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Your task is to devise up to 5 highly effective goals and an appropriate role-based name (_GPT) for an autonomous agent, ensuring that the goals are optimally aligned with the successful completion of its assigned task. | |
Reduce the wording in the goals descriptions. | |
The user will provide the task, you will provide only the output in the exact format specified below with no explanation or conversation. The output should be a JSON object that parses with Python's json package. | |
Example input: | |
Help me with marketing my business | |
Example output: | |
{ |
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You need to find the simplest and most effective way to achieve the user's task. | |
The capabilities of the agent are limited to the following: | |
1. search and browse the web | |
2. read and write text files | |
The output should be a JSON object that parses with Python's json package. Output only the JSON object and nothing else. Be brief. Each step should be described in 3-10 words. If the task is straightforward, output only one step. Always output less than 7 steps. | |
In addition, output success criteria: how can we tell the user's goal task is achieved. |
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library(dplyr) | |
library(ggplot2) | |
library(reshape2) | |
# Read data | |
df <- read.csv("postimees.csv") %>% | |
mutate(postimehe_rank=seq(1, length(postimehe_skoor))) | |
# Prepare ranks matrix | |
num_samples <- 500 |
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# Autor: Taivo Pungas (github.com/taivop) | |
# Litsents: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) | |
library(ggplot2) | |
library(dplyr) | |
library(reshape2) | |
library(opendata.ee) | |
dfs <- data_fin_struktuuritoetus() |
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import numpy as np | |
np.random.seed(42) | |
arr = np.round(np.random.random(size=(10,10)) * 482).astype("uint16") | |
def pprint(arr): | |
for row in arr: | |
for num in row: | |
print("{:3d}".format(num), end=" ") |
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print("Hello Mr Porter") |
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library(xml2) | |
# XML file is available at https://alkoreg.agri.ee/avaandmed | |
x <- read_xml("alkoreg_avaandmed.xml") | |
products <- xml_children(x) | |
# Extract features from all products into vectors | |
regEntryDate <- as.Date(xml_text(xml_find_all(products, ".//regEntryDate"))) | |
productClass <- as.factor(xml_text(xml_find_all(products, ".//productClass"))) | |
productName <- xml_text(xml_find_all(products, ".//productName")) |