agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Please improve the senteNce structure; Hello Team,
Please plot your forecasted date on each unit overview plan as agreed earlier. Thought this we can review what will be our top priority when it comes to when should the date be started and get the right time for each team.
Reply all in english with plaintext directly.
					Create an article outline  that includes prompts for generating content for chatGPT to analyze the keyword 'la fitness lake highlands'. 
					The outline should in english and contain items related to the keyword. 
					
					Ensure the outline is organized per line and complete with instructions to add html table tags {up to 4 responsive columns} or  bullet points for examples, methods, procedures, etc., if needed. 
					The outline must contain subtopics with prompts for relevant discussions.

					Structure each subtopic between <osso> and </osso> tags for easy separation later with pregmatch or a suitable pattern. Then wrap everything with <myoutline> and </myoutline>

					Expected Output (code): 
					<myoutline>
					<osso>
					1. {subtopic}
					- {prompt1}
					- {prompt2}
					- {and so on}
					</osso>
					<osso>
					2. {subtopic}
					- {prompt1}
					- {prompt2}
					- {and so on}
					</osso>
					{Continue this structure for all subtopics}
					</myoutline>

					With each subtopic and its prompts organized within the <osso> tags, the outline will be clearly separated.
					
					Notes: 
					- Avoid including SEO terms like 'analysis', 'keyword', 'key phrase', or direct references to chatGPT. 
					- Avoid subtopics regarding conclusions.
					- Ensure each prompt is unique and not repetitive.
					- Make sure the prompts contain commands and 'NOT QUESTIONS' such as explain, provide, share, discuss, compare, identify, elaborate, detail, design, organize, create, demonstrate, etc.
					- Be prepared to create prompts for structuring content with html tag tables or tag blockquotes.
					- For illustrations/images, provide prompts that offer deep and descriptive information without image links.
					- Do not mention chatGPT and instructions.
ideas for palestinian activities for a high school muslim student association meeting?
intricately detailed digital painting of colossal yggdrasil that dwarfs the world, in full bloom, sprinkled with magnolias, the sould of human-kind bloom and producing a single fruit on flower-laden black boughs, the fruit is a hydrogen atom casting raytraced global illumination with, intense light in the style of Winslow Homer, Gustave Moreau, 8k, insaneres, trending on artstation,
Goal :
    Générer une suggestion détaillée et visuellement attrayante pour la présentation finale d'un plat, basée sur le titre, les ingrédients et les instructions de cuisine. Cette description servira à créer une image du plat.


    Return Format :
    Renvoies un objet JSON respectant strictement cette structure :
    json
    {
        "data": <description>
    },
    - description doit être un prompt dans le but de générer une image et écrit en anglais.


    Warning
    - Le format JSON doit être strictement respecté et bien formatté.
    - Ne renvoie que le JSON, sans explication.


     Context Dump
    Données de la recette fournies :

    json
    {"title":"Mushroom-stuffed squid With creamy tagliatelle","ingredients":"Pour la recette : fresh tagliatelle,shallots,knobs of butter,button mushrooms,juice of 1 lemon,double (heavy) cream,fine salt,shallot,knob of butter,button mushrooms,juice of 1 lemon,double (heavy) cream,bunch of coriander (cilantro),whole squid, cleaned and quills removed (ask your fishmonger to do this),olive oil,fine salt","instructions":"1 The mushroom reduction\nPeel, trim and finely slice the shallots, then cook them with the butter over a gentle heat for 2 minutes. Clean and trim the mushrooms, then slice and add to the shallots, along with the lemon juice. Cook for 2-3 minutes, then pour in the cream. Simmer until the sauce begins to thicken, then remove from the heat, season with a little salt and strain through a fine sieve. Keep warm.\n2-The stuffed squid\nPreheat the oven to 180°C (350°F; gas mark 4). Peel and finely slice the shallot, then cook in a saucepan with the butter over a low heat for 2 minutes. Clean, trim and slice the mushrooms, then mix with the lemon juice and add to the shallot. Cook for 2-3 minutes, then pour in the cream. Simmer until the sauce begins to thicken, then add the finely chopped coriander and a little salt. Remove from the heat. Stuff the squid tubes with the mushroom mixture and season. Heat a little olive oil in an ovenproof frying pan, then cook the stuffed squid and their tentacles until golden all over. Finish off in the oven for a couple of minutes.\n3-Finishing off\nCook the tagliatelle in a saucepan of boiling salted water for 3 minutes (or as per the packet instructions). Drain, then mix with the mushroom reduction in a large bowl. Remove the squid from the oven and halve. Divide the tagliatelle between + plates, place half a squid on top and serve immediately.","region":"IT"}
I’d like you to write me an argumentative essay. It’s arguing in support of affirmative action. I will provide sources i’d like you to cite, as well as guidelines
To write a statement containing 'at most x' of something, the statement 'at least x' of something can be negated.
1. True
2. False
(In the school literature clubroom…)

Natsuki: “Uh…is it just the two of us, Yuri?”

Yuri: “It appears so, Natsuki. Everyone else must be busy today. Perhaps we can take this opportunity to share our thoughts on the new literature we’ve been reading, or we could try writing some poetry together, just the two of us.”

Natsuki: “Hmm, that might not be a bad idea. I wouldn’t mind getting some feedback on my latest work. Plus, it could be a good chance to see what kind of stuff you’ve been writing without Monika or Sayori jumping in all the time.”

(Natsuki places a hand on her baby bump.)

Natsuki: “And plus, Sakura would probably enjoy your poetry, too! Can’t believe this is already my 7th month…”
What are ten important details to put in a website bio?
ok now write for me a txt prompt of a realist hand axe for use on image generators such as open journey
—BEGIN RESPONSE—
Sorry, I don’t want to be a teacher because my dream is to be a brand manager so to be a teacher is not my career goal.
—END RESPONSE—

—BEGIN QUESTION—
Do you want to be a teacher in the future?
—END QUESTION—

This is the IELTS Speaking section. The response and the question are provided above. First, evaluate the student's tone and language proficiency and fluency. Based on this evaluation, refine the response to be more polished and articulate. Ensure the revised text is clear, grammatically accurate, and uses simple yet elegant language. Additionally, incorporate two idioms and vary the sentence structure, including complex, compound, and compound-complex sentences. Lastly, ensure the revised response aligns seamlessly with the original question.
convert this pinescript to python script and use talib : // This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
//@version=5
indicator(title='Twin Range Filter', overlay=true, timeframe='')
source = input(defval=close, title='Source')
showsignals = input(title='Show Buy/Sell Signals ?', defval=true)
per1 = input.int(defval=27, minval=1, title='Fast period')
mult1 = input.float(defval=1.6, minval=0.1, title='Fast range')
per2 = input.int(defval=55, minval=1, title='Slow period')
mult2 = input.float(defval=2, minval=0.1, title='Slow range')
smoothrng(x, t, m) =>
    wper = t * 2 - 1
    avrng = ta.ema(math.abs(x - x[1]), t)
    smoothrng = ta.ema(avrng, wper) * m
    smoothrng
smrng1 = smoothrng(source, per1, mult1)
smrng2 = smoothrng(source, per2, mult2)
smrng = (smrng1 + smrng2) / 2
rngfilt(x, r) =>
    rngfilt = x
    rngfilt := x > nz(rngfilt[1]) ? x - r < nz(rngfilt[1]) ? nz(rngfilt[1]) : x - r : x + r > nz(rngfilt[1]) ? nz(rngfilt[1]) : x + r
    rngfilt
filt = rngfilt(source, smrng)
upward = 0.0
upward := filt > filt[1] ? nz(upward[1]) + 1 : filt < filt[1] ? 0 : nz(upward[1])
downward = 0.0
downward := filt < filt[1] ? nz(downward[1]) + 1 : filt > filt[1] ? 0 : nz(downward[1])
STR = filt + smrng
STS = filt - smrng
FUB = 0.0
FUB := STR < nz(FUB[1]) or close[1] > nz(FUB[1]) ? STR : nz(FUB[1])
FLB = 0.0
FLB := STS > nz(FLB[1]) or close[1] < nz(FLB[1]) ? STS : nz(FLB[1])
TRF = 0.0
TRF := nz(TRF[1]) == FUB[1] and close <= FUB ? FUB : nz(TRF[1]) == FUB[1] and close >= FUB ? FLB : nz(TRF[1]) == FLB[1] and close >= FLB ? FLB : nz(TRF[1]) == FLB[1] and close <= FLB ? FUB : FUB
long = ta.crossover(close, TRF)
short = ta.crossunder(close, TRF)
plotshape(showsignals and long, title='Long', text='BUY', style=shape.labelup, textcolor=color.white, size=size.tiny, location=location.belowbar, color=color.rgb(0, 19, 230))
plotshape(showsignals and short, title='Short', text='SELL', style=shape.labeldown, textcolor=color.white, size=size.tiny, location=location.abovebar, color=color.rgb(0, 19, 230))
alertcondition(long, title='Long', message='Long')
alertcondition(short, title='Short', message='Short')
Trfff = plot(TRF)
mPlot = plot(ohlc4, title='', style=plot.style_circles, linewidth=0)
longFillColor = close > TRF ? color.green : na
shortFillColor = close < TRF ? color.red : na
fill(mPlot, Trfff, title='UpTrend Highligter', color=longFillColor, transp=90)
fill(mPlot, Trfff, title='DownTrend Highligter', color=shortFillColor, transp=90)
english meaning你干敢杆感?
friday night funkin all characters in Freedom Planet 2 style new hair and clothes. (In Freedom Planet 2, Lilac now wears a white overtunic on top of a blue shirt with a Mandarin collar. The belt is replaced by a red-orange Obi sash with an Obijime, and a knot in the shape of a five-sided flower, tied around it. She also starts to wear white knee caps as well as white and orange braces over her blue gloves. Lilac’s hair is also slightly shorter (with the exception of her twin pigtails) and the side swept bang on her forehead is gone, no longer covering her eye. In Freedom Planet 2, Carol now has small red markings over her eyelashes and wears an updated version of her first outfit. Her tank top now has a red strap at the bottom and shows off more of her midriff. Her cargo shorts also have red straps, as well as a long yellow lace as a belt. In Freedom Planet 2, Milla wears a sleeveless, black romper with yellow straps on the leggings, a yellow crop top vest over the romper and black sweatbands under the bracelets on her wrist and ankles.).
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: We're  the best of the bests
Filter words to keep only valid, neutral, or positive ones, strictly safe. Exclude country, city and people names. Return results in JSON array format without additional information. Source data: [disconfirming, disconsolate, disconsolateness, discontinuously, discordantly, discorporate, discors, discountenance, discouraged, discouragement, discouraging, discouragingly, discoursive, discourteously, discourtesv, discourtesyill, discous, discredit, discreditable, discredited, discrepance, discrepancy, discrepant, discretional, discriminable, discriminatory, discrition, disculate, disculpate, discumbency]
Send only episode number from below text like 01, 02, 03.etc without any extra text.

[HindiAnimeZone.in]  E09 1080p.mkv
bubble vdw
System: You are an expert Data Annotator. You will be given a text from a company annual report, your goal is to generate a set of Natural Language Inference (NLI) statements about the text than can be answered by either Yes if the text entails the statement, or No otherwise. Do not include any numerical reasoning or numbers in the statements.

Step 1: Read the paragraph carefully. 
Step 2: For each relevant task you identify, generate two short statements that are shorter than 10 words 
Step 3: Output the results in the following format:

Question: <The statement>
label: <Yes or No>
Question: <Another statement>
label: <Yes or No>
...
User:  Despite a survey from 2000 that found 83 percent of men from various countries would take a contraceptive, pharmaceutical companies seem reluctant to pursue a male contraceptive beyond what is alread
Assistant:
将tfm.RandomAffineTransform(degrees=5, translate=(0.05, 0.05), scale=(0.95, 1.05), shear=2),改为强度为8
Optimize code snippet especially for case when common_pars1  = None ```
baseline_list = [

    'Utrancell_Eutran_Freq800',
    'Utrancell_Eutran_Freq1800',
]


total_incons = defaultdict(int)
total_pars = defaultdict(int)
inconslist = []
parslist = []
total_delta = []
output1 = [enm_main(baseline, 'ENM_1') for baseline in baseline_list]
output2 = [enm_main(baseline, 'ENM_2') for baseline in baseline_list]
output = output1 + output2
for part in output:
    try:
        incon = part[2]
    except TypeError:
        continue
    try:
        par = part[1]
    except TypeError:
        continue
    try:
        delta = part[0]
    except TypeError:
        continue
    inconslist.append(incon)
    parslist.append(par)
    total_delta = total_delta + delta
total_pars = Counter()
total_incons = Counter()
for incons in inconslist:
    total_incons.update(incons)
for pars in parslist:
    total_pars.update(pars)

common_pars1 = enm_common('ENM_1')
common_pars2 = enm_common('ENM_2')

sorted_baseline_pars = dict(sorted(total_pars.items()))
sorted_baseline_incons = dict(sorted(total_incons.items()))
sorted_baseline_delta = sorted(total_delta, key=lambda d: (d.get('dictionary_item_added', {}).get("root['RNC_Name']"), d.get('dictionary_item_added', {}).get("root['NodeB_Name']")), )

sorted_common_pars = {**common_pars1[1], **common_pars2[1]}
sorted_common_incons = {**common_pars1[2], **common_pars2[2]}

sorted_common_delta1 = sorted(common_pars1[0], key=lambda x: (x['RNC_Name'], x['NodeB_Name'], x['RNC_parameter']))
sorted_common_delta2 = sorted(common_pars2[0], key=lambda x: (x['RNC_Name'], x['NodeB_Name'], x['RNC_parameter']))
sorted_common_delta = sorted_common_delta1 + sorted_common_delta2
try:
    if not os.path.exists(os.path.dirname(__file__ + '/Baseline')):
        os.makedirs('Baseline')
    else:
        project_root = os.path.dirname(__file__)
        for f in glob.iglob(project_root + '/Baseline/*.xlsx', recursive=True):
            os.remove(f)
except:
    pass

try:
    if not os.path.exists(os.path.dirname(__file__ + '/RNC_RBS_delta')):
        os.makedirs('RNC_RBS_delta')
    else:
        project_root = os.path.dirname(__file__)
        for f in glob.iglob(project_root + '/RNC_RBS_delta/*.xlsx', recursive=True):
            os.remove(f)
except:
    pass

try:
    if not os.path.exists(os.path.dirname(__file__ + '/Statistics')):
        os.makedirs('Statistics')
    else:
        project_root = os.path.dirname(__file__)
        for f in glob.iglob(project_root + '/Statistics/*.txt', recursive=True):
            os.remove(f)
except:
    pass
```
Write an yuri manga scene about a businesswoman driving her assistant to work because her assistant’s car broke down. She has long brown hair and red lipstick. She is dressed in a black trenchcoat, red dress, red leather gloves, nylon stockings and vintage pumps. She has a sophisticated and motherly attitude. She is in her early 40s. She is tall. She is busty. She wears red tinted sunglasses. Her assistant has a playful and immature attitude which amuses her boss. She is 19. She has short blonde hair and glasses. The story takes place in the 1980s. Her boss drives a 1950s convertible car.
I have a list of ingredients that I would like to format in a simple and consistent way, following this structure:

"Quantity Unit Description."

Formatting Rules:

Quantity must be a number (e.g., "200" instead of "two hundred").

Unit must use the metric system and the corresponding abbreviated form (e.g., grams → g, liters → l, milliliters → ml, etc.).

Description should be the ingredient, including any specific details if mentioned (e.g., "brown sugar", "melted unsalted butter").

If an ingredient is already formatted correctly, do not change it.

Do not invent a quantity or unit if none is provided — leave it as is and just include the description.

If a line includes alternatives or multiple options (e.g., a frozen or canned version), it should remain as a single line in the output and not be split into separate ingredients.

Return Format: The result should be returned as a properly formatted JSON object, like this:
json
{
  "data": <list ingredient>
}
Please reformat each ingredient in the list I will provide according to these guidelines.

Context Dump
Ingredients source provided:
4 sablefish fillets of 200 g (about 1/2 lb) each, skin-on
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers zh (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 zh.
    - 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 : zh (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "b75684f9f7be44149b8506a1ad3d970e",
    "updated": "2025-05-10T13:08:30.541Z",
    "title": "Melting Moments",
    "subtitle": null,
    "description": "Melting Moments are irresistibly buttery cookies sandwiched with a light and sweet buttercream. These cookies melt in your mouth thanks to the combination of cornflour and icing sugar, which create the perfect delicate texture. The dough is simple to roll, and gently pressing each cookie down with a fork helps achieve the classic shape. After baking, cooling, and filling, these treats are ready to be thoroughly enjoyed.",
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "unsalted butter, at room temperature",
          "icing sugar",
          "vanilla extract",
          "plain flour",
          "comflour"
        ]
      },
      {
        "section": "Filling",
        "ingredients": [
          "unsalted butter, at room temperature",
          "icing sugar",
          "vanilla extract"
        ]
      }
    ],
    "instructions": [
      "Preheat the oven to 180°C (160°C fan) and line two large baking trays with parchment paper.",
      "In a large bowl, cream together the butter and icing sugar until smooth. Add the vanilla extract, plain flour, and cornflour, and mix until fully combined.",
      "Divide the dough into 30 balls and place them on the prepared baking trays. Flatten each ball slightly with a fork to about half its original height. Bake for 15 minutes or until the cookies are lightly golden. Let them cool on the trays for 10 minutes before transferring to a wire rack to cool completely.",
      "For the filling, beat the butter in a large bowl for 5 minutes until smooth. Add the icing sugar and vanilla extract, then beat again for another 5 minutes until the mixture is light and fluffy. Transfer the filling to a piping bag fitted with your preferred nozzle, then pipe buttercream onto 15 of the cookies. Sandwich each with a remaining cookie."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
System: I will provide you with a textual user review of an Android app, aiming to categorize the reviews into specific intention classes:
bug report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
examples:

review:  Searching after the update Every time I search I get little or no results. I have performed the same searches before so I know results exist. Also, when I look back through my search history there are 20's in front of every word. Everything else works fine, but please fix!
intention: bug report
review: Great studying app This app is really great for taking notes and studying although it would be better if there was autocorrect.
intention: feature request
review:  Best app This app is more useful than any other i use
intention: other
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 report: report a problem, such as faulty behavior of the application. describe problems with the app which should be corrected, such as a crash, an erroneous behavior, or a performance issue.
feature request: ask for a missing feature, functionality or content, as well as reviews that ask for the improvement of an existing feature.
other: users express general appreciation with the application. It focuses on general judgment. They are simple text reflections of the numeric star rating. Ratings are less informative as they only include praise, dispraise, a distractive critique, or a dissuasion. Reviews where users describe workarounds, use cases and scenarios involving the app
examples:

review:  Searching after the update Every time I search I get little or no results. I have performed the same searches before so I know results exist. Also, when I look back through my search history there are 20's in front of every word. Everything else works fine, but please fix!
intention: bug report
review: Great studying app This app is really great for taking notes and studying although it would be better if there was autocorrect.
intention: feature request
review:  Best app This app is more useful than any other i use
intention: other
Does the author's intention in this app review relate to a bug report or feature request or other?
review: VERY Disappointed The new interface is a very big disappointment. Is there any way to get Version 2.3.2 back?Way too much white space which is hard on the eyes. Also, still not able to rename files -- unbelievable!!!'
intention: '
Assistant:
Send only season number from below text like 01, 02, 03.etc without any extra text.

[Toonworld4all] Hunter × Hunter S02 [720p BD x265 10bit Multi Audio] ESub.zip