Run: 2026-10-08-01M4DAE9K5WEDT241WQSG4GMXX

Run Details

Duration:
14.2s
Cost:
$0.001129
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 dusk, and by eleven it had soaked through the shoulders of Harlow Quinn's coat and found the gap at her collar. She didn't feel it. Her eyes were on the man ahead, a dark shape cutting across Camden High Street, weaving between a bus shelter and a knot of smokers who scattered from his path. Tomás Herrera ran like a man who had once been trained to carry weight through crowds. Even at this distance she could see the set of his shoulders, the rhythm of a body that knew where every obstacle would be before it arrived. Her left wrist ached where the worn leather watch pressed against the bone. She checked it out of habit. Eleven-oh-four. Eighteen years on the job had given her a habit for everything. "Herrera, stop!" Her voice carried, flat and hard, the voice she used in interview rooms. He did not stop. He glanced back once, his face pale under the sodium lamps, and then he was past the bus shelter and turning sharply into Camden Lock Place. She followed, boots striking the wet pavement in a controlled rhythm. Her radio hissed in her coat pocket. She ignored it. Backup was twelve minutes out at best, and twelve minutes was a lifetime when the man you were chasing had just walked out of a surveillance file with two dead informants in his wake. The lane was narrow, lined with shuttered stalls and the dripping canvas of market awnings. Halfway along, Herrera vaulted a low railing and disappeared behind a row of bins. Quinn reached the railing and stopped, breath steaming in front of her. Beyond the bins was a service yard, and beyond that the black mouth of an old ventilation shaft, its iron grille hanging open on one hinge. She drew her weapon and kept it low. Her pulse was steady. It always was, the way Morris used to tease her about, saying she could be shot at and still reach for the sugar. Three years ago Morris had walked into a basement in Hackney with a warrant and a torch, and he had walked out of her life without a body to bury. The coroner's report had used the phrase *unexplained circumstances*. She had read it so many times she could recite it. What the report had not explained was the smell that clung to the basement walls, sweet and metallic, like wet coins in a jar of honey, or the way the security footage had gone white for four minutes and then resumed with nothing in the frame at all. She had spent three years building a case against the people who had been in that basement. Herrera was the first thread that had come loose. She stepped over the railing. The yard was slick with oil and rain. At the shaft she crouched, angled her torch down, and saw the metal rungs descending into a dark that seemed to swallow the light rather than reflect it. Something glinted on the wet concrete beside the grille. She picked it up. It was small, smooth, and pale, the size of a playing die, carved with a spiral of tiny symbols she didn't recognise. It was warm. Not warm from a pocket. Warm the way a stone is warm after lying in the sun all afternoon, though there had been no sun in London for a week. A bone token. She had read about them in a file she had not been meant to see, a file with no case number and a handwritten note in the margin: *Entry to the Market. Do not enter without.* She turned it over in her gloved fingers. Somewhere below, faint and distant, a bell rang once, then fell silent. Her radio crackled. "Quinn, where are you? Sarge says you're not answering." She keyed the mic. "Camden Lock, service yard. Herrera went down a ventilation shaft. I'm following." "Ma'am, wait for—" She let go of the button. The rational choice was obvious. She was a detective, not a field operative. She had no idea what was at the bottom of that shaft, no backup, no reliable map, and a token whose purpose she could only guess. A woman with eighteen years of commendations would wait at the top, call it in, and let the proper people handle it. But the proper people had not been in that basement in Hackney. The proper people had not watched a partner vanish between one heartbeat and the next. And Herrera had not run from her out of fear of a jail cell. He had run the way a man runs toward something he believes will save him, and she had seen the same look on his face that she'd seen on the faces of the dead. She slid the token into her inside pocket, against her ribs, where she could feel its faint warmth through the wet fabric. Then she took hold of the first rung, checked the watch one last time, and started down into the dark.
created_at"2026-10-08T08:35:24.4Z" 10/8/2026, 8:35:24 AM
model"anthropic/claude-haiku-5.5-20261007"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency14134
moderation_latency(null)
generation_time14134
tokens_prompt888
tokens_completion1149
native_tokens_prompt1266
native_tokens_completion2004
native_tokens_completion_images(null)
native_tokens_reasoning513
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"
usage0.0011286
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1791448524-yyMHJmeXCbI1Bw8UPgUW"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1791448524-jbNPvOmPidK6EpmgildH"
upstream_id"msg_011CfpTr8Z7YAhJDdpy4p7M7"
provider_responses
0
endpoint_id"b6cdf493-3a61-441f-a134-e7944fddb980"
id"msg_011CfpTr8Z7YAhJDdpy4p7M7"
is_byokfalse
latency787
model_permaslug"anthropic/claude-haiku-5.5-20261007"
provider_name"Claude Platform on AWS"
status200
total_cost0.0011286
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
totalTags1
adverbTagCount0
adverbTags(empty)
dialogueSentences4
tagDensity0.25
leniency0.5
rawRatio0
effectiveRatio0
94.08% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount845
totalAiIsmAdverbs1
found
0
adverb"sharply"
count1
highlights
0"sharply"
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)
76.33% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount845
totalAiIsms4
found
0
word"weight"
count1
1
word"pulse"
count1
2
word"could feel"
count1
3
word"warmth"
count1
highlights
0"weight"
1"pulse"
2"could feel"
3"warmth"
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
narrationSentences56
matches(empty)
66.33% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount1
hedgeCount2
narrationSentences56
filterMatches
0"watch"
hedgeMatches
0"seemed to"
1"try to"
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
maxSentenceWordsSeen48
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans2
markdownWords10
totalWords845
ratio0.012
matches
0"unexplained circumstances"
1"Entry to the Market. Do not enter without."
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions1
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions19
wordCount819
uniqueNames12
maxNameDensity0.49
worstName"Herrera"
maxWindowNameDensity1
worstWindowName"Camden"
discoveredNames
Harlow1
Quinn2
Camden2
High1
Street1
Herrera4
Lock1
Place1
Morris2
Hackney2
London1
Market1
persons
0"Harlow"
1"Quinn"
2"Herrera"
3"Morris"
places
0"Camden"
1"High"
2"Street"
3"Lock"
4"Place"
5"Hackney"
6"London"
7"Market"
globalScore1
windowScore1
90.48% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences42
glossingSentenceCount1
matches
0"dark that seemed to swallow the light rather than reflect it"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount845
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences59
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs20
mean42.25
std31.13
cv0.737
sampleLengths
061
175
245
355
467
5133
626
741
89
959
1039
1120
1212
1316
143
156
1661
1775
1822
1920
92.73% Passive voice overuse
Target: ≤2% passive sentences
passiveCount2
totalSentences56
matches
0"been trained"
1"been meant"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount1
totalVerbs123
matches
0"were chasing"
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
wordCount821
adjectiveStacks0
stackExamples(empty)
adverbCount20
adverbRatio0.024360535931790498
lyAdverbCount2
lyAdverbRatio0.00243605359317905
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
mean14.32
std10.31
cv0.72
sampleLengths
027
14
230
316
427
513
66
71
812
915
104
1126
1211
137
143
1534
1615
1714
1812
1926
208
214
2223
2330
249
2511
2648
2717
289
295
308
3128
329
334
3422
353
365
3725
383
3932
404
418
4212
433
449
454
4612
473
486
495
74.01% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats3
diversityRatio0.4745762711864407
totalSentences59
uniqueOpeners28
100.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount2
totalSentences55
matches
0"Somewhere below, faint and distant,"
1"Then she took hold of"
ratio0.036
16.36% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount28
totalSentences55
matches
0"She didn't feel it."
1"Her eyes were on the"
2"Her left wrist ached where"
3"She checked it out of"
4"Her voice carried, flat and"
5"He did not stop."
6"He glanced back once, his"
7"She followed, boots striking the"
8"Her radio hissed in her"
9"She ignored it."
10"She drew her weapon and"
11"Her pulse was steady."
12"It always was, the way"
13"She had read it so"
14"She had spent three years"
15"She stepped over the railing."
16"She picked it up."
17"It was small, smooth, and"
18"It was warm."
19"She had read about them"
ratio0.509
78.18% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount42
totalSentences55
matches
0"The rain had been falling"
1"She didn't feel it."
2"Her eyes were on the"
3"Tomás Herrera ran like a"
4"Her left wrist ached where"
5"She checked it out of"
6"Her voice carried, flat and"
7"He did not stop."
8"He glanced back once, his"
9"She followed, boots striking the"
10"Her radio hissed in her"
11"She ignored it."
12"Backup was twelve minutes out"
13"The lane was narrow, lined"
14"Halfway along, Herrera vaulted a"
15"Quinn reached the railing and"
16"She drew her weapon and"
17"Her pulse was steady."
18"It always was, the way"
19"The coroner's report had used"
ratio0.764
90.91% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount1
totalSentences55
matches
0"Even at this distance she"
ratio0.018
96.77% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences31
technicalSentenceCount2
matches
0"Her eyes were on the man ahead, a dark shape cutting across Camden High Street, weaving between a bus shelter and a knot of smokers who scattered from his path."
1"What the report had not explained was the smell that clung to the basement walls, sweet and metallic, like wet coins in a jar of honey, or the way the security …"
100.00% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags1
uselessAdditionCount0
matches(empty)
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags0
92.5396%