Run: 2026-09-29-01M3P2FZH4VWNNNXHJZM21WFVK

Run Details

Duration:
31.4s
Cost:
$0.033544
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="4414" name="Eva Kowalski" aliases="Eva, Evie" pronouns="she/her" species="Human" storyRole="Best Friend"> <eyeColor>green</eyeColor> <hair>curly red</hair> <height>5'4"</height> <age>26</age> <glasses>round glasses</glasses> <satchel>worn leather satchel full of books</satchel> <freckles>freckled complexion</freckles> <nervousHabit>tucking hair behind her left ear</nervousHabit> <description>Aurora's childhood friend and an occult researcher.</description> <backstory>Moved to London two years before Aurora and works as a research assistant at the British Museum's restricted archives. She holds a master's degree in Ancient History from Oxford.</backstory> </character> <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> <item id="2004" name="Veil Compass" aliases="the Compass, shadow compass" type="Tool" origin="The Veil Market"> <casingMaterial>small brass compass</casingMaterial> <faceMarkings>face etched with protective sigils</faceMarkings> <needle>points toward the nearest supernatural rift or portal</needle> <patina>casing has a patina of verdigris</patina> <creator>crafted by a Shade artisan</creator> <description>A compass attuned to supernatural energy.</description> </item> </codex> Continue the story using the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the mystery genre. A detective arrives at a crime scene in an unusual location. Something is wrong — the evidence doesn't add up. A colleague is present, offering a different interpretation. Through careful observation and deduction, the detective begins to see what others have missed. 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 lift to the disused platform had been out of service since 1974, so Quinn took the stairs. Two hundred and forty steps, by her count, each one slick with condensation. A uniformed constable met her at the bottom and handed her a torch, and she found she did not need it. Arc lamps on tripods flooded the platform in flat white light. "Detective." Eva Kowalski stood beside the far tripod with her satchel clutched to her ribs, curls escaping from a knot at the back of her head. She tucked a strand behind her left ear. "I got here twenty minutes ago." "I know. The desk sergeant logged you." Quinn ducked under the tape. "You told him you were with the Museum." "I am with the Museum." "You're a research assistant. I asked for someone who could read the markings." "I can read the markings." Eva pushed her round glasses higher. "That's the problem." Quinn crouched at the platform edge. The body lay on its back between two rusted benches, a man in his fifties, grey suit, no coat. His mouth hung open. His lips had gone the blue-white of skimmed milk. Chalk lines, or something like chalk, ringed him in a circle of interlocking curves. "Tell me what you see," Quinn said. "A ritual killing." Eva spoke fast, the words tumbling. "Those are binding sigils, Mesopotamian in structure but corrupted. Someone laid him in the centre to contain something. Or to call it. The wounds—" "There are no wounds." Eva stopped. "Look at him." Quinn tilted her head at the corpse. "No blood. No bruising at the throat. No ligature marks. Pathology will tell me more, but I've seen forty drownings, and this man has the froth at his nostrils." "He's drowned?" Eva laughed, a short cracked sound. "Down here?" "That's what doesn't sit right." Quinn stood, her knees popping. She pressed her thumb against the worn leather strap of her watch, a habit she had never shaken. "The nearest water main is three hundred yards east. The tunnels are dry. The constable who found him says the floor was dry. And his suit—" She pointed. "Bone dry. Not damp at the cuffs, not even creased. A man who drowns in a bath still gets his sleeves wet." "So someone moved him." "Did they?" Quinn walked the perimeter of the circle, boots crunching on grit. "Come here. Tell me about the dust." Eva edged closer. Grey powder lay thick across the platform, a century of brake dust and soot and rot. In front of the benches it had been scraped to bare concrete in long parallel bands. "Something was dragged?" "Something was set down. Over and over." Quinn pointed with her torch. "Those aren't drag marks. Look at the edges. Sharp, square. A rectangle, then another beside it, then a lane between them. Eight, nine, ten of them, running the length of the platform." "Stalls," Eva whispered. Quinn's head turned. "Stalls." "I mean—" Eva's hand flew to her hair. "Like stalls. Market stalls. That's what they look like." "That's what they look like." Quinn let the silence stretch. "Forty years I've been walking crime scenes, or near enough, and I've never seen a scene that looks like a car boot sale. Somebody held a gathering down here. A big one. Hundreds of feet wore that path along the tunnel mouth. See how the grit's ground to powder there? Then everyone packed up and left, and they left him." "Or they were the ones who killed him." "A crowd that size? No. You don't murder a man in front of hundreds, then tidy up so neatly you sweep the dust from your stall footprints." Quinn moved to the body and knelt again. "Whoever cleared this place did it fast, but with care. They took the tables, the lamps, the crates. They didn't take him, and they didn't touch the circle." "Because the circle is the message." "Is it?" Quinn drew a pen from her coat and pointed to the nearest curve of the chalk. "Your Mesopotamian sigils. Tell me which way they run." Eva bent. Her lips moved. "Clockwise. Inward." "And the ones at his head?" A long pause. "Outward." "So the design contradicts itself. Someone drew this in a hurry, or drew it to look like something it wasn't." Quinn kept her voice level. "A ritual meant to bind wouldn't spiral both ways. This is stage dressing, Miss Kowalski. Somebody wanted a detective to see occult nonsense and go chasing cults through Camden, while the real question sat dry and quiet in the dust." "What real question?" "Why a drowned man is lying in a tunnel with no water." Quinn slid two gloved fingers into the dead man's jacket pocket. She withdrew a small, pale disc, yellowed as an old tooth, drilled through the centre with a hole no wider than a pencil lead. She held it to the light. Fine lines had been scratched across its surface. "And why he's carrying this." Eva's face lost its colour beneath the freckles. "Where did he get that?" "You tell me. It's bone. Human, if I'm any judge, though I'll wait for the lab." Quinn turned it over. "The same lines are cut into the tile behind you. Small, low down, near the skirting. Almost like someone marked the entrance." Eva looked. Quinn watched her look. The girl's mouth tightened, and her hand crept to her ear again. "You've seen these before," Quinn said. "In a catalogue. At work." Eva swallowed. "Restricted archive material. It could mean anything." "It could. But you flinched." Quinn slipped the token into an evidence bag and sealed it. "So let's try the other pocket." She reached across the body. Her fingers closed on something cold and heavy, and she drew out a small brass compass, its casing furred with green verdigris. Etched glyphs crowded the face, tiny and precise, and the glass had been polished by years of thumbs. The needle should have pointed north. It didn't. It spun, slowed, lurched, and locked on the blank tiled wall at the platform's end. Quinn rose. She took a step to the left. The needle followed the wall. She took a step to the right. It followed again, steady as a dog on a scent. "There's nothing there," Eva said, too quickly. "There's a wall." Quinn stared at the needle, then at the wall, then at the girl beside her. "Which is precisely what a man would say who'd never checked behind it."
created_at"2026-09-29T07:53:59.084Z" 9/29/2026, 7:53:59 AM
model"anthropic/claude-sonnet-5.5-20260928"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency3609
moderation_latency(null)
generation_time31346
tokens_prompt1112
tokens_completion1871
native_tokens_prompt1597
native_tokens_completion3035
native_tokens_completion_images(null)
native_tokens_reasoning836
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"
usage0.033544
router(null)
user_agent"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer(null)
request_id"req-1790668439-Xw8cmLzKJXfadZnA3sUR"
session_id(null)
preset_id(null)
api_type"completions"
id"gen-1790668439-Kg7DVAU4IfTdRYUacRWk"
upstream_id"msg_011CfXNQgDzc7VFTGfGPrebi"
provider_responses
0
endpoint_id"99aaad94-923b-4fc1-b763-271ed5486f7a"
id"msg_011CfXNQgDzc7VFTGfGPrebi"
is_byokfalse
latency782
model_permaslug"anthropic/claude-sonnet-5.5-20260928"
provider_name"Claude Platform on AWS"
status200
total_cost0.033544
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
totalTags24
adverbTagCount1
adverbTags
0"Eva spoke fast [fast]"
dialogueSentences56
tagDensity0.429
leniency0.857
rawRatio0.042
effectiveRatio0.036
90.80% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1087
totalAiIsmAdverbs2
found
0
adverb"quickly"
count1
1
adverb"precisely"
count1
highlights
0"quickly"
1"precisely"
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)
81.60% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1087
totalAiIsms4
found
0
word"structure"
count1
1
word"silence"
count1
2
word"etched"
count1
3
word"lurched"
count1
highlights
0"structure"
1"silence"
2"etched"
3"lurched"
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
narrationSentences61
matches(empty)
96.02% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount0
narrationSentences61
filterMatches
0"watch"
1"look"
hedgeMatches(empty)
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences93
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
repeatedSegmentCount0
maxSentenceWordsSeen60
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1087
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions13
unquotedAttributions0
matches(empty)
0.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions34
wordCount523
uniqueNames3
maxNameDensity3.82
worstName"Quinn"
maxWindowNameDensity6
worstWindowName"Quinn"
discoveredNames
Quinn20
Kowalski1
Eva13
persons
0"Quinn"
1"Kowalski"
2"Eva"
places(empty)
globalScore0
windowScore0
80.56% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences36
glossingSentenceCount1
matches
0"something like chalk, ringed him in a circle"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches0
per1kWords0
wordCount1087
matches(empty)
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences93
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs43
mean25.28
std22.78
cv0.901
sampleLengths
063
140
220
35
413
514
652
77
833
94
102
1139
1210
1378
144
1520
1635
173
1844
193
204
2117
2270
238
2463
256
2627
277
286
294
3065
313
3266
3313
3442
3518
366
3714
3822
3968
4031
417
4231
88.01% Passive voice overuse
Target: ≤2% passive sentences
passiveCount3
totalSentences61
matches
0"been scraped"
1"been scratched"
2"been polished"
100.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount0
totalVerbs87
matches(empty)
100.00% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount0
semicolonCount0
flaggedSentences0
totalSentences93
ratio0
matches(empty)
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount524
adjectiveStacks0
stackExamples(empty)
adverbCount9
adverbRatio0.01717557251908397
lyAdverbCount1
lyAdverbRatio0.0019083969465648854
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences93
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences93
mean11.69
std9.86
cv0.844
sampleLengths
018
113
221
311
426
58
66
712
88
95
1013
1111
123
136
1419
154
169
1714
187
199
2024
214
222
2310
2429
258
262
2710
2818
2928
3022
314
3213
337
343
3516
3616
373
3812
3932
403
413
421
438
449
4510
4660
478
4835
4928
77.78% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats6
diversityRatio0.5053763440860215
totalSentences93
uniqueOpeners47
0.00% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount0
totalSentences51
matches(empty)
ratio0
100.00% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount13
totalSentences51
matches
0"She tucked a strand behind"
1"His mouth hung open."
2"His lips had gone the"
3"She pressed her thumb against"
4"Her lips moved."
5"She withdrew a small, pale"
6"She held it to the"
7"She reached across the body."
8"Her fingers closed on something"
9"It spun, slowed, lurched, and"
10"She took a step to"
11"She took a step to"
12"It followed again, steady as"
ratio0.255
9.02% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount46
totalSentences51
matches
0"The lift to the disused"
1"A uniformed constable met her"
2"Arc lamps on tripods flooded"
3"Eva Kowalski stood beside the"
4"She tucked a strand behind"
5"Quinn ducked under the tape"
6"Eva pushed her round glasses"
7"Quinn crouched at the platform"
8"The body lay on its"
9"His mouth hung open."
10"His lips had gone the"
11"Chalk lines, or something like"
12"Eva spoke fast, the words"
13"Quinn tilted her head at"
14"Eva laughed, a short cracked"
15"Quinn stood, her knees popping"
16"She pressed her thumb against"
17"Quinn walked the perimeter of"
18"Eva edged closer."
19"Quinn pointed with her torch"
ratio0.902
0.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount0
totalSentences51
matches(empty)
ratio0
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences16
technicalSentenceCount0
matches(empty)
62.50% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags24
uselessAdditionCount3
matches
0"Eva spoke fast, the words tumbling"
1"Quinn stood, her knees popping"
2"Quinn walked, boots crunching on grit"
96.43% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags6
fancyCount3
fancyTags
0"Eva spoke fast (speak)"
1"Eva laughed (laugh)"
2"Eva whispered (whisper)"
dialogueSentences56
tagDensity0.107
leniency0.214
rawRatio0.5
effectiveRatio0.107
82.7569%