Run: 2026-10-08-01M4DA3994SBWXRHQE2YCRQ540

Run Details

Duration:
12.7s
Cost:
$0.000987
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 canal an hour earlier and hadn't let up since. It hammered the awnings of Camden High Street and ran in black ribbons down the gutters, and Harlow Quinn ran through it with her coat open and her warrant card cold in her fist. Ahead, the man in the dark jacket cut left past a shuttered kebab shop and vanished into the mouth of an alley. Tomás Herrera. Former paramedic, struck off, currently the most useful pair of hands in a city full of things that bled wrong. She had watched him stitch a man's throat in a Soho back room eleven days ago, and she had watched the man's throat close up under his fingers like a zip. She had not written any of it down. That was the first mistake she had made in three years, and she intended it to be the last. She took the corner hard. Her boots skidded on painted metal. The alley was narrow, walled by corrugated hoarding and the blind backs of warehouses, and it smelled of wet cardboard and diesel. Halfway down, a stack of pallets had been pushed aside. Beyond it, a gap in the hoarding showed a rusted gate hanging from one hinge. Her left wrist was wet where the leather strap of her watch had darkened. She glanced at the dial out of reflex. Twelve minutes past midnight. Eighteen years in the Met had taught her the value of a timestamp, and the value of never letting a suspect set the pace. "Herrera!" Her voice bounced off brick. "Police. Stop where you are." Nothing answered her but the rain and the faint, sweet hum of a generator somewhere behind the wall. She pushed through the gap. Beyond the gate, a set of concrete steps sloped down into a throat of darkness. Someone had painted a sign over the arch, long since flaked to ghost-letters: CAMDEN TOWN, and beneath it, in a newer hand, scratched into the tile with what looked like a nail, a small white symbol she didn't recognise. A bone, or a stylised version of one. Fresh chalk lay in a line on the lowest step, unsmudged, as if someone had laid it there that evening. The tunnel below was lit by a string of bulbs that flickered and steadied, flickered and steadied. She could hear voices now, a murmur of many people, and a burst of laughter that was not quite human in its pitch. Then, closer, the scuff of a shoe on tile. Herrera's shoe. She'd have known that gait anywhere by now. She took the first step down and stopped, because her body stopped her. Three years ago, in a flooded basement off the Old Kent Road, she had gone down a set of stairs after Ray Morris. She had heard him call her name from below, and she had gone down, and when she reached the bottom there had been nothing there but his torch lying on the wet floor, still burning, and a smell like struck matches and something sweeter underneath. They had never found him. The official report said he had absconded under suspicious circumstances. The truth was in her notebook, and the notebook was full of things she had no words for. Her hand went to her hip, to the radio. She thought about calling it in. Who would she call? Her Superintendent, who had already asked her twice whether she was sleeping? The rest of the Met, who would find a chalk symbol and a tunnel full of noise and conclude she had lost her mind? Morris's brother, who still sent her Christmas cards? She thought about the bone token she did not have, and the fact that the chalk on the step was clearly a mark for people who did. Below, Herrera spoke, low and unhurried, to someone she could not see. "She's not supposed to be here," he said. "Not yet." A woman's voice answered, amused. "Then she's either very brave or very stupid. Which one are you banking on, Tommy?" Quinn breathed in. Rain ran off the end of her nose and dropped onto the step. She could go back up the alley and wait for the morning. Herrera would surface eventually, or he wouldn't. The clique would keep doing whatever it did behind its locked doors, and she would keep a list of names that no court in England would accept. Or she could go down into the place that had swallowed her partner and see what it wanted from her. Quinn unbuttoned the second button of her coat, moved her hand away from the radio, and started down the steps, one measured stride at a time, the way she had been trained to walk into rooms where the danger had not yet declared itself.
created_at"2026-10-08T08:29:23.628Z" 10/8/2026, 8:29:23 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency12627
moderation_latency(null)
generation_time12627
tokens_prompt888
tokens_completion1179
native_tokens_prompt1266
native_tokens_completion1721
native_tokens_completion_images(null)
native_tokens_reasoning265
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"
usage0.0009871
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791448163-kZI0IJ6OsDrL2nQ3Q91e"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791448163-6hKRJBQUwmsNtOQv5TQx"
upstream_id"msg_011CfpTPXbT8b5ACoKuw2hHP"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpTPXbT8b5ACoKuw2hHP"
is_byokfalse
latency904
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0009871
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
totalTags2
adverbTagCount0
adverbTags(empty)
dialogueSentences5
tagDensity0.4
leniency0.8
rawRatio0
effectiveRatio0
87.61% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount807
totalAiIsmAdverbs2
found
0
adverb"very"
count2
highlights
0"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)
81.41% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount807
totalAiIsms3
found
0
word"flickered"
count2
1
word"measured"
count1
highlights
0"flickered"
1"measured"
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
narrationSentences52
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences52
filterMatches
0"watch"
hedgeMatches(empty)
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
maxSentenceWordsSeen45
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords807
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions2
unquotedAttributions1
matches
0"Below, Herrera spoke, low and unhurried, to someone she could not see."
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions23
wordCount778
uniqueNames16
maxNameDensity0.51
worstName"Herrera"
maxWindowNameDensity1
worstWindowName"Quinn"
discoveredNames
Camden1
High1
Street1
Harlow1
Quinn3
Herrera4
Soho1
Met2
Old1
Kent1
Road1
Ray1
Morris2
Superintendent1
Christmas1
England1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Ray"
4"Morris"
places
0"Camden"
1"High"
2"Street"
3"Soho"
4"Old"
5"Kent"
6"Road"
7"England"
globalScore1
windowScore1
28.05% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences41
glossingSentenceCount2
matches
0"looked like a nail, a small white symbol"
1"not quite human in its pitch"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount807
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences55
matches
0"known that gait"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs19
mean42.47
std30.33
cv0.714
sampleLengths
050
1102
258
350
411
518
65
782
849
910
10114
1163
1227
1322
1420
1516
1646
1720
1844
85.02% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences52
matches
0"been pushed"
1"was lit"
2"been trained"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs123
matches
0"was sleeping"
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
wordCount779
adjectiveStacks0
stackExamples(empty)
adverbCount17
adverbRatio0.021822849807445442
lyAdverbCount3
lyAdverbRatio0.0038510911424903724
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
mean14.67
std10.33
cv0.704
sampleLengths
016
134
222
32
420
531
68
719
85
96
1022
1110
1215
1314
148
154
1624
176
185
1918
205
2115
2239
238
2420
2517
2623
279
282
298
3013
3123
3245
335
3410
3518
369
376
384
3912
4024
418
4227
4312
448
452
465
4715
483
4913
79.39% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.509090909090909
totalSentences55
uniqueOpeners28
68.03% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences49
matches
0"Then, closer, the scuff of"
ratio0.02
64.90% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount19
totalSentences49
matches
0"It hammered the awnings of"
1"She had watched him stitch"
2"She had not written any"
3"She took the corner hard."
4"Her boots skidded on painted"
5"Her left wrist was wet"
6"She glanced at the dial"
7"Her voice bounced off brick"
8"She pushed through the gap."
9"She could hear voices now,"
10"She'd have known that gait"
11"She took the first step"
12"She had heard him call"
13"They had never found him."
14"Her hand went to her"
15"She thought about calling it"
16"Her Superintendent, who had already"
17"She thought about the bone"
18"She could go back up"
ratio0.388
82.45% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount37
totalSentences49
matches
0"The rain had come in"
1"It hammered the awnings of"
2"She had watched him stitch"
3"She had not written any"
4"That was the first mistake"
5"She took the corner hard."
6"Her boots skidded on painted"
7"The alley was narrow, walled"
8"Halfway down, a stack of"
9"Her left wrist was wet"
10"She glanced at the dial"
11"Her voice bounced off brick"
12"Nothing answered her but the"
13"She pushed through the gap."
14"Someone had painted a sign"
15"A bone, or a stylised"
16"The tunnel below was lit"
17"She could hear voices now,"
18"She'd have known that gait"
19"She took the first step"
ratio0.755
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences49
matches(empty)
ratio0
96.77% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences31
technicalSentenceCount2
matches
0"Former paramedic, struck off, currently the most useful pair of hands in a city full of things that bled wrong."
1"She had heard him call her name from below, and she had gone down, and when she reached the bottom there had been nothing there but his torch lying on the wet f…"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags2
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags1
fancyCount0
fancyTags(empty)
dialogueSentences5
tagDensity0.2
leniency0.4
rawRatio0
effectiveRatio0
89.1211%