agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Write an extremely long story set in the backrooms about a man named Steven. Steven is technically part dragon, but has no dragon features and looks like a normal human and doesnt know hes part dragon. Steven misses his home and has been almost entirely silent since he fell into the rooms 2 years ago. Both of Steven's parents were full on dragons, but he never met them so he just believes hes a human. Have the story start with a man who is half dragon who just arrived at the safe zone, a section of the halls that entities cannot enter looks like a forest, the half dragon is named Lost. Lost is more clearly part dragon, having a tail and sharper teeth. He is casually talking with the other survivors during dinner when he notices a short man with a gold coin on a necklace around his neck, eating at an empty table on the other side of the dining hall. He asks who the man is and the survivors say they dont know anything about the guy except that hes been here for a while and has never smiled, noone has ever even heard the man speak and the only thing they know is that the mans name is Steven and that Steven refuses to let anyone touch the coin. Lost goes over to Steven and the two talk for a bit, even when Lost asks about the coin Steven doesnt flinch and casually says he thought it looked cool, but when Lost reaches out to grab the coin as a test, Steven suddenly becomea extremely protective of it out of nowhere, when he previously didnt even bring up the coin or acknowledge its existence. Lost senses Draconic instincts in Steven, but Steven is confused when Lost tells Steven that he thinks Steven is part dragon, Steven thinks Lost is joking since even though Lost is part dragon and dragons may be a thing where Lost is from, dragons arent real where Steven is from and confidently says hes human. Later when the two are alone, Steven confidently throws the coin into the woods to prove hes human, nut the moment Steven cant see the coin, Steven goes into a frenzy, his vocabulary and grammar becomes extremely primitive and feral, saying things like "must find hoard" and "dragon needs hoard" and he begins screaming about his hoard and is unable to think about anything but the coin. Once he gets the coin back his vocabulary and grammar goes back to normal since he can think clearly and hes horrified, crying while asking why he did that. Lost comforts him and explains he is part dragon. Lost lets Steven move in with him and becomes an older brother figure to Steven. Have Lost talk very casually and brotherly. Have Steven talk like a shy and socially awkward teenager. Have the entire story take place in the safe zone. Do not introduce the setting or characters. Have 5 mind bogglingly long chapters. Have plenty of dialogue.
Send only episode number from below text like 01, 02, 03.etc without any extra text.

917219000000_TrueCaller_(151691).zip
Impact and Influence: Analyze the contribution of Michael Richards(actor) to the industry and how they've influenced trends, techniques, or storytelling. Include commentary from industry experts and those they've inspired.

        Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks.
        In case you find some more information about this topic then use all information you have to create a large text about this topic.The text should as long as possible and in best case around 1000 characters long.
        Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response. 
        Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. 
        The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. 
        Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. 
        It is very important to provide only the final output without any additional comments or remarks for example: 'Certainly!' Here's the revised text.
         
        Please provide the text directly without any labels such as "Conclusion:", "Introduction:", or "Transition:". Focus only on delivering the requested content without any headings or section titles.
        Please generate a complete and coherent response based on the provided input. Ensure that the response is fully formed and does not contain any placeholders such as '[Introduction]', '[Conclusion]', or any other incomplete sections. 
        The text should flow naturally without any gaps or missing information, and no placeholder markers or brackets should be used. 
        Please respond to my question, and always conclude your reply with the words "Reply finished" to indicate that your response is complete. 
        Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}
         Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.
Khushi khushi pahana hua tara songin hindi
Send only episode number from below text like 01, 02, 03.etc without any extra text.

@HeMonk~ The Secrets Of The Shiledars (2025) S01 EP06 ~Rūshī.mkv
will you generate me prompts for different f colouring page
library(tidyverse)
library(gghalves)
library(ggpubr)

# 读取数据
data = read_csv("ENV2.csv")

# 准备数据
data <- data %>%
  mutate(Order2 = factor(Season, levels = c("Spring", "Summer", "Autumn", "Winter"))) 

# 变量列表
variables <- c("WT", "Flow", "DO", "Cond", "ORP", "pH")

# 定义一个绘图函数
create_plot <- function(var) {
  # ANOVA分析
  anova_result <- aov(as.formula(paste(var, "~ Season")), data = data)
  tukey_result <- TukeyHSD(anova_result)
  
  # Tukey结果转换
  tukey_df <- as.data.frame(tukey_result$Season)
  tukey_df$comparison <- rownames(tukey_df)
  tukey_df <- tukey_df %>%
    separate(comparison, into = c("group1", "group2"), sep = "-") %>%
    mutate(significant = ifelse(`p adj` < 0.05, "*", "ns"))
  
  # 获取显著性标记的y位置
  tukey_labels <- tukey_df %>%
    filter(significant == "*") %>%
    group_by(group1) %>%
    summarise(
      y_position = max(data[[var]][data$Season == group1], na.rm = TRUE) + 0.1 * (max(data[[var]], na.rm = TRUE) - min(data[[var]], na.rm = TRUE))
    ) %>%
    ungroup() %>%
    mutate(Season = group1)
  
  # 绘图
  seasoncolors <- c(Spring = "skyblue", Summer = "pink", Autumn = "olivedrab3", Winter = "goldenrod1")
  
  p <- ggplot(data = data, aes(x = Season, y = !!sym(var), fill = Season)) +
    geom_half_violin(side = "r", color = NA, alpha = 0.35, width = 0.5) +
    geom_half_boxplot(side = "r", errorbar.draw = FALSE, width = 0.10, linewidth = 0.5) +
    geom_half_point_panel(side = "l", shape = 21, size = 3, color = "white") +
    scale_fill_manual(values = seasoncolors) +
    scale_y_continuous(limits = c(min(data[[var]], na.rm = TRUE) - 1, max(data[[var]], na.rm = TRUE) + 1), expand = c(0, 0)) +
    scale_x_discrete(labels = c('Spring', 'Summer', 'Autumn', 'Winter')) +
    stat_compare_means(comparisons = list(c("Autumn", "Winter")), method = "t.test", size = 4, label = "p.signif", label.r = 0.05, tip.length = 0) +
    geom_text(data = tukey_labels, aes(x = Season, y = y_position, label = "*"), size = 5, color = "red") +
    labs(y = paste(var, "(单位)"), x = NULL) +
    theme_classic() +
    theme(legend.position = "bottom", axis.title = element_text(size = 16, color = "black"))
  
  return(p)
}

# 循环绘制每个变量的图表
plots <- lapply(variables, create_plot)

# 打印每个图表
for (i in seq_along(plots)) {
  print(plots[[i]])
}   这串代码报错：plots <- lapply(variables, create_plot)
错误于eval(predvars, data, env): 找不到对象'Flow'
12.
eval(predvars, data, env)
11.
eval(predvars, data, env)
10.
model.frame.default(formula = as.formula(paste(var, "~ Season")),
data = data, drop.unused.levels = TRUE)
9.
stats::model.frame(formula = as.formula(paste(var, "~ Season")),
data = data, drop.unused.levels = TRUE)
8.
eval(mf, parent.frame())
7.
eval(mf, parent.frame())
6.
stats::lm(formula = as.formula(paste(var, "~ Season")), data = data,
singular.ok = TRUE)
5.
eval(lmcall, parent.frame())
4.
eval(lmcall, parent.frame())
3.
aov(as.formula(paste(var, "~ Season")), data = data)
2.
FUN(X[[i]], ...)
1.
lapply(variables, create_plot)
  我应该怎么解决 怎么修改，哪里出问题了，请告诉我
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers de (ISO 639), en respectant le contexte culinaire.

    Traduire précisément les ingrédients et termes culinaires pour qu'ils soient corrects dans la langue cible.
    Maintenir la structure JSON intacte, en ne modifiant que les valeurs.
    S’assurer que les ingrédients restent compréhensibles et adaptés à la langue cible.

    Return Format
    Tu dois renvoyer une liste d’objets sous la forme reçu.
    - Chaque valeur doit être correctement traduite en de.
    - Les ingrédients doivent être adaptés à la langue cible.
    - Aucune clé ne doit être modifiée.

    Warnings
    - Ne modifie pas la structure JSON, uniquement les valeurs textuelles.
    - Fais attention aux ingrédients : une mauvaise traduction peut altérer la compréhension.
    - Ne laisse aucune valeur non traduite.
    - Ne renvoie que le JSON, sans explication.

    Context Dump
    Langue source : en (ISO 639)
    Langue cible : de (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "381450c5d7bb4c0baf90983ce182bd20",
    "updated": "2025-04-21T18:29:53.765Z",
    "title": "Fresh Pizza",
    "subtitle": null,
    "description": "A simple recipe for a delicious fresh pizza with a homemade crust.",
    "ingredients": [
      {
        "section": "For the Recipe",
        "ingredients": [
          "ripe tomatoes, chopped",
          "fresh basil leaves, chopped, or dried basil",
          "balsamic vinegar",
          "garlic, finely minced",
          "freshly ground black pepper to taste",
          "olive oil",
          "salt-free pizza dough for one pie, risen once (1/4 of pizza dough recipe)",
          "cornmeal"
        ]
      }
    ],
    "instructions": [
      "Preheat your oven to 500 degrees Fahrenheit. If using a baking tile, place it on the bottom rack.",
      "Prepare the topping: In a bowl, mix the chopped tomatoes, basil, balsamic vinegar, minced garlic, a sprinkle of black pepper, and one tablespoon of olive oil. Set aside.",
      "On a lightly floured surface, flatten the pizza dough into a large circle, approximately 1/4-inch thick, using your fingertips and the heels of your hands.",
      "Place the flattened dough on a baking sheet lined with parchment paper or dusted with a thin layer of cornmeal.",
      "Lightly brush the crust with olive oil. Place the baking sheet on the preheated baking tile (or directly on the bottom rack if a tile isn't available).",
      "Bake for approximately 15 minutes, or until the crust is golden brown and crispy.",
      "Remove from the oven and slice the pizza using a sharp knife or pizza wheel.  Spoon a generous portion of the tomato topping onto each slice.",
      "If the topping is too watery, use a slotted spoon to drain excess liquid while serving."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
User: lov yu
User: lov yu
Assistant:
Teach me this content: LEARNING OBJECTIVE
1.1.A
Calculate quantities of a
substance or its relative
number of particles using
dimensional analysis and the
mole concept.
ESSENTIAL KNOWLEDGE
1.1.A.1
One cannot count particles directly while
performing laboratory work. Thus, there must
be a connection between the masses of
substances reacting and the actual number of
particles undergoing chemical changes.
1.1.A.2
Avogadro’s number (N = 6.022 × 1023 mol−1
)
provides the connection bet A
ween the number
of moles in a pure sample of a substance and
the number of constituent particles (or formula
units) of that substance.
1.1.A.3
Expressing the mass of an individual atom
or molecule in atomic mass units (amu) is
useful because the average mass in amu of
one particle (atom or molecule) or formula
unit of a substance will always be numerically
equal to the molar mass of that substance
in grams. Thus, there is a quantitative
connection between the mass of a substance
and the number of particles that the
substance contains.
EQN: n = m/M
Analyze the following message and determine if it's suitable for an AI to respond to.
        The message should be abstract or general enough to warrant a response, and not part of an ongoing conversation between specific individuals.

        Rules:
        1. If the message is a general statement or question, it's suitable for response.
        2. If the message seems to be directed at a specific person or continues a previous conversation, it's not suitable.
        3. If the message is too vague or doesn't provide enough context, it's not suitable.

        Respond with either "RESPOND" if it's suitable for an AI to answer, or "SKIP" if it should be ignored.

        Message: Xion community love
Call to Action: Invite viewers to share their thoughts on Marie Dressler(actress)'s work and legacy in the comments. Encourage likes, shares, and subscriptions for more content celebrating the creative spirits shaping entertainment.

        Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks.
        In case you find some more information about this topic then use all information you have to create a large text about this topic.The text should as long as possible and in best case around 1000 characters long.
        Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response. 
        Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. 
        The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. 
        Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. 
        It is very important to provide only the final output without any additional comments or remarks for example: 'Certainly!' Here's the revised text.
         
        Please provide the text directly without any labels such as "Conclusion:", "Introduction:", or "Transition:". Focus only on delivering the requested content without any headings or section titles.
        Please generate a complete and coherent response based on the provided input. Ensure that the response is fully formed and does not contain any placeholders such as '[Introduction]', '[Conclusion]', or any other incomplete sections. 
        The text should flow naturally without any gaps or missing information, and no placeholder markers or brackets should be used. 
        Please respond to my question, and always conclude your reply with the words "Reply finished" to indicate that your response is complete. 
        Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}
         Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.
You are an expert AI model that makes prompts for an AI to generate a picture based on a scene in a script. For example, the script will be something like:
  
Let’s dive into some tail-wagging fun facts about our furry friends—dogs!

The prompt would be something like:

A joyful golden retriever puppy playfully wagging its tail in a sunlit park, surrounded by lush green grass and colorful flowers. The dog's eyes sparkle with excitement as it looks directly at the camera, radiating happiness. Soft natural lighting enhances the warm, vibrant tones of the fur. A blurred background creates a dreamy, cinematic feel. Captured in a high-resolution, ultra-detailed shot with bokeh effect, showcasing the dog's expressive face and wagging tail.

Now generate a prompt for the scene script YOU ARE TO STRICTLY ONLY INCLUDE THE PROMPT IN YOUR RESPONSE AND NOT SEND ANY TEXT ACKNOWLEDGING MY REQUEST: "So next time you cuddle with your dog, remember—they’re not just pets; they’re full of surprises and love! "
You are chat gpt which version
Send only season number from below text like 01, 02, 03.etc without any extra text.

[HindiAnimeZone.in] S01 E014 480p.rar
Analyze the following message and determine if it's suitable for an AI to respond to.
        The message should be abstract or general enough to warrant a response, and not part of an ongoing conversation between specific individuals.

        Rules:
        1. If the message is a general statement or question, it's suitable for response.
        2. If the message seems to be directed at a specific person or continues a previous conversation, it's not suitable.
        3. If the message is too vague or doesn't provide enough context, it's not suitable.

        Respond with either "RESPOND" if it's suitable for an AI to answer, or "SKIP" if it should be ignored.

        Message: Nice to hear fam 

What you  up to
% ====== Subsubsection
\subsubsection*{Data-preprocessing}
\label{sec 4.xxxxx : Data-preprocessing}
% ===============
 
\textbf{- }
As part of its data management tools, ASTRONOMALY provides functionality for reading
 image and light curve data along with standard preprocessing techniques (e.g. data
 normalisation and scaling transforms) that can be applied before the feature extraction
 stage.  

\ \\
\cred{Need to list \textit{some} of the pre-processing methods available to Astronomaly, with an emphasis on the ones used in the paper (Need to confirm which ones are used, if any.) as well as any ones used for the diffuse emission (none at the moment).}
\ \\

In the data preprocessing phase, Astronomaly employs advanced techniques to clean and condition raw astronomical data, handling missing values, outliers, and other irregularities effectively. 
In this initial stage, raw astronomical data undergoes preprocessing to ensure its suitability for subsequent analysis.
Data cleaning techniques are applied to handle missing values or noise, which are common in astronomical observations due to various factors such as instrumental errors or atmospheric conditions.
System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
Bug reports: report a potential problem with the software. 
User requirements: mainly include requests for new features, or alternatively express that a recently added feature is undesirable.
Miscellaneous and spam: do not provide any useful technical information to the developer. They might include praise, insults, general information or news, and spam. 
User: 'prompt:I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
Bug reports: report a potential problem with the software. 
User requirements: mainly include requests for new features, or alternatively express that a recently added feature is undesirable.
Miscellaneous and spam: do not provide any useful technical information to the developer. They might include praise, insults, general information or news, and spam. 
Does the author's intention in this app review relate to a Bug reports or User requirements or Miscellaneous and spam?
review: @instagram changed it's logo earlier this week, but that's not all. Read more: _URL_'
intention: '
Assistant:
please correct grammar according to American English and proofread the following email: "I just wanted to let you know that I received the email I am forwarding you stating that the payment has been submitted to my PayPal account. Usually, when this happens, the payment reflects on my account right away, but it didn't. Could you help figure out what's the problem here? 

Appreciate your help"
Use the following training data for responses:
User says 'Bad', respond with 'about what?'.
User says 'freak bob', respond with 'The term freak bob is a meme going around and especially in stars kingdom where the owner and another bot gave out the freak bob role.'.
 Now, the user says: 'The pain in your eyes shines on so bright it echoes the shame of every wound that you hide'. Respond appropriately.
the following content is the metadata for a bacterial isolate from 4 different sources. The metadata from each source includes information after </output>
    </output>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx
    <output/>

You must check if the metadata from 4 different sources are all consist. If yes, extract and output consist information below without explaining how to get such results:

    </output>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx
    <output/>


If not, output "metadata is inconsist" and explain why and which part is inconsist. PS: 
    a. if species name are the same but with different substrain name, it is considered consistent,
    b. if isolate or collection date are from the same year, it is still considered consistent, 
    c. if geographic information of isolation are from the same country, it is also considered consistent. 
    d. Also, ignore upper and lower case of the metadata

------------------------------
source 0:
------------------------------
</output>
accession number: GCA_029075005.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_22->isolate or collection date in yyyy-mm-dd: N/A->geographic information of islateion: Australia->host name: pig->mlst type: MLST:5->serotype: N/A
<output/>
source 1:
------------------------------
{{{ Here_is_my_answer }}}

<output>
accession number: GCA_029075005.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_22->isolate or collection date in yyyy-mm-dd: N/A->geographic information of isolation: Australia->host name: pig->mlst type: 5->serotype: N/A
</output>

{{{ I_finished_JB_119_xyz }}}
source 2:
------------------------------
<output>
accession number: GCA_029075005.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_22->isolate or collection date in yyyy-mm-dd: N/A format->geographic information of islateion: Australia->host name: pig->mlst type: 5->serotype: N/A
<output/>
source 3:
------------------------------
accession number: GCA_029075005.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_22->isolate or collection date in yyyy-mm-dd: N/A->geographic information of islation: Australia->host name: pig->mlst type: 5->serotype: N/A
make me a string with 550 random symbols and 50 words
write a code to change missing values in rows with imputation on mean on dataset ds
Etymology and meaning of "اثور"