Run: 2026-02-19-01KHTQQSNZT7PKFFYHXZBBN9AB

Run Details

Duration:
2m 16s
Cost:
$0.001655
Provider:
Novita

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 fell on London in sheets, a relentless curtain that blurred the neon signs and slicked the cobbles black. Detective Harlow Quinn moved against the downpour, a shadow with military precision. Her worn leather watch, a constant weight on her left wrist, was soaked through. She’d been six steps behind the runner for nine minutes. He was a spindly figure in a dark coat, vanishing and reappearing in the veils of rain, tracing a path westward from the grimy edges of King’s Cross. She’d picked him up outside The Raven’s Nest, seen the furtive exchange of a small wrapped package, and something in his darting, panicked look had triggered the instinct honed over eighteen years. He knew he was being followed. Her sharp jaw was set, teeth clenched against the cold. She kept her distance, using doorways and the swollen gutters for cover, a ghost in her own city. The streetlights swam in the puddles. He cut left down an alley, and she followed, her flat-soled boots silent on the wet asphalt. The alley opened onto a broader street near Camden, but he didn’t head for the main drag. Instead, he vaulted a low wall beside a shuttered newsagent’s and dropped from sight. Quinn scaled the wall without breaking stride, landing in a crouch. Before her was a rusted metal staircase, spiraling down into a maw of darkness beside the old roundel of a disused Tube station. The air that rose from it was warmer, thick with the smell of damp concrete and something else—incense and ozone. The runner had disappeared down those stairs. She hesitated for a fraction of a second. Protocol said call it in, request backup, secure the perimeter. But backup meant questions, meant explaining why she was tailing a suspect connected to Silas’s bar, meant the slow, grinding machinery of the Met. Morris had vanished following a lead off the books. She’d found his service pistol in a canal a week later, rusted shut. Her hand went to her own holster, the grip reassuringly solid under her damp coat. She took the stairs two at a time, descending into the earth. The clang of her footsteps echoed back at her. The light from the street faded, replaced by a flickering, guttering glow from below. The staircase ended in a broad, tiled corridor, part of the old station’s infrastructure. Graffiti covered the walls in chaotic swirls. Up ahead, a heavy-looking iron door stood ajar. Warm, smoky light bled from the crack. Quinn approached, back to the wall, and peered through the gap. The cavernous space of the abandoned ticket hall had been transformed. Dozens of makeshift stalls made of scrap wood and faded cloth were crammed together, lit by oil lamps, fairy lights, and strange, self-contained glowing orbs that hovered in the air. Stalls sold jars of viscous liquids that pulsed with faint light, carved bones, dried herbs hanging in bunches, and what looked like archaic, bladed weapons. People—if they were all people—moved through the crowded aisles. A tall figure with a stitched-together leather coat seemed to have an extra joint in its arms. A woman with skin the color of weathered bark was examining a necklace of sharp teeth. The hum of low conversation was punctuated by harsh bartering in languages Quinn didn’t recognize. The Veil Market. She’d heard the whispers, always dismissed them as junkie fantasy or the ramblings of unhinged informants. It was real. And her suspect was threading his way through the crowd, his dark coat easy to spot as he pushed toward the far end of the hall, where a heavier concentration of armed-looking individuals loitered near another arched exit. To follow him in there was to step into a world with its own rules. She saw no uniforms, no police presence. A stallholder with milky-white eyes turned his head slowly and seemed to look directly at her shadowed form in the doorway. Her pulse thrummed in her ears. This was the edge of everything she knew. Morris had been chasing whispers of something ‘other,’ something that didn’t fit the files. She’d filed his last report herself, a confused rant about ‘bleed-through’ and ‘bone tokens.’ She’d marked it as trauma-induced psychosis, a lie that had eaten at her every day since. The runner was getting away, about to vanish into the deeper tunnels. Quinn’s hand tightened on the doorframe. Another detective might have held back, set up surveillance, played it safe. But safe had gotten Morris killed and his death buried. Safe left these shadows undisturbed. She took a deep breath of the strange, spiced air and stepped across the threshold. The atmosphere changed immediately. The hum of conversation dipped for a moment as several nearby figures paused their haggling to watch her. She held her head high, projecting an authority she didn’t feel, and moved into the flow of traffic, heading in the direction her suspect had gone. Her eyes scanned, not just for him, but for exits, for threats. She passed a stall selling vials of what was labeled ‘Fae Moon Dew.’ Another had stacks of old books bound in what looked like human skin. The stallholder, a gaunt man with ink-stained fingers, grinned at her, revealing pointed teeth. “Looking for something specific, officer?” She ignored him, her focus ahead. She caught a glimpse of the dark coat slipping through a beaded curtain at the hall’s far end. Picking up her pace, she shouldered past a hulking figure wrapped in reeking furs. A hand closed around her upper arm, the grip like iron. “You’re a long way from your beat.” Quinn turned. The man holding her was broad-shouldered, with olive skin and short, curly dark brown hair. His eyes were a warm brown, but they held no warmth as they looked her over. A scar ran along his left forearm, pale against his skin. He wore a simple dark sweater, but around his neck, a Saint Christopher medallion glinted in the lamplight. She tried to pull her arm free. “Let go.” “You shouldn’t be here,” he said, his accent Spanish. Seville, if she had to guess. His voice was low, meant only for her. “This isn’t a place for your kind.” “My kind?” “The law-abiding. The daylight kind.” His gaze flicked over her shoulder, toward the beaded curtain. “You’re following Miguel. He’s small-time. Not worth what will happen if you keep going.” “You know him?” “I know he’s frightened. And frightened people do stupid things, especially here.” He released her arm but stepped into her path, blocking her way. “Turn around. Go back up the stairs.” Quinn’s eyes narrowed. “Who are you?” “Someone who patches up the messes made when your world bumps into this one. My name is Tomás Herrera.” He said it like it should mean something to her. It didn’t. “You’re Quinn. Harlow Quinn. You were Morris’s partner.” The name hit her like a physical blow. The noise of the market seemed to recede. “How do you know that?” “Because he came here too. Once.” Tomás’s expression softened, but it was pity, not kindness. “He was asking the wrong questions in the wrong places. Like you are now.” “What happened to him?” “He found an answer.” Tomás glanced past her again, his body tensing. “You need to leave. Now.” Quinn followed his look. Three figures had detached themselves from the shadows near the beaded curtain. They weren’t looking at her; they were looking at Tomás. And they were moving toward them.
created_at"2026-02-19T10:39:40.721Z" 2/19/2026, 10:39:40 AM
model"microsoft/wizardlm-2-8x22b"
app_id182717
external_user(null)
streamedtrue
cancelledfalse
latency1109
moderation_latency(null)
generation_time135324
tokens_prompt1143
tokens_completion1943
native_tokens_prompt1005
native_tokens_completion1664
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)
origin"https://ncbench.com/"
is_byokfalse
finish_reason"stop"
native_finish_reason"stop"
usage0.00165478
router(null)
provider_responses
0
endpoint_id"92d0658f-b48a-4746-96e7-75ea71d5e060"
id"cd920d31-bc51-4c89-b28f-3ab1f6f4ddad"
is_byokfalse
latency1109
model_permaslug"microsoft/wizardlm-2-8x22b"
provider_name"Novita"
status200
api_type"completions"
id"gen-1771497580-fwhpYNB6Uj0cLsV1J6yZ"
upstream_id"cd920d31-bc51-4c89-b28f-3ab1f6f4ddad"
total_cost0.00165478
cache_discount(null)
upstream_inference_cost0
provider_name"Novita"

Evaluation details

Result Evaluator Details Meta Data
100.00% Adverbs in dialogue tags
Target: ≤10% dialogue tags with adverbs
totalTags9
adverbTagCount0
adverbTags(empty)
dialogueSentences20
tagDensity0.45
leniency0.9
rawRatio0
effectiveRatio0
96.03% AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount1259
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)
52.34% AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount1259
totalAiIsms12
found
0
word"weight"
count1
1
word"tracing"
count1
2
word"maw"
count1
3
word"footsteps"
count1
4
word"echoed"
count1
5
word"chaotic"
count1
6
word"pulsed"
count1
7
word"pulse"
count1
8
word"scanned"
count1
9
word"hulking"
count1
10
word"warmth"
count1
11
word"flicked"
count1
highlights
0"weight"
1"tracing"
2"maw"
3"footsteps"
4"echoed"
5"chaotic"
6"pulsed"
7"pulse"
8"scanned"
9"hulking"
10"warmth"
11"flicked"
100.00% Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches1
maxInWindow1
found
0
label"eyes widened/narrowed"
count1
highlights
0"eyes narrowed"
100.00% Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells0
narrationSentences87
matches(empty)
44.33% Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount2
hedgeCount4
narrationSentences87
filterMatches
0"watch"
1"look"
hedgeMatches
0"seemed to"
1"tried to"
100.00% Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences101
gibberishSentences0
adjustedGibberishSentences0
longSentenceCount0
runOnParagraphCount0
giantParagraphCount0
wordSaladCount0
repetitionLoopCount0
controlTokenCount0
maxSentenceWordsSeen39
ratio0
matches(empty)
100.00% Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans0
markdownWords0
totalWords1245
ratio0
matches(empty)
100.00% Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions4
unquotedAttributions0
matches(empty)
100.00% Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions30
wordCount1066
uniqueNames19
maxNameDensity0.75
worstName"Quinn"
maxWindowNameDensity1.5
worstWindowName"Quinn"
discoveredNames
London1
Harlow1
Quinn8
King1
Cross1
Raven1
Nest1
Camden1
Tube1
Silas1
Met1
Veil1
Market1
Morris3
Saint1
Christopher1
Spanish1
Seville1
Tomás3
persons
0"Harlow"
1"Quinn"
2"King"
3"Cross"
4"Nest"
5"Silas"
6"Market"
7"Morris"
8"Saint"
9"Christopher"
10"Tomás"
places
0"London"
1"Raven"
2"Camden"
3"Seville"
globalScore1
windowScore1
100.00% Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences78
glossingSentenceCount1
matches
0"looked like archaic, bladed weapons"
100.00% "Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches1
per1kWords0.803
wordCount1245
matches
0"not just for him, but for exits, for threats"
100.00% Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount0
totalSentences101
matches(empty)
100.00% Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs35
mean35.57
std26.9
cv0.756
sampleLengths
056
166
251
385
47
564
615
771
811
9123
1022
1138
1243
1358
1412
1548
1660
1745
1838
1911
207
2162
229
2330
242
2529
263
2731
286
2939
3021
3129
324
3317
3432
85.10% Passive voice overuse
Target: ≤2% passive sentences
passiveCount5
totalSentences87
matches
0"being followed"
1"been transformed"
2"were crammed"
3"was punctuated"
4"was labeled"
0.00% Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount6
totalVerbs189
matches
0"was tailing"
1"was examining"
2"was threading"
3"were looking"
4"were moving"
57.99% Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount3
semicolonCount1
flaggedSentences3
totalSentences101
ratio0.03
matches
0"The air that rose from it was warmer, thick with the smell of damp concrete and something else—incense and ozone."
1"People—if they were all people—moved through the crowded aisles."
2"They weren’t looking at her; they were looking at Tomás."
100.00% Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount780
adjectiveStacks0
stackExamples(empty)
adverbCount11
adverbRatio0.014102564102564103
lyAdverbCount5
lyAdverbRatio0.00641025641025641
100.00% Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences101
echoCount0
echoWords(empty)
100.00% Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences101
mean12.33
std7.46
cv0.605
sampleLengths
020
112
214
310
428
532
66
710
818
96
1017
1117
1214
1311
1423
1520
167
178
1810
1924
209
2113
2215
2312
249
2514
2614
277
288
297
3011
3111
3230
3325
349
3517
3616
3715
383
3916
403
4138
4215
437
4421
456
468
4714
4830
4912
57.10% Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats9
diversityRatio0.39603960396039606
totalSentences101
uniqueOpeners40
39.22% Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount1
totalSentences85
matches
0"Instead, he vaulted a low"
ratio0.012
69.41% Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount32
totalSentences85
matches
0"Her worn leather watch, a"
1"She’d been six steps behind"
2"He was a spindly figure"
3"She’d picked him up outside"
4"He knew he was being"
5"Her sharp jaw was set,"
6"She kept her distance, using"
7"He cut left down an"
8"She hesitated for a fraction"
9"She’d found his service pistol"
10"Her hand went to her"
11"She took the stairs two"
12"She’d heard the whispers, always"
13"It was real."
14"She saw no uniforms, no"
15"Her pulse thrummed in her"
16"She’d filed his last report"
17"She took a deep breath"
18"She held her head high,"
19"Her eyes scanned, not just"
ratio0.376
30.59% Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount73
totalSentences85
matches
0"The rain fell on London"
1"Detective Harlow Quinn moved against"
2"Her worn leather watch, a"
3"She’d been six steps behind"
4"He was a spindly figure"
5"She’d picked him up outside"
6"He knew he was being"
7"Her sharp jaw was set,"
8"She kept her distance, using"
9"The streetlights swam in the"
10"He cut left down an"
11"The alley opened onto a"
12"Quinn scaled the wall without"
13"The air that rose from"
14"The runner had disappeared down"
15"She hesitated for a fraction"
16"Protocol said call it in,"
17"Morris had vanished following a"
18"She’d found his service pistol"
19"Her hand went to her"
ratio0.859
100.00% Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount2
totalSentences85
matches
0"Before her was a rusted"
1"To follow him in there"
ratio0.024
100.00% Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences50
technicalSentenceCount1
matches
0"Dozens of makeshift stalls made of scrap wood and faded cloth were crammed together, lit by oil lamps, fairy lights, and strange, self-contained glowing orbs th…"
69.44% Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags9
uselessAdditionCount1
matches
0"Tomás glanced, his body tensing"
100.00% Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags2
fancyCount0
fancyTags(empty)
dialogueSentences20
tagDensity0.1
leniency0.2
rawRatio0
effectiveRatio0
83.3851%