Run: 2026-10-09-01M4G04QFMZ1E3E5W3YPB7CBCC

Run Details

Duration:
13.6s
Cost:
$0.001092
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 down all evening, hard and mean, and by the time Harlow Quinn reached Camden High Street it had soaked through her coat and found the gap at her collar. Water ran off the cropped line of her hair and into her eyes. She blinked it away and kept her gaze on the figure ahead. Tomás Herrera did not run like a man who was afraid of being caught. He ran like a man who had done this before, weaving between umbrellas and parked delivery bikes, cutting left at the bus shelter without breaking stride. Under the sodium lamps she caught the flash of the Saint Christopher medallion swinging at his throat, and the pale ridge of the old knife scar on his left forearm as he shoved a pedestrian aside. "Police! Stop where you are!" He didn't stop. Nobody ever did, not on the first shout. She'd learned that over eighteen years, and she'd learned it again three years ago, on a wet night not unlike this one, when she had been the one running and DS Morris had been the one shouting, and then there had been nothing at all but a silence that no inquest had been able to explain. She checked her watch. The worn leather strap was damp under her fingers. Twenty past eleven. The phone in her pocket had gone dead an hour ago, and the radio had given her nothing but static since the Nest. Herrera vaulted a low railing and dropped onto the slick steps of a shuttered market hall. Quinn followed, feet skidding, catching herself on the cold iron. Her knee protested. She ignored it. The steps led down, not up. Beneath the market hall, between two boarded-over shop fronts, a gap had been cut into the pavement. A set of iron stairs descended into darkness, and at the bottom, faint and strange, glowed a light the color of bruised plums. She reached the edge and looked down. Herrera was already at the bottom, pausing only long enough to glance back at her. He wasn't smiling. He looked tired, and something close to pity crossed his face before he turned and disappeared around a corner. Quinn stood at the top of the stairs with the rain hammering her shoulders and did the arithmetic she had been trained to do. Backup was forty minutes out, if they answered at all. The last three officers who had been sent into the tunnels under Camden had been found in the canal, or hadn't been found at all. Her own notes on the Nest, on the clique, on the people who drank there and never seemed to age, were a mess of coincidences that only made sense if you didn't look at them squarely. She thought of Morris's hand on her arm that last night. *Quinn, wait.* She hadn't waited. She went down the stairs. The air changed within three steps. It was warmer, and it smelled of wet stone, incense, and something sweeter underneath, like burnt sugar and copper. The iron treads rang under her boots. At the bottom the tunnel opened into a platform that had once been a Tube station and was now something else entirely. Tiled walls had been hung with tarpaulins and strings of glass lanterns. Stalls of scarred wood sagged under objects she couldn't quite name: a jar of something that moved when she looked at it, a rack of knives with handles carved from what might have been antler or bone, a woman selling folded paper that whispered when the air touched it. Nobody looked at her, which was worse than if they had. Every head had turned, very slightly, in the direction of the stairs, as if she were a bell that had been rung. She saw Herrera at the far end, near the mouth of a tunnel, speaking to a broad-shouldered man in a long coat. The man had a bone token hanging on a cord around his neck. Herrera glanced at Quinn, then at the man, and shook his head once, minutely. *Don't.* The broad man smiled at her across the platform. Then he lifted one hand and made a small, deliberate gesture, a closing of fingers, and the lanterns along the wall guttered in unison. Quinn's hand went to her holster out of habit. Her fingers found nothing but damp wool. Her service weapon was in a locker at the station, where regulation said it should be when she was off duty, and she had been off duty for nine hours before she'd stopped being off duty at all. She could turn around. Climb the stairs, call it in, let the Met send the people who were paid to walk into things like this. Morris would have told her to do exactly that. But Morris was not here, and the thing that had taken him had not been waiting for a warrant. Whatever was happening down here had already reached up into the city and taken a man she'd trusted with her life. If she walked away now, she would spend the rest of her career wondering what had been on the other side of this platform. Quinn wiped the rain from her eyes, straightened her shoulders, and started walking toward the tunnel.
created_at"2026-10-09T09:33:08.476Z" 10/9/2026, 9:33:08 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2917
moderation_latency(null)
generation_time13615
tokens_prompt888
tokens_completion1417
native_tokens_prompt1266
native_tokens_completion1931
native_tokens_completion_images(null)
native_tokens_reasoning365
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"
usage0.0010921
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791538388-DMwOvoBLqKElGN1Wtr86"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791538388-CcSrPxLF1Jm8qExAgzlH"
upstream_id"msg_011CfrS4QPULhMB1jJxtwZyJ"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfrS4QPULhMB1jJxtwZyJ"
is_byokfalse
latency872
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0010921
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
totalTags0
88.66% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount882
totalAiIsmAdverbs2
found
0
adverb"very"
count1
1
adverb"slightly"
count1
highlights
0"very"
1"slightly"
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)
94.33% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount882
totalAiIsms1
found
0
word"silence"
count1
highlights
0"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
emotionTells1
narrationSentences55
matches
0"was afraid"
90.91% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount1
narrationSentences55
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)
analyzedSentences56
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen56
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans2
markdownWords3
totalWords882
ratio0.003
matches
0"Quinn, wait."
1"Don't."
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions3
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions27
wordCount877
uniqueNames12
maxNameDensity0.8
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
Harlow1
Quinn7
Camden2
High1
Street1
Herrera5
Saint1
Christopher1
Morris4
Nest2
Tube1
Met1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Saint"
4"Christopher"
5"Morris"
6"Met"
places
0"Camden"
1"High"
2"Street"
globalScore1
windowScore1
38.89% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences45
glossingSentenceCount2
matches
0"something close to pity crossed his face before"
1"quite name: a jar of something that moved when she looked at it, a rack of knives with handles carved from what might have been antler or bone, a woman selling folded paper that whispered when the air touched it"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount882
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences56
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs20
mean44.1
std29.06
cv0.659
sampleLengths
058
176
25
367
439
532
646
744
895
916
105
11115
1233
1349
141
1533
1654
1734
1864
1916
66.99% Passive voice overuse
Target: ≤2% passive sentences
passiveCount6
totalSentences55
matches
0"being caught"
1"been trained"
2"been sent"
3"been found"
4"been found"
5"been hung"
6"been rung"
7"were paid"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs148
matches
0"was happening"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences56
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount879
adjectiveStacks0
stackExamples(empty)
adverbCount27
adverbRatio0.030716723549488054
lyAdverbCount7
lyAdverbRatio0.007963594994311717
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences56
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences56
mean15.75
std11.59
cv0.736
sampleLengths
033
113
212
314
426
536
65
73
88
956
104
119
123
1323
1416
1510
163
173
186
1917
2023
217
2215
233
2419
2524
2610
2725
2836
2911
305
315
326
3319
347
3522
3612
3749
3811
3922
4022
4113
4214
431
449
4524
469
477
4838
494
75.00% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.48214285714285715
totalSentences56
uniqueOpeners27
61.73% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences54
matches
0"Then he lifted one hand"
ratio0.019
86.67% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount18
totalSentences54
matches
0"She blinked it away and"
1"He ran like a man"
2"He didn't stop."
3"She'd learned that over eighteen"
4"She checked her watch."
5"Her knee protested."
6"She ignored it."
7"She reached the edge and"
8"He wasn't smiling."
9"He looked tired, and something"
10"Her own notes on the"
11"She thought of Morris's hand"
12"She went down the stairs."
13"It was warmer, and it"
14"She saw Herrera at the"
15"Her fingers found nothing but"
16"Her service weapon was in"
17"She could turn around."
ratio0.333
43.33% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount45
totalSentences54
matches
0"The rain had come down"
1"Water ran off the cropped"
2"She blinked it away and"
3"Tomás Herrera did not run"
4"He ran like a man"
5"He didn't stop."
6"Nobody ever did, not on"
7"She'd learned that over eighteen"
8"She checked her watch."
9"The worn leather strap was"
10"The phone in her pocket"
11"Herrera vaulted a low railing"
12"Quinn followed, feet skidding, catching"
13"Her knee protested."
14"She ignored it."
15"The steps led down, not"
16"A set of iron stairs"
17"She reached the edge and"
18"Herrera was already at the"
19"He wasn't smiling."
ratio0.833
92.59% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences54
matches
0"If she walked away now,"
ratio0.019
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences35
technicalSentenceCount2
matches
0"He ran like a man who had done this before, weaving between umbrellas and parked delivery bikes, cutting left at the bus shelter without breaking stride."
1"Stalls of scarred wood sagged under objects she couldn't quite name: a jar of something that moved when she looked at it, a rack of knives with handles carved f…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags0
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
91.3033%