Run: 2026-10-08-01M4D9WV06NKRGQ4RKJHD82V08

Run Details

Duration:
12.3s
Cost:
$0.000989
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 been falling since eight, and by midnight it had soaked through Quinn's coat and found the gap at her collar. She didn't care. The man ahead of her was running, and every stride he took across the wet pavement of Chalk Farm Road told her he knew the city better than she did. Tomás Herrera. Twenty-nine years old, a paramedic until the General Medical Council had taken his license, and now, according to two informants who had not survived the telling, the clique's quiet physician. Stitches in the dark. Wounds that should have killed people and didn't. She had watched him leave the Raven's Nest twenty minutes ago through the kitchen door, not the front, and she had followed him out without calling it in. She knew that was a mistake. She had made a decision anyway. He cut left under the railway arch, and his shoulders dropped as if he had reached a place he trusted. Quinn's left wrist ached where the worn leather watch sat against her pulse. Its hands read 00:14. Three years ago, almost to the hour, she had stood in a stairwell in Hackney with Dan Morris's blood on her gloves and a sound behind the door that no pathologist had been able to explain. Her partner had walked in and never walked out. The file said he had died of a cardiac event. She had never believed a word of it. She came under the arch at a run, her heels striking the wet brick. The space was empty. Ahead, a green neon sign buzzed over a stairwell that should not have been open at this hour. The lettering had been painted over, but the light bled through in patches, and she recognised the shape of a bird. Herrera was already halfway down. Quinn reached the top of the steps and stopped. The stairs went down into darkness that smelled of wet stone, old iron, and something sweeter underneath, like burning herbs. Tiled walls, once white, were furred with grime. A faded roundel hung above the arch: the Tube sign, its bar and circle, stripped of the station name. Below it, someone had nailed a small carved sign to the wall. A skull, with a bone-white token dangling from a loop of wire beneath it. She drew her warrant card, held it up, and found her voice was steady. "Metropolitan Police. Stop where you are." Her echo came back to her, then a second voice, lower, wry, from somewhere below. "You're not in your jurisdiction, Detective." Herrera's voice. He had stopped on the landing. She could just make out the pale line of his shirt and the silver catch of the medallion at his throat. "Come up here," she said. "Slowly." "I can't. You don't understand what's down there." A pause. His breath was ragged, the breath of a man who had run too far. "Neither did Morris." The name went through her like cold water. She took one step down before she could stop herself, and the old wound in her chest pulled tight. "What did you say?" "He came down here three years ago, looking for the same thing you're looking for. He didn't have a token either." Herrera's shadow moved on the wall. "I'm sorry. I thought you should know before you decide." Quinn stood very still. Every training instinct she had built over eighteen years pushed at her. Call for backup. Wait at the top. Get a warrant. Get a team. Establish the perimeter. Procedure was the only thing that had ever kept her from becoming the kind of detective who ended up in a stairwell with blood on her gloves. But procedure had not saved Morris. Procedure had written the report that buried him. She looked at the bone token hanging on its wire. Then at the dark below. Then at her watch. 00:21. Every minute she waited, the market moved on or closed its doors, and whatever had taken Dan Morris would be waiting with its patience intact. "Herrera," she said. "Where is the entrance?" A long silence. Then his voice, quieter. "There isn't one for you. Not without a token." "Then get me one." "That's not how it works." "Then tell me how it does work, or I come down there blind, and you can explain to a coroner why a Metropolitan detective is missing in your basement." She heard him exhale, a sound almost like a laugh. "You're either very brave or very stupid." "Eighteen years in the job," Quinn said. "I've stopped being able to tell the difference." Somewhere far below, a train she could not hear rattled through a tunnel that had been closed since before she was born. The air shifted, warm against her wet face, and the carved skull above the arch seemed to watch her. She took the first step down.
created_at"2026-10-08T08:25:52.399Z" 10/8/2026, 8:25:52 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2402
moderation_latency(null)
generation_time12259
tokens_prompt888
tokens_completion1358
native_tokens_prompt1266
native_tokens_completion1724
native_tokens_completion_images(null)
native_tokens_reasoning246
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"
usage0.0009886
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791447952-6pTIzhkCKp7c3ACXP4Bd"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791447952-UHzrMkAJ46Kb4DIkATNO"
upstream_id"msg_011CfpT7xvzsnVsVwnCgVnku"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpT7xvzsnVsVwnCgVnku"
is_byokfalse
latency693
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0009886
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
totalTags4
adverbTagCount0
adverbTags(empty)
dialogueSentences18
tagDensity0.222
leniency0.444
rawRatio0
effectiveRatio0
75.40% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount813
totalAiIsmAdverbs4
found
0
adverb"slowly"
count1
1
adverb"very"
count3
highlights
0"slowly"
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)
81.55% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount813
totalAiIsms3
found
0
word"pulse"
count1
1
word"echo"
count1
2
word"silence"
count1
highlights
0"pulse"
1"echo"
2"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
emotionTells0
narrationSentences62
matches(empty)
73.73% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount1
narrationSentences62
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)
analyzedSentences75
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen36
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords813
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions8
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions24
wordCount679
uniqueNames14
maxNameDensity0.74
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Herrera"
discoveredNames
Quinn5
Chalk1
Farm1
Road1
Herrera4
General1
Medical1
Council1
Raven1
Nest1
Hackney1
Dan2
Morris3
Tube1
persons
0"Quinn"
1"Herrera"
2"Council"
3"Raven"
4"Dan"
5"Morris"
places
0"Chalk"
1"Farm"
2"Road"
3"Hackney"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences40
glossingSentenceCount0
matches(empty)
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount813
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences75
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs28
mean29.04
std25.6
cv0.882
sampleLengths
056
172
212
3100
457
55
682
720
815
96
1029
116
1227
1327
144
1537
1659
1714
1845
197
2016
214
225
2329
2417
2515
2641
276
82.63% Passive voice overuse
Target: ≤2% passive sentences
passiveCount4
totalSentences62
matches
0"been open"
1"been painted"
2"were furred"
3"been closed"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs114
matches
0"was running"
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences75
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount681
adjectiveStacks0
stackExamples(empty)
adverbCount18
adverbRatio0.02643171806167401
lyAdverbCount1
lyAdverbRatio0.0014684287812041115
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences75
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences75
mean10.84
std8.47
cv0.781
sampleLengths
023
13
230
32
430
54
68
728
86
96
1020
1113
124
1336
149
1510
168
1714
184
1918
2021
215
229
2320
248
2519
2612
2714
2814
296
3015
316
322
336
3421
355
361
3710
3814
393
408
4119
424
4327
4410
454
4612
473
484
493
76.58% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats5
diversityRatio0.5
totalSentences74
uniqueOpeners37
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount4
totalSentences55
matches
0"Then at the dark below."
1"Then at her watch."
2"Then his voice, quieter."
3"Somewhere far below, a train"
ratio0.073
89.09% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount18
totalSentences55
matches
0"She didn't care."
1"She had watched him leave"
2"She knew that was a"
3"She had made a decision"
4"He cut left under the"
5"Its hands read 00:14."
6"Her partner had walked in"
7"She had never believed a"
8"She came under the arch"
9"She drew her warrant card,"
10"Her echo came back to"
11"He had stopped on the"
12"She could just make out"
13"His breath was ragged, the"
14"She took one step down"
15"She looked at the bone"
16"She heard him exhale, a"
17"She took the first step"
ratio0.327
96.36% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount40
totalSentences55
matches
0"The rain had been falling"
1"She didn't care."
2"The man ahead of her"
3"Stitches in the dark."
4"Wounds that should have killed"
5"She had watched him leave"
6"She knew that was a"
7"She had made a decision"
8"He cut left under the"
9"Quinn's left wrist ached where"
10"Its hands read 00:14."
11"Her partner had walked in"
12"The file said he had"
13"She had never believed a"
14"She came under the arch"
15"The space was empty."
16"The lettering had been painted"
17"Herrera was already halfway down."
18"Quinn reached the top of"
19"The stairs went down into"
ratio0.727
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences55
matches(empty)
ratio0
37.04% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences27
technicalSentenceCount4
matches
0"Twenty-nine years old, a paramedic until the General Medical Council had taken his license, and now, according to two informants who had not survived the tellin…"
1"Ahead, a green neon sign buzzed over a stairwell that should not have been open at this hour."
2"The stairs went down into darkness that smelled of wet stone, old iron, and something sweeter underneath, like burning herbs."
3"Procedure was the only thing that had ever kept her from becoming the kind of detective who ended up in a stairwell with blood on her gloves."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags4
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags3
fancyCount0
fancyTags(empty)
dialogueSentences18
tagDensity0.167
leniency0.333
rawRatio0
effectiveRatio0
90.4125%