Run: 2026-04-24-01KPZV3SHCGGJAV4VEA87P8PJN

Run Details

Duration:
27.1s
Cost:
$0.054020
Provider:
Amazon Bedrock

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
Rain came down in sheets across Camden High Street, silver under the sodium lamps, black where the shadows fell. Quinn ran through it with her coat flapping open and her boots throwing up arcs of oily water, and ahead of her Tomás Herrera ran like a man who had been expecting this moment for a long time. He was fast. She would give him that. A paramedic's body, lean and conditioned, and a paramedic's knack for threading bystanders without breaking stride. He'd slipped past the drunks outside the kebab shop, vaulted a bike chained to a railing, and was now cutting across the traffic on Kentish Town Road with the easy disregard of someone who had done worse things than jaywalk. Quinn's lungs burned. Forty-one was not thirty-one, and the rain had soaked through her collar so that every stride sent a cold trickle down between her shoulder blades. Her left wrist ached where the watch strap had gone soft and heavy with water. She kept her eyes on the back of his jacket — dark canvas, the hood torn at the seam — and forced her breath into a rhythm. In four, out four. Morris had taught her that, years ago, running the towpath at Limehouse before everything went wrong. "Herrera!" she shouted. "Stop — police!" He didn't even glance back. He'd heard her the first three times. A black cab laid on its horn as Tomás cut across its bonnet. Quinn skidded around the back of it, slapped a hand against wet metal, and kept going. Her radio crackled under her lapel, some dispatcher asking her twenty, but she didn't have breath to answer. He was turning. Left, off the main road, down a narrow street she didn't know the name of. Warehouses. Shuttered shopfronts. The kind of street where CCTV went to die. She turned the corner and nearly lost him. He was thirty yards ahead, silhouetted against a streetlamp that guttered and flickered, and then he did something strange. He stopped. He looked up, and around, as if checking the sky. The moon was hidden behind cloud but she knew it was full tonight — she'd noted it that morning, because Morris had been killed on a full moon, and she noted full moons now the way other people noted birthdays. Then Tomás pulled something from his pocket. Small. Pale. He looked once, directly, over his shoulder at her — and she saw his face clearly for the first time, the scar on his forearm briefly visible where his sleeve had ridden up, his Saint Christopher medal caught on the outside of his shirt — and he stepped sideways, into what she had taken to be a brick wall. He was gone. "No," Quinn said aloud, to nobody, to the rain. "No, no, no." She closed the distance at a sprint and slammed her palms against the brick where he had been. It was wet. It was solid. She ran her hand along the seams, feeling for a door, a catch, anything, and her fingers found instead the edge of something that did not quite belong to the wall. A recess. An archway, maybe, that her eyes kept sliding off. She had to look at it sideways, peripherally, the way you looked at a dim star. There were steps going down. Quinn stood at the top of them with rain running into her eyes and tried to make her training tell her what to do. Training said: radio in. Training said: do not pursue a suspect alone into an unknown structure without backup, without a warrant, without a clear idea of ingress and egress. Training said a great many sensible things, and none of them had saved Morris. Three years ago she had stood in a doorway in Whitechapel and she had waited, by the book, for the armed response unit to arrive. Morris had gone in because Morris had heard a child screaming. When Quinn had finally been allowed through the door, there had been no child, and there had been no Morris, only a room that smelled of burnt hair and a chalk mark on the floor that none of the forensic team could photograph properly. The inquest had said accidental. The inquest had said a lot of things. Quinn had not believed any of them. She pressed the button on her radio. "Quinn to control. In pursuit of suspect Herrera, Tomás, foot, off Kentish Town Road — " She looked at the street sign and found that she could not quite read it, the letters somehow wrong. " — vicinity of Camden Lock. Suspect has entered a below-ground structure. I am going in." The radio hissed static back at her. Not the ordinary static of a weak signal. A thicker sound, almost like breath. "Repeat, control. I am going in. Mark my last." Nothing. Then, faintly, very far away, a voice that might have been the dispatcher and might have been something else: "...copy, Quinn, we have your... do not..." and then the hiss swallowed it. She drew her baton. Her service did not issue her a firearm and tonight she regretted it more than usual. She thumbed the safety off the torch clipped to her belt and tested the beam against the wet bricks. The light went in and did not come out the other side, as if the dark at the bottom of the steps were drinking it. Quinn thought of Tomás Herrera's face as he had looked at her over his shoulder. Not afraid. Not triumphant. Something more like pity. That decided her. She stepped down. The rain noise cut off three paces in, as cleanly as a door closing, and she realized she could no longer hear the traffic on Kentish Town Road or the dispatcher's static or her own heart. Only her boots on the stone and, from somewhere below, the distant, unmistakable hum of a crowd — voices, laughter, the ring of coin on metal, a woman singing in a language Quinn did not know. She tightened her grip on the baton, touched the worn leather of the watch at her wrist for luck, and went down into the dark after him.
created_at"2026-04-24T13:33:20.312Z" 4/24/2026, 1:33:20 PM
model"anthropic/claude-4.7-opus-20260416"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency787
moderation_latency(null)
generation_time27048
tokens_prompt914
tokens_completion1459
native_tokens_prompt1269
native_tokens_completion1907
native_tokens_completion_images(null)
native_tokens_reasoning0
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"
usage0.05402
router(null)
provider_responses
0
endpoint_id"0fa65117-00d9-4835-808d-9b68041d9ce4"
id"3385e82f-342c-41ee-8d92-1581a0120fab"
is_byokfalse
latency777
model_permaslug"anthropic/claude-4.7-opus-20260416"
provider_name"Amazon Bedrock"
status200
user_agent"langchainjs-openai/1.0.0 ((node/v24.14.1; linux; x64))"
http_referer(null)
request_id"req-1777037600-IMlN983qNHmBz1cff6qH"
session_id(null)
api_type"completions"
id"gen-1777037600-aKBg2zZLz2ZG4Qir4DMa"
upstream_id"3385e82f-342c-41ee-8d92-1581a0120fab"
total_cost0.05402
cache_discount(null)
upstream_inference_cost0
provider_name"Amazon Bedrock"
response_cache_source_id(null)

Evaluation details

Result Evaluator Details Meta Data
0.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags4
adverbTagCount1
adverbTags
0"Quinn said aloud [aloud]"
dialogueSentences8
tagDensity0.5
leniency1
rawRatio0.25
effectiveRatio0.25
95.12% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1024
totalAiIsmAdverbs1
found
0
adverb"very"
count1
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)
85.35% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1024
totalAiIsms3
found
0
word"flickered"
count1
1
word"structure"
count2
highlights
0"flickered"
1"structure"
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
narrationSentences72
matches(empty)
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount0
hedgeCount1
narrationSentences72
filterMatches(empty)
hedgeMatches
0"tried to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences76
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen57
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1035
ratio0
matches(empty)
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
totalMentions37
wordCount979
uniqueNames15
maxNameDensity0.92
worstName"Quinn"
maxWindowNameDensity2
worstWindowName"Morris"
discoveredNames
Camden1
High1
Street1
Tomás4
Herrera2
Kentish2
Town2
Road2
Limehouse1
Morris6
Saint1
Christopher1
Whitechapel1
Quinn9
Training3
persons
0"Tomás"
1"Herrera"
2"Morris"
3"Saint"
4"Christopher"
5"Quinn"
6"Training"
places
0"Camden"
1"High"
2"Street"
3"Kentish"
4"Town"
5"Road"
6"Limehouse"
7"Whitechapel"
globalScore1
windowScore1
0.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences46
glossingSentenceCount3
matches
0"as if checking the sky"
1"not quite belong to the wall"
2"not quite read it, the letters somehow wrong"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1035
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences76
matches
0"found that she"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs23
mean45
std31.76
cv0.706
sampleLengths
057
164
290
36
412
577
679
768
83
912
1082
115
1267
13100
1458
1521
169
1733
1864
1923
203
2175
2227
90.64% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences72
matches
0"was hidden"
1"been killed"
2"was gone"
3"been allowed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount2
totalVerbs145
matches
0"was turning"
1"were drinking"
0.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount6
semicolonCount0
flaggedSentences4
totalSentences76
ratio0.053
matches
0"She kept her eyes on the back of his jacket — dark canvas, the hood torn at the seam — and forced her breath into a rhythm."
1"The moon was hidden behind cloud but she knew it was full tonight — she'd noted it that morning, because Morris had been killed on a full moon, and she noted full moons now the way other people noted birthdays."
2"He looked once, directly, over his shoulder at her — and she saw his face clearly for the first time, the scar on his forearm briefly visible where his sleeve had ridden up, his Saint Christopher medal caught on the outside of his shirt — and he stepped sideways, into what she had taken to be a brick wall."
3"Only her boots on the stone and, from somewhere below, the distant, unmistakable hum of a crowd — voices, laughter, the ring of coin on metal, a woman singing in a language Quinn did not know."
94.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount975
adjectiveStacks1
stackExamples
0"great many sensible things,"
adverbCount36
adverbRatio0.036923076923076927
lyAdverbCount12
lyAdverbRatio0.012307692307692308
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences76
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences76
mean13.62
std12.52
cv0.919
sampleLengths
019
138
23
35
416
540
63
725
815
927
104
1116
123
133
145
157
1613
1716
1818
193
2015
211
222
239
248
2519
262
2710
2840
297
301
311
3259
333
349
353
3618
373
383
3931
402
419
4216
435
4424
454
4625
4714
4825
4911
57.02% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats10
diversityRatio0.42105263157894735
totalSentences76
uniqueOpeners32
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences62
matches
0"Then Tomás pulled something from"
1"Then, faintly, very far away,"
2"Only her boots on the"
ratio0.048
52.26% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount26
totalSentences62
matches
0"He was fast."
1"She would give him that."
2"He'd slipped past the drunks"
3"Her left wrist ached where"
4"She kept her eyes on"
5"He didn't even glance back."
6"He'd heard her the first"
7"Her radio crackled under her"
8"He was turning."
9"She turned the corner and"
10"He was thirty yards ahead,"
11"He looked up, and around,"
12"He looked once, directly, over"
13"He was gone."
14"She closed the distance at"
15"It was wet."
16"It was solid."
17"She ran her hand along"
18"She had to look at"
19"She pressed the button on"
ratio0.419
40.65% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount52
totalSentences62
matches
0"Rain came down in sheets"
1"Quinn ran through it with"
2"He was fast."
3"She would give him that."
4"A paramedic's body, lean and"
5"He'd slipped past the drunks"
6"Quinn's lungs burned."
7"Her left wrist ached where"
8"She kept her eyes on"
9"Morris had taught her that,"
10"He didn't even glance back."
11"He'd heard her the first"
12"A black cab laid on"
13"Quinn skidded around the back"
14"Her radio crackled under her"
15"He was turning."
16"The kind of street where"
17"She turned the corner and"
18"He was thirty yards ahead,"
19"He looked up, and around,"
ratio0.839
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences62
matches(empty)
ratio0
75.89% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences32
technicalSentenceCount3
matches
0"Quinn ran through it with her coat flapping open and her boots throwing up arcs of oily water, and ahead of her Tomás Herrera ran like a man who had been expect…"
1"He'd slipped past the drunks outside the kebab shop, vaulted a bike chained to a railing, and was now cutting across the traffic on Kentish Town Road with the e…"
2"He was thirty yards ahead, silhouetted against a streetlamp that guttered and flickered, and then he did something strange."
0.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount1
matches
0"She looked, the letters somehow wrong"
25.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount1
fancyTags
0"she shouted (shout)"
dialogueSentences8
tagDensity0.25
leniency0.5
rawRatio0.5
effectiveRatio0.25
73.8642%