NC Bench
Overview
About
Tests
Categories
Models
Vendors
Compare
Run: 2026-10-08-01M4DAMEN945F2F3VQM40BYVVZ
Run Details
Model:
Claude Haiku 5.5 (Adaptive)
Test:
Bad Writing Habits
Scenario:
Literary fiction: old friends reunite
Duration:
21.7s
Cost:
$0.002390
Provider:
Claude Platform on AWS
Evaluation summary
Score
Evaluator
Details Link
100.00%
Adverbs in dialogue tags
(details)
93.53%
AI-ism adverb frequency
(details)
100.00%
AI-ism character names
(details)
100.00%
AI-ism location names
(details)
87.06%
AI-ism word frequency
(details)
100.00%
Cliché density
(details)
100.00%
Emotion telling (show vs. tell)
(details)
100.00%
Filter word density
(details)
100.00%
Gibberish response detection
(details)
100.00%
Markdown formatting overuse
(details)
100.00%
Missing dialogue indicators (quotation marks)
(details)
79.95%
Name drop frequency
(details)
66.67%
Narrator intent-glossing
(details)
100.00%
"Not X but Y" pattern overuse
(details)
100.00%
Overuse of "that" (subordinate clause padding)
(details)
100.00%
Paragraph length variance
(details)
96.27%
Passive voice overuse
(details)
100.00%
Past progressive (was/were + -ing) overuse
(details)
100.00%
Em-dash & semicolon overuse
(details)
100.00%
Purple prose (modifier overload)
(details)
100.00%
Repeated phrase echo
(details)
100.00%
Sentence length variance
(details)
85.38%
Sentence opener variety
(details)
100.00%
Adverb-first sentence starts
(details)
45.00%
Pronoun-first sentence starts
(details)
22.50%
Subject-first sentence starts
(details)
0.00%
Subordinate conjunction sentence starts
(details)
30.08%
Technical jargon density
(details)
75.00%
Useless dialogue additions
(details)
100.00%
Dialogue tag variety (said vs. fancy)
(details)
86.0477%
Overall
Model input
System
You are an expert and award-winning novel writer in the dark Literary Fiction genre. Your readers are hooked on your stories and can't wait to read the book you have in store. <important> - MUST: Start each chapter in medias res (in the middle of action). AVOID talking about the weather, time of day, the position of the sun, or other boring stuff. - The instructions/summary are just a that, a summary. Feel free to add details, stretch sections and add interesting transitions/descriptions/fillers. - When called for it, have fast-paced action, but otherwise take things slow. This is an experience, and not for people with short attention spans. Enjoy the wine, not slurp Red Bull. - Add descriptions when transitioning places/times/etc, or when coming by new places (e.g. a corner shop, a cafe, etc.). But remember: NOT at the start or end of a chapter. - MUST: AVOID ending in internal monologue, reminiscing or waiting for the next day. A good chapter end is when we're ending right when shit hits the fan. We want to have the reader be excited for what comes next. So no reminiscing, contemplating or summarizing the day. It's popcorn time! </important>
User/Human
Take into account the following glossary of characters/locations/items/lore... when writing your response: <codex> <character id="4412" name="Aurora Carter" aliases="Rory, Laila, Aurora, Carter, Malphora" pronouns="she/her" species="Human" storyRole="Protagonist"> <eyeColor>bright blue</eyeColor> <hair>straight shoulder-length black</hair> <height>5'6"</height> <age>25</age> <scar>small crescent-shaped scar on her left wrist from a childhood accident</scar> <description>Aurora, referred to by her friends as Rory, is cool-headed, intelligent, and known for quick out-of-the-box thinking.</description> <backstory>Born in Cardiff to an Irish barrister father (Brendan Carter) and Welsh teacher mother (Jennifer Carter née Ellis), Rory reluctantly pursued Pre-Law at Cardiff University. She fled to London at the behest of her childhood friend Eva after leaving an abusive ex named Evan. By day, Rory works part-time as a delivery person for Yu-Fei Cheung's Golden Empress restaurant. She lives in a flat above Silas' bar.</backstory> </character> <character id="4413" name="Silas Blackwood" aliases="Silas, Si, the Spymaster" pronouns="he/him" species="Human" storyRole="Mentor"> <eyeColor>hazel</eyeColor> <hair>grey-streaked auburn</hair> <height>6'1"</height> <age>58</age> <beard>neatly trimmed, matching his grey-streaked auburn hair</beard> <limp>slight limp in his left leg from an old knee injury</limp> <signetRing>silver signet ring, always worn on his right hand</signetRing> <description>Silas is a retired intelligence operative turned bar owner. He carries himself with quiet authority.</description> <backstory>Former MI6 field agent who retired after a botched operation in Prague left him with a permanent knee injury. He opened "The Raven's Nest" bar in Soho as a front for his network of contacts.</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> </codex> <proseGuidelines> <styleGuide> - Write in past tense and use British English spelling and grammar - Keep a Flesch reading ease score of 60 - Respect the the Royal Order of Adjectives: The order is: opinion, size, age, shape, color, origin, material, purpose, followed by the noun itself (e.g., "a lovely little old rectangular green French silver whittling knife") - Respect the ablaut reduplication rule (e.g. tick-tock, flip-flop) - Write in active voice - Passive voice: <bad>The book was read by Sarah.</bad> - Active voice: <good>Sarah read the book.</good> - Reduce the use of passive verbs - <bad>For a moment, I was tempted to throw in the towel.</bad> - <good>For a moment, I felt tempted to throw in the towel.</good> - Avoid misplaced modifiers that can cause confusion when starting with "-ing" words: - <bad>Considering going to the store, the empty fridge reflected in Betty's eyes.</bad> - <good>Betty stared into the empty fridge. It was time to go to the store.</good> - Avoid redundant adverbs that state the obvious meaning already contained in the verb: - <bad>She whispered quietly to her mom.</bad> - <good>She whispered to her mom.</good> - Use stronger, more descriptive verbs over weak ones: - <bad>Daniel drove quickly to his mother's house.</bad> - <good>Daniel raced to his mother's house.</good> - Omit adverbs that don't add solid meaning like "extremely", "definitely", "truly", "very", "really": - <bad>The movie was extremely boring.</bad> - <good>The movie was dull.</good> - Use adverbs to replace clunky phrasing when they increase clarity: - <bad>He threw the bags into the corner in a rough manner.</bad> - <good>He threw the bags into the corner roughly.</good> - Avoid making simple thoughts needlessly complex: - <bad>After I woke up in the morning the other day, I went downstairs, turned on the stove, and made myself a very good omelet.</bad> - <good>I cooked a delicious omelet for breakfast yesterday morning.</good> - Never backload sentences by putting the main idea at the end: - <bad>I decided not to wear too many layers because it's really hot outside.</bad> - <good>It's sweltering outside today, so I dressed light.</good> - Omit nonessential details that don't contribute to the core meaning: - <bad>It doesn't matter what kind of coffee I buy, where it's from, or if it's organic or not—I need to have cream because I really don't like how the bitterness makes me feel.</bad> - <good>I add cream to my coffee because the bitter taste makes me feel unwell.</good> - Always follow the "show, don't tell" principle. For instance: - Telling: <bad>Michael was terribly afraid of the dark.</bad> - Showing: <good>Michael tensed as his mother switched off the light and left the room.</good>- Telling: <bad>I walked through the forest. It was already Fall, and I was getting cold.</bad> - Showing: <good>Dry orange leaves crunched under my feet. I pulled my coat's collar up and rubbed my hands together.</good>- Add sensory details (sight, smell, taste, sound, touch) to support the "showing" (but keep an active voice) - <bad>The room was filled with the scent of copper.</bad> - <good>Copper stung my nostrils. Blood. Recent.</good> - Use descriptive language more sporadically. While vivid descriptions are engaging, human writers often use them in bursts rather than consistently throughout a piece. When adding them, make them count! Like when we transition from one location to the next, or someone is reminiscing their past, or explaining a concept/their dream... - Avoid adverbs and clichés and overused/commonly used phrases. Aim for fresh and original descriptions. - Avoid writing all sentences in the typical subject, verb, object structure. Mix short, punchy sentences with long, descriptive ones. Drop fill words to add variety. Like so: <good>Locked. Seems like someone doesn't want his secrets exposed. I can work with that.</good> - Convey events and story through dialogue. It is important to keep a unique voice for every character and make it consistent. - Write dialogue that reveals characters' personalities, motivations, emotions, and attitudes in an interesting and compelling manner - Leave dialogue unattributed. If needed, only use "he/she said" dialogue tags and convey people's actions or face expressions through their speech. Dialogue always is standalone, never part of a paragraph. Like so: - <bad>"I don't know," Helena said nonchalantly, shrugging her shoulders</bad> - <good>"No idea" "Why not? It was your responsibility"</good> - Avoid boring and mushy dialog and descriptions, have dialogue always continue the action, never stall or include unnecessary fluff. Vary the descriptions to not repeat yourself. Avoid conversations that are just "Let's go" "yes, let's" or "Are you ready?" "Yes I'm ready". Those are not interesting. Think hard about every situtation and word of text before writing dialogue. If it doesn't serve a purpose and it's just people talking about their day, leave it. No one wants to have a normal dinner scene, something needs to happen for it to be in the story. Words are expensive to print, so make sure they count! - Put dialogue on its own paragraph to separate scene and action. - Use body language to reveal hidden feelings and implied accusations- Imply feelings and thoughts, never state them directly - NEVER use indicators of uncertainty like "trying" or "maybe" - NEVER use em-dashes, use commas for asides instead </styleGuide> <voiceGuide> Each character in the story needs to have distinct speech patterns: - Word choice preferences - Sentence length tendencies - Cultural/educational influences - Verbal tics and catchphrases Learn how each person talks and continue in their style, and use their Codex entries as reference. <examples> - <bad>"We need to go now." "Yes, we should leave." "I agree."</bad> <good>"Time's up." "Indeed, our departure is rather overdue." "Whatever, let's bounce."</good> - Power Dynamic Example: <bad> "We need to discuss the contract." "Yes, let's talk about it." "I have concerns." </bad> <good> "A word about the contract." "Of course, Mr. Blackwood. Whatever you need." "The terms seem..." A manicured nail tapped the desk. "Inadequate." "I can explain every-" "Can you?" </good> </examples> </voiceGuide> <dialogueFlow> When writing dialogue, consider that it usually has a goal in mind, which gives it a certain flow. Make dialogue sections also quite snappy in the back and forth, and don't spread the lines out as much. It's good to have details before, after, or as a chunk in-between, but we don't want to have a trail of "dialogue breadcrumbs" spread throughout a conversation. <examples> - Pattern 1 - Question/Deflection/Revelation: <good> "Where were you last night?" "Work. The usual." "Lipstick's an interesting shade for spreadsheets." </good> - Pattern 2 - Statement/Contradiction/Escalation: <good> "Your brother's clean." "Tommy doesn't touch drugs." "I'm holding his tox screen." </good> - Pattern 3 - Observation/Denial/Truth: <good> "That's a new watch." "Birthday gift." "We both know what birthdays mean in this business." </good> - Example - A Simple Coffee Order: <bad> "I'll have a coffee." "What size?" "Large, please." </bad> <good> "Black coffee.""Size?""Large. Been a long night." "That bodega shooting?" "You watch too much news." "My brother owns that store." </good> This short exchange: - Advances plot (reveals connection to crime) - Shows character (cop working late) - Creates tension (unexpected connection) - Sets up future conflict (personal stake) - Example - Dinner Scene: <bad> "Pass the salt." "Here you go." "Thanks." </bad> <good> "Salt?" "Perfect as is. Mother's recipe." "Mother always did prefer... bland things." "Unlike your first wife?" </good> - Example - Office Small Talk: <bad> "Nice weather today." "Yes, very nice." "Good for golf." </bad> <good> "Perfect golf weather." "Shame about your membership." "Temporary suspension. Board meets next week." "I know. I called the vote." </good> </examples> </dialogueFlow> <subtextGuide> - Layer dialogue with hidden meaning: <bad>"I hate you!" she yelled angrily.</bad> <good>"I made your favorite dinner." The burnt pot sat accusingly on the stove.</good> - Create tension through indirect communication: <bad>"Are you cheating on me?"</bad> <good>"Late meeting again?" The lipstick stain on his collar caught the light.</good> <examples> - Example 1 - Unspoken Betrayal: <bad> "Did you tell them about our plans?" "No, I would never betray you." "I don't believe you." </bad> <good> "Funny. Johnson mentioned our expansion plans today." "The market's full of rumors." "Mentioned the exact numbers, actually." The pen in his hand snapped. </good> - Example 2 - Failed Marriage: <bad> "You're never home anymore." "I have to work late." "I miss you." </bad> <good> "Your dinner's in the microwave. Again." "Meetings ran long." "They always do." She folded the same shirt for the third time. </good> - Example 3 - Power Struggle: <bad> "You can't fire me." "I'm the boss." "I'll fight this." </bad> <good> "That's my father's nameplate you're sitting behind." "Was." "The board meeting's on Thursday." </good> </examples> </subtextGuide> <sceneDetail> While writing dialogue makes things more fun, sometimes we need to add detail to not have it be a full on theatre piece. <examples> - Example A (Power Dynamic Scene) <good> "Where's my money?" The ledger snapped shut. "I need more time." "Interesting." He pulled out a familiar gold pocket watch. My mother's. "Time is exactly what you bargained with last month." "That was different-" "Was it?" The watch dangled between us. "Four generations of O'Reillys have wound this every night. Your mother. Your grandmother. Your great-grandmother.Shall we see who winds it next?" </good> - Example B (Action Chase) It's much better to be in the head of the character experiencing it, showing a bit of their though-process, mannerisms and personality: <good> Three rules for surviving a goblin chase in Covent Garden: Don't run straight. Don't look back. Don't let them herd you underground. I broke the first rule at Drury Lane. Rookie mistake. The fruit cart I dodged sailed into the wall behind me. Glass shattered. Someone screamed about insurance. *Tourist season's getting rough*, the scream seemed to say. Londoners adapt fast. "Oi! Market's closed!" The goblin's accent was pure East End. They're evolving. Learning. I spotted the Warren Street tube station sign ahead. *Shit.* There went rule three. </good> - Example C (Crime Scene Investigation) <good> "Greek." Davies snapped photos of the symbols. "No, wait. Reverse Greek." "Someone's been watching too many horror films." I picked up a receipt from the floor. Occult supply shop in Camden. Paid by credit card. *Amateur hour*. "Could be dangerous though," Davies said. "Remember Bristol?" "Bristol was Sanskrit. And actual cultists." I pointed to the nearest symbol. "This genius wrote 'darkness' backwards but used a Sigma instead of an S. It's summoning Instagram followers at best." "Speaking of followers..." Davies pointed to heavy foot traffic in the dust. Multiple sets. All new trainers. *Ah.* "Anyone check local uni paranormal societies?" </good> - Example D (Dialogue-heavy) <good> "Found your card in her wallet." The detective slid it across the table. Worn edges. Folded corner. Three years old at least. "Standard practice. I work missing persons." "Funny thing about missing persons.Eventually they stop being missing. Turn up in the Thames. Under motorways. In pieces." "I don't-" "You found four last year. All breathing." She opened a file. "This would've been your first dead one. If we hadn't found her first." The card felt heavier somehow. </good> </examples> </sceneDetail> <sceneOpenings> KEY RULE: Never open with more than one paragraph of description, no matter how beautiful. Description should be earned through action and dialogue. Instead of opening with setting description, open with: - A character doing something specific - A line of dialogue that raises questions - An unexpected observation - A moment of tension or conflict - Start with a hook that's 1-2 sentences maximum - Follow with a smaller paragraph that provides immediate context - Vary paragraph size from the start to create visual interest </sceneOpenings> </proseGuidelines> Write a chapter based on the following instructions: <instructions> Write a fully fleshed out scene of at least 1000 words in the literary fiction genre. Two old friends who haven't seen each other in years run into each other unexpectedly at a bar. One of them has changed significantly. Through their conversation and the tension between who they were and who they've become, reveal the weight of time and unspoken regret. 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
Messages
Meta
AI
"Nobody in London calls me that."
The voice behind me kept
its shape anyway, soft at the edges and sharp in the middle, the way the Welsh valleys left their mark on a vowel.
I set the Golden Empress
bag on the bar. Sweet and sour pork, two portions of egg fried rice, one bag of prawn crackers that had already lost their crunch to the ride across Soho.
The order belonged to a
flat in Dean Street.
I did not belong at
the bar for another twenty minutes, but my feet had stopped moving. "Ror," Eva said. "Still hiding behind takeaway." I turned.
She had cut her hair
into a severe black bob that ended at her jaw, and the blonde had gone entirely.
A camel coat hung from
her shoulders, cut so well it
looked like it had been poured on
.
The Eva I remembered wore
her brother's football shirt to bed and drank Bacardi from the bottle on the wall outside the Splott chip shop.
This woman had a wedding
band on her right hand, which made no sense, and she held her clutch against her ribs like it might be taken from her. "Eva." "You look well."
Her eyes flicked over my
apron, the delivery badge, the faded Cardiff University lanyard I had forgotten to remove from my bag strap. "Working for the Chinese restaurant. Good for you." "Golden Empress pays on time. Which is more than most people manage."
Silas appeared at the end
of the bar without seeming to cross the room. He wiped a glass,
slowly
, the signet ring on his right hand catching the green glow from the window.
He did not look at
me.
He looked at Eva, the
way he looked at men who came in asking for a booth with a view of the door. "Drink?" he said. "Tonic. Ice. Lime if you've got one." "You used to drink anything with a label on it," I said. "I used to do a lot of things."
She set her clutch on
the bar and turned the ring around her finger, once, twice. "Sober eighteen months. Don't make the face." "I wasn't making a face." "You were thinking about it. You always had that little crease between your brows when you were deciding whether to be kind."
Silas placed the tonic in
front of her and went back to polishing, unhurried, as if he had heard nothing and remembered everything. I leaned on the bar.
My knuckles found the old
crescent scar on my left wrist, the one I'd got falling off Eva's garden wall when we were nine.
She had been the one
who climbed down first to check I was alive. "You didn't come to London to ask about my delivery schedule." "No."
Eva took a sip, grimaced
at the bitterness, and kept drinking. "I came for a meeting in Mayfair. Property. My husband's company is buying a development on the Bay." "Good for him." "Good for us. Aurora, the letters. Did you get them?" Four years of
silence
sat between us on the mahogany.
The last letter had arrived
at my old flat in Cardiff the week after I left, a cream envelope with a train ticket inside and a note in Eva's looping hand. *Come now. Don't wait for him to come to the door.* I had read it on the floor of a bedsit in Hackney with a cracked phone and a black eye that
was still turning
yellow at the edges.
Then I had stopped answering
her calls, one after another, because answering meant explaining, and explaining meant saying his name out loud. "Some," I said. "Which ones?" "The ones that mattered." Her mouth tightened.
She set the glass down
with a precise little click, the way you'd place a bomb on a table if you wanted everyone to notice. "Then you know he's been asking around." The ceiling fan above the bar kept turning. Silas's cloth stopped moving on the glass. "Who has?" My voice came out flat, which was the only way it came out these days. "Evan." Eva pushed her clutch an inch closer to me, as if it held something she'd rather not carry any longer. "He was at my husband's office last Tuesday. He asked for you by your full name, Aurora Carter, and he had a photo of you on the Golden Empress bike." Silas set the glass down behind the bar with a soft, deliberate sound. "Was there a reason you didn't call me first?" I asked. Eva's laugh cracked in the middle. "Ror. I've been calling you for four years."
created_at
"2026-10-08T08:38:46.203Z"
10/8/2026, 8:38:46 AM
model
"anthropic/claude-haiku-5.5-20261007"
app_id
182717
external_user
(null)
streamed
true
cancelled
false
latency
20909
moderation_latency
(null)
generation_time
21637
tokens_prompt
4445
tokens_completion
1043
native_tokens_prompt
6496
native_tokens_completion
3480
native_tokens_completion_images
(null)
native_tokens_reasoning
2055
native_tokens_cached
0
num_media_prompt
(null)
num_input_audio_prompt
(null)
num_media_completion
0
num_search_results
(null)
num_fetches
(null)
web_search_engine
(null)
origin
"https://ncbench.com/"
is_byok
false
finish_reason
"stop"
native_finish_reason
"end_turn"
service_tier
"default"
usage
0.0023896
router
(null)
user_agent
"langchainjs-openai/1.0.0 ((node/v24.21.0; linux; x64))"
http_referer
(null)
request_id
"req-1791448726-S5mRt3IEF8ZmFdYGZGFr"
session_id
(null)
preset_id
(null)
api_type
"completions"
id
"gen-1791448726-9yI9C1KsKgquMB295M2A"
upstream_id
"msg_011CfpU716ovkDHvt6SzcZoD"
provider_responses
0
endpoint_id
"b6cdf493-3a61-441f-a134-e7944fddb980"
id
"msg_011CfpU716ovkDHvt6SzcZoD"
is_byok
false
latency
806
model_permaslug
"anthropic/claude-haiku-5.5-20261007"
provider_name
"Claude Platform on AWS"
status
200
total_cost
0.0023896
cache_discount
(null)
upstream_inference_cost
0
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
totalTags
10
adverbTagCount
0
adverbTags
(empty)
dialogueSentences
28
tagDensity
0.357
leniency
0.714
rawRatio
0
effectiveRatio
0
93.53%
AI-ism adverb frequency
Target: <2% AI-ism adverbs (58 tracked)
wordCount
773
totalAiIsmAdverbs
1
found
0
adverb
"slowly"
count
1
highlights
0
"slowly"
100.00%
AI-ism character names
Target: 0 AI-default names (16 tracked, −20% each)
codexExemptions
0
"Blackwood"
found
(empty)
100.00%
AI-ism location names
Target: 0 AI-default location names (33 tracked, −20% each)
codexExemptions
(empty)
found
(empty)
87.06%
AI-ism word frequency
Target: <2% AI-ism words (290 tracked)
wordCount
773
totalAiIsms
2
found
0
word
"flicked"
count
1
1
word
"silence"
count
1
highlights
0
"flicked"
1
"silence"
100.00%
Cliché density
Target: ≤1 cliche(s) per 800-word window
totalCliches
0
maxInWindow
0
found
(empty)
highlights
(empty)
100.00%
Emotion telling (show vs. tell)
Target: ≤3% sentences with emotion telling
emotionTells
0
narrationSentences
39
matches
(empty)
100.00%
Filter word density
Target: ≤3% sentences with filter/hedge words
filterCount
0
hedgeCount
0
narrationSentences
39
filterMatches
(empty)
hedgeMatches
(empty)
100.00%
Gibberish response detection
Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words)
analyzedSentences
57
gibberishSentences
0
adjustedGibberishSentences
0
longSentenceCount
0
runOnParagraphCount
0
giantParagraphCount
0
wordSaladCount
0
repetitionLoopCount
0
controlTokenCount
0
repeatedSegmentCount
0
maxSentenceWordsSeen
37
ratio
0
matches
(empty)
100.00%
Markdown formatting overuse
Target: ≤5% words in markdown formatting
markdownSpans
1
markdownWords
11
totalWords
773
ratio
0.014
matches
0
"Come now. Don't wait for him to come to the door."
100.00%
Missing dialogue indicators (quotation marks)
Target: ≤10% speech attributions without quotation marks
totalAttributions
10
unquotedAttributions
0
matches
(empty)
79.95%
Name drop frequency
Target: ≤1.0 per-name mentions per 100 words
totalMentions
25
wordCount
571
uniqueNames
14
maxNameDensity
1.4
worstName
"Eva"
maxWindowNameDensity
2
worstWindowName
"Eva"
discoveredNames
Welsh
1
Golden
1
Empress
1
Soho
1
Dean
1
Street
1
Eva
8
Bacardi
1
Splott
1
Cardiff
2
University
1
Silas
4
Four
1
Hackney
1
persons
0
"Eva"
1
"Silas"
places
0
"Soho"
1
"Dean"
2
"Street"
3
"Splott"
4
"Cardiff"
5
"Hackney"
globalScore
0.799
windowScore
1
66.67%
Narrator intent-glossing
Target: ≤2% narration sentences with intent-glossing patterns
analyzedSentences
30
glossingSentenceCount
1
matches
0
"looked like it had been poured on"
100.00%
"Not X but Y" pattern overuse
Target: ≤1 "not X but Y" per 1000 words
totalMatches
0
per1kWords
0
wordCount
773
matches
(empty)
100.00%
Overuse of "that" (subordinate clause padding)
Target: ≤2% sentences with "that" clauses
thatCount
0
totalSentences
57
matches
(empty)
100.00%
Paragraph length variance
Target: CV ≥0.5 for paragraph word counts
totalParagraphs
32
mean
24.16
std
27.46
cv
1.137
sampleLengths
0
6
1
89
2
7
3
2
4
93
5
1
6
34
7
12
8
62
9
3
10
7
11
12
12
31
13
5
14
22
15
23
16
54
17
30
18
3
19
10
20
102
21
3
22
2
23
4
24
28
25
7
26
15
27
17
28
51
29
13
30
11
31
14
96.27%
Passive voice overuse
Target: ≤2% passive sentences
passiveCount
1
totalSentences
39
matches
0
"been poured"
100.00%
Past progressive (was/were + -ing) overuse
Target: ≤2% past progressive verbs
pastProgressiveCount
1
totalVerbs
87
matches
0
"was still turning"
100.00%
Em-dash & semicolon overuse
Target: ≤2% sentences with em-dashes/semicolons
emDashCount
0
semicolonCount
0
flaggedSentences
0
totalSentences
57
ratio
0
matches
(empty)
100.00%
Purple prose (modifier overload)
Target: <4% adverbs, <2% -ly adverbs, no adj stacking
wordCount
571
adjectiveStacks
0
stackExamples
(empty)
adverbCount
14
adverbRatio
0.024518388791593695
lyAdverbCount
3
lyAdverbRatio
0.005253940455341506
100.00%
Repeated phrase echo
Target: ≤20% sentences with echoes (window: 2)
totalSentences
57
echoCount
0
echoWords
(empty)
100.00%
Sentence length variance
Target: CV ≥0.4 for sentence word counts
totalSentences
57
mean
13.56
std
9.23
cv
0.68
sampleLengths
0
6
1
28
2
9
3
26
4
9
5
17
6
3
7
4
8
2
9
21
10
18
11
25
12
29
13
1
14
26
15
8
16
12
17
14
18
19
19
6
20
23
21
3
22
7
23
12
24
24
25
7
26
5
27
22
28
23
29
5
30
24
31
14
32
11
33
12
34
18
35
3
36
10
37
10
38
31
39
2
40
37
41
22
42
3
43
2
44
4
45
3
46
25
47
7
48
8
49
7
85.38%
Sentence opener variety
Target: ≥60% unique sentence openers
consecutiveRepeats
3
diversityRatio
0.543859649122807
totalSentences
57
uniqueOpeners
31
100.00%
Adverb-first sentence starts
Target: ≥3% sentences starting with an adverb
adverbCount
1
totalSentences
32
matches
0
"Then I had stopped answering"
ratio
0.031
45.00%
Pronoun-first sentence starts
Target: ≤30% sentences starting with a pronoun
pronounCount
14
totalSentences
32
matches
0
"I set the Golden Empress"
1
"I did not belong at"
2
"She had cut her hair"
3
"Her eyes flicked over my"
4
"He wiped a glass, slowly,"
5
"He did not look at"
6
"He looked at Eva, the"
7
"She set her clutch on"
8
"I leaned on the bar."
9
"My knuckles found the old"
10
"She had been the one"
11
"Her mouth tightened."
12
"She set the glass down"
13
"My voice came out flat,"
ratio
0.438
22.50%
Subject-first sentence starts
Target: ≤72% sentences starting with a subject
subjectCount
28
totalSentences
32
matches
0
"The voice behind me kept"
1
"I set the Golden Empress"
2
"The order belonged to a"
3
"I did not belong at"
4
"She had cut her hair"
5
"A camel coat hung from"
6
"The Eva I remembered wore"
7
"This woman had a wedding"
8
"Her eyes flicked over my"
9
"Silas appeared at the end"
10
"He wiped a glass, slowly,"
11
"He did not look at"
12
"He looked at Eva, the"
13
"She set her clutch on"
14
"Silas placed the tonic in"
15
"I leaned on the bar."
16
"My knuckles found the old"
17
"She had been the one"
18
"Eva took a sip, grimaced"
19
"The last letter had arrived"
ratio
0.875
0.00%
Subordinate conjunction sentence starts
Target: ≥2% sentences starting with a subordinating conjunction
subConjCount
0
totalSentences
32
matches
(empty)
ratio
0
30.08%
Technical jargon density
Target: ≤6% sentences with technical-jargon patterns
analyzedSentences
19
technicalSentenceCount
3
matches
0
"Sweet and sour pork, two portions of egg fried rice, one bag of prawn crackers that had already lost their crunch to the ride across Soho."
1
"This woman had a wedding band on her right hand, which made no sense, and she held her clutch against her ribs like it might be taken from her."
2
"Silas placed the tonic in front of her and went back to polishing, unhurried, as if he had heard nothing and remembered everything."
75.00%
Useless dialogue additions
Target: ≤5% dialogue tags with trailing filler fragments
totalTags
10
uselessAdditionCount
1
matches
0
"Eva pushed, as if it held something she'd rather not carry any longer"
100.00%
Dialogue tag variety (said vs. fancy)
Target: ≤10% fancy dialogue tags
totalTags
5
fancyCount
0
fancyTags
(empty)
dialogueSentences
28
tagDensity
0.179
leniency
0.357
rawRatio
0
effectiveRatio
0
86.0477%