Run: 2026-10-09-01M4FYHBY2MK3JWACNSX30Z2T5

Run Details

Duration:
14.7s
Cost:
$0.001137
Provider:
Claude Platform on AWS

Model input

System
You are an expert fiction writer. Always keep the following rules in mind: - Write in past tense and use General English spelling, grammar, and colloquialisms/slang. - Write in active voice - Always follow the "show, don't tell" principle. - Avoid adverbs and cliches and overused/commonly used phrases. Aim for fresh and original descriptions. - Convey events and story through dialogue. - Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. - Skip "he/she said said" dialogue tags and convey people's actions or face expressions through their speech - Avoid mushy dialog and descriptions, have dialogue always continue the action, never stall or add unnecessary fluff. Vary the descriptions to not repeat yourself. - Put dialogue on its own paragraph to separate scene and action. - Reduce indicators of uncertainty like "trying" or "maybe" When writing text: - NEVER conclude the scene on your own, follow the beat instructions very closely. - NEVER end with foreshadowing. - NEVER write further than what I prompt you with. - AVOID imagining possible endings, NEVER deviate from the instructions. - STOP EARLY if the continuation contains what was required in the instructions. You do not need to fill out the full amount of words possible.
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <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> Continue the story using the following instructions: <instructions> 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. </instructions>

Model response

AI
The rain had soaked through Quinn's collar within the first block, and now it ran in cold threads down the back of her neck. Ahead, Tomás Herrera cut between a bus shelter and a shuttered kebab van, his curls plastered flat, one hand pressed to his ribs. He hadn't stopped to look back once. Men who looked back were the ones who slowed down. "Herrera!" Her voice bounced off the wet brickwork of Camden High Street. "You're limping. You won't outrun me on that leg." He didn't answer. He swung left, past the kerb, through a gap in the iron railings that someone had bent apart years ago and nobody had bothered to fix. Quinn followed, her boots skidding on the slick paving, and caught the edge of a rusted spike that tore a line across the back of her hand. She swore and kept moving. The market crowds had thinned to nothing after the late pubs emptied. Neon from the chicken shop and the phone repair kiosk smeared across puddles, shredding into red and green. Somewhere behind her a generator coughed and died. The rain drummed on the lids of the bins and on the tarpaulin over a stack of pallets, and beneath all of it she heard the faint, rhythmic clatter of footsteps going down. She stopped at the mouth of the alley. The sign above the old station entrance had been painted over so many times the letters bulged like scar tissue. Someone had hung a plastic bag over the lamp. Beneath it, the stairs curved down into the dark, slick with rainwater that ran in a steady stream toward the tiles below. Tomás stood on the third step from the bottom, one hand on the wall, breathing hard. He turned around, and in the thin light she saw the scar on his left forearm, a pale ridge running from wrist to elbow. The Saint Christopher medallion swung free of his collar and caught the glow. "You should go home, Detective," he said. His accent softened the vowels, made the words sound almost gentle. "Whatever you think I did, you are standing in the wrong place." "I think you left a man in a doorway on Chalk Farm Road with his chest open and a tourniquet tied on the wrong arm." She stepped onto the first stair. The metal edge gave a little under her weight. "I think you were paid to do it, and I think you'll tell me who paid you." "He was already dying." "Then you should have called it in. Not run." Tomás laughed, short and bitter, and the sound echoed down the tunnel. "Called it in to whom? Your lot would have had me in a cell before the ambulance got through the traffic. Do you know what happens to people like me in your cells?" Quinn pulled her warrant card from her inside pocket and held it up, water beading on the laminate. "Come up here and tell me." He glanced past her, toward the street. Then down, into the tunnel. She watched him weigh it, the way a man weighs a bridge he knows is rotten. "You don't know what's down there," he said. "Then you can tell me that too." He reached into his jacket and drew out a small object between two fingers, holding it up so the light caught it. It was a token, pale and smooth, carved from something that was not quite bone and not quite wood. A thin groove ran around its edge, worn dark by handling. "Only one way in," Tomás said. "This gets you past the door. Nothing gets you back out unless somebody on the other side wants you to leave." Quinn stared at the token. Her left wrist ached where the worn leather watch sat against her skin, and she thought of Morris, of the way his radio had crackled out a single word in a basement three years ago and then gone silent for good. She had searched that basement until her hands bled. The wall had been wrong, somehow. Smelled of salt and something older. She had never written any of it down in a report. The rain hammered the plastic bag above the lamp. A train rumbled somewhere far beneath the city, though she knew this line had been dead for decades. "Toss it to me," she said. "No." "Then I come and take it." Tomás studied her for a long moment, then shook his head slowly, almost with pity. "You want to walk into the Market with a warrant and a bad hand and a dead partner on your conscience, Detective. Be my guest." He turned and went down the last steps, into the dark, and the faint clatter of his boots faded into a deeper sound, a murmur of voices and the slow scrape of something heavy being dragged across stone. Quinn looked at the wet railings behind her, at the lit street and the ordinary world she could still walk away from. Then she looked down the stairs, and she took another step.
created_at"2026-10-09T09:05:05.481Z" 10/9/2026, 9:05:05 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency2647
moderation_latency(null)
generation_time14637
tokens_prompt1104
tokens_completion1381
native_tokens_prompt1576
native_tokens_completion1958
native_tokens_completion_images(null)
native_tokens_reasoning439
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"
usage0.0011366
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791536705-ZwPIWPgGowMiPDPnmadw"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791536705-KcD4KADN0XS0RyUJVFT9"
upstream_id"msg_011CfrPvL64F5ikkKH361Ben"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfrPvL64F5ikkKH361Ben"
is_byokfalse
latency733
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0011366
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
totalTags6
adverbTagCount0
adverbTags(empty)
dialogueSentences18
tagDensity0.333
leniency0.667
rawRatio0
effectiveRatio0
94.11% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount849
totalAiIsmAdverbs1
found
0
adverb"slowly"
count1
highlights
0"slowly"
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)
76.44% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount849
totalAiIsms4
found
0
word"rhythmic"
count1
1
word"footsteps"
count1
2
word"weight"
count1
3
word"echoed"
count1
highlights
0"rhythmic"
1"footsteps"
2"weight"
3"echoed"
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
narrationSentences47
matches
0"t with pity"
100.00% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount0
narrationSentences47
filterMatches
0"watch"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences59
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen41
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords849
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions10
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions17
wordCount654
uniqueNames9
maxNameDensity0.76
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Tomás"
discoveredNames
Quinn5
Tomás5
Herrera1
Camden1
High1
Street1
Saint1
Christopher1
Morris1
persons
0"Quinn"
1"Tomás"
2"Herrera"
3"Saint"
4"Christopher"
5"Morris"
places
0"Camden"
1"High"
2"Street"
globalScore1
windowScore1
85.90% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences39
glossingSentenceCount1
matches
0"not quite bone and not quite wood"
1"not quite wood"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount849
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount1
totalSentences59
matches
0"searched that basement"
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs26
mean32.65
std22.79
cv0.698
sampleLengths
064
121
256
35
471
559
653
730
857
94
109
1145
1224
1328
148
157
1652
1727
1878
1927
206
211
226
2340
2438
2533
90.33% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences47
matches
0"been painted"
1"being dragged"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs102
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences59
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount654
adjectiveStacks0
stackExamples(empty)
adverbCount25
adverbRatio0.0382262996941896
lyAdverbCount1
lyAdverbRatio0.0015290519877675841
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences59
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences59
mean14.39
std9.14
cv0.635
sampleLengths
024
123
27
310
412
59
63
726
827
95
1012
1118
128
1333
148
1520
169
1722
1816
1924
2013
217
2211
2312
2431
259
2617
274
289
2912
3033
3118
326
337
345
3516
368
377
3822
3919
4011
416
4221
435
4441
459
466
476
4811
499
74.58% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats1
diversityRatio0.4576271186440678
totalSentences59
uniqueOpeners27
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount3
totalSentences43
matches
0"Somewhere behind her a generator"
1"Then down, into the tunnel."
2"Then she looked down the"
ratio0.07
61.86% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount17
totalSentences43
matches
0"He hadn't stopped to look"
1"Her voice bounced off the"
2"He didn't answer."
3"He swung left, past the"
4"She swore and kept moving."
5"She stopped at the mouth"
6"He turned around, and in"
7"His accent softened the vowels,"
8"She stepped onto the first"
9"He glanced past her, toward"
10"She watched him weigh it,"
11"He reached into his jacket"
12"It was a token, pale"
13"Her left wrist ached where"
14"She had searched that basement"
15"She had never written any"
16"He turned and went down"
ratio0.395
29.77% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount37
totalSentences43
matches
0"The rain had soaked through"
1"He hadn't stopped to look"
2"Men who looked back were"
3"Her voice bounced off the"
4"He didn't answer."
5"He swung left, past the"
6"Quinn followed, her boots skidding"
7"She swore and kept moving."
8"The market crowds had thinned"
9"Neon from the chicken shop"
10"The rain drummed on the"
11"She stopped at the mouth"
12"The sign above the old"
13"Someone had hung a plastic"
14"Tomás stood on the third"
15"He turned around, and in"
16"The Saint Christopher medallion swung"
17"His accent softened the vowels,"
18"She stepped onto the first"
19"The metal edge gave a"
ratio0.86
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences43
matches(empty)
ratio0
89.95% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences27
technicalSentenceCount2
matches
0"Beneath it, the stairs curved down into the dark, slick with rainwater that ran in a steady stream toward the tiles below."
1"It was a token, pale and smooth, carved from something that was not quite bone and not quite wood."
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags6
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags4
fancyCount0
fancyTags(empty)
dialogueSentences18
tagDensity0.222
leniency0.444
rawRatio0
effectiveRatio0
90.0978%