"
dialogueSentences19
tagDensity0.263
leniency0.526
rawRatio0.2
effectiveRatio0.105
82.1210%