This project serves as a wrapper for the Twitter premium and enterprise search APIs, providing a command-line utility and a Python library. Pretty docs can be seen here.
- Supports 30-day Search and Full Archive Search (not the standard Search API at this time).
- Command-line utility is pipeable to other tools (e.g.,
jq). - Automatically handles pagination of search results with specifiable limits
- Delivers a stream of data to the user for low in-memory requirements
- Handles enterprise and premium authentication methods
- Flexible usage within a python program
- Compatible with our group's Tweet Parser for rapid extraction of relevant data fields from each tweet payload
- Supports the Search Counts endpoint, which can reduce API call usage and provide rapid insights if you only need Tweet volumes and not Tweet payloads
The searchtweets library is on Pypi:
pip install searchtweetsOr you can install the development version locally via
git clone https://github.com/twitterdev/search-tweets-python
cd search-tweets-python
pip install -e .The premium and enterprise Search APIs use different authentication methods and we attempt to provide a seamless way to handle authentication for all customers. We know credentials can be tricky or annoying - please read this in its entirety.
Premium clients will require the bearer_token and endpoint
fields; Enterprise clients require username, password, and
endpoint. If you do not specify the account_type, we attempt to
discern the account type and declare a warning about this behavior.
For premium search products, we are using app-only authentication and the bearer tokens are not delivered with an expiration time. You can provide either: - your application key and secret (the library will handle bearer-token authentication) - a bearer token that you get yourself
Many developers might find providing your application key and secret more straightforward and letting this library manage your bearer token generation for you. Please see here for an overview of the premium authentication method.
We support both YAML-file based methods and environment variables for storing credentials, and provide flexible handling with sensible defaults.
For premium customers, the simplest credential file should look like this:
search_tweets_api:
account_type: premium
endpoint: <FULL_URL_OF_ENDPOINT>
consumer_key: <CONSUMER_KEY>
consumer_secret: <CONSUMER_SECRET>For enterprise customers, the simplest credential file should look like this:
search_tweets_api:
account_type: enterprise
endpoint: <FULL_URL_OF_ENDPOINT>
username: <USERNAME>
password: <PW>By default, this library expects this file at
"~/.twitter_keys.yaml", but you can pass the relevant location as
needed, either with the --credential-file flag for the command-line
app or as demonstrated below in a Python program.
Both above examples require no special command-line arguments or
in-program arguments. The credential parsing methods, unless otherwise
specified, will look for a YAML key called search_tweets_api.
For developers who have multiple endpoints and/or search products, you
can keep all credentials in the same file and specify specific keys to
use. --credential-file-key specifies this behavior in the command
line app. An example:
search_tweets_30_day_dev:
account_type: premium
endpoint: <FULL_URL_OF_ENDPOINT>
consumer_key: <KEY>
consumer_secret: <SECRET>
(optional) bearer_token: <TOKEN>
search_tweets_30_day_prod:
account_type: premium
endpoint: <FULL_URL_OF_ENDPOINT>
bearer_token: <TOKEN>
search_tweets_fullarchive_dev:
account_type: premium
endpoint: <FULL_URL_OF_ENDPOINT>
bearer_token: <TOKEN>
search_tweets_fullarchive_prod:
account_type: premium
endpoint: <FULL_URL_OF_ENDPOINT>
bearer_token: <TOKEN>If you want or need to pass credentials via environment variables, you can set the appropriate variables for your product of the following:
export SEARCHTWEETS_ENDPOINT= export SEARCHTWEETS_USERNAME= export SEARCHTWEETS_PASSWORD= export SEARCHTWEETS_BEARER_TOKEN= export SEARCHTWEETS_ACCOUNT_TYPE= export SEARCHTWEETS_CONSUMER_KEY= export SEARCHTWEETS_CONSUMER_SECRET=
The load_credentials function will attempt to find these variables
if it cannot load fields from the YAML file, and it will overwrite any
credentials from the YAML file that are present as environment
variables if they have been parsed. This behavior can be changed by
setting the load_credentials parameter env_overwrite to
False.
The following cells demonstrates credential handling in the Python library.
from searchtweets import load_credentialsload_credentials(filename="./search_tweets_creds_example.yaml",
yaml_key="search_tweets_ent_example",
env_overwrite=False){'username': '<MY_USERNAME>',
'password': '<MY_PASSWORD>',
'endpoint': '<MY_ENDPOINT>'}
load_credentials(filename="./search_tweets_creds_example.yaml",
yaml_key="search_tweets_premium_example",
env_overwrite=False){'bearer_token': '<A_VERY_LONG_MAGIC_STRING>',
'endpoint': 'https://api.twitter.com/1.1/tweets/search/30day/dev.json',
'extra_headers_dict': None}
If we set our environment variables, the program will look for them regardless of a YAML file's validity or existence.
import os
os.environ["SEARCHTWEETS_USERNAME"] = "<ENV_USERNAME>"
os.environ["SEARCHTWEETS_PASSWORD"] = "<ENV_PW>"
os.environ["SEARCHTWEETS_ENDPOINT"] = "<https://endpoint>"
load_credentials(filename="nothing_here.yaml", yaml_key="no_key_here")cannot read file nothing_here.yaml Error parsing YAML file; searching for valid environment variables
{'username': '<ENV_USERNAME>',
'password': '<ENV_PW>',
'endpoint': '<https://endpoint>'}
the flags:
--credential-file <FILENAME>--credential-file-key <KEY>--env-overwrite
are used to control credential behavior from the command-line app.
The library includes an application, search_tweets.py, that provides
rapid access to Tweets. When you use pip to install this package,
search_tweets.py is installed globally. The file is located in the
tools/ directory for those who want to run it locally.
Note that the --results-per-call flag specifies an argument to the
API ( maxResults, results returned per CALL), not as a hard max to
number of results returned from this program. The argument
--max-results defines the maximum number of results to return from a
given call. All examples assume that your credentials are set up
correctly in the default location - .twitter_keys.yaml or in
environment variables.
Stream json results to stdout without saving
search_tweets.py \
--max-results 1000 \
--results-per-call 100 \
--filter-rule "beyonce has:hashtags" \
--print-streamStream json results to stdout and save to a file
search_tweets.py \
--max-results 1000 \
--results-per-call 100 \
--filter-rule "beyonce has:hashtags" \
--filename-prefix beyonce_geo \
--print-streamSave to file without output
search_tweets.py \
--max-results 100 \
--results-per-call 100 \
--filter-rule "beyonce has:hashtags" \
--filename-prefix beyonce_geo \
--no-print-streamOne or more custom headers can be specified from the command line, using
the --extra-headers argument and a JSON-formatted string
representing a dictionary of extra headers:
search_tweets.py \
--filter-rule "beyonce has:hashtags" \
--extra-headers '{"<MY_HEADER_KEY>":"<MY_HEADER_VALUE>"}'Options can be passed via a configuration file (either ini or YAML).
Example files can be found in the tools/api_config_example.config or
./tools/api_yaml_example.yaml files, which might look like this:
[search_rules]
from_date = 2017-06-01
to_date = 2017-09-01
pt_rule = beyonce has:geo
[search_params]
results_per_call = 500
max_results = 500
[output_params]
save_file = True
filename_prefix = beyonce
results_per_file = 10000000Or this:
search_rules:
from-date: 2017-06-01
to-date: 2017-09-01 01:01
pt-rule: kanye
search_params:
results-per-call: 500
max-results: 500
output_params:
save_file: True
filename_prefix: kanye
results_per_file: 10000000Custom headers can be specified in a config file, under a specific credentials key:
search_tweets_api:
account_type: premium
endpoint: <FULL_URL_OF_ENDPOINT>
username: <USERNAME>
password: <PW>
extra_headers:
<MY_HEADER_KEY>: <MY_HEADER_VALUE>When using a config file in conjunction with the command-line utility,
you need to specify your config file via the --config-file
parameter. Additional command-line arguments will either be added to
the config file args or overwrite the config file args if both are
specified and present.
Example:
search_tweets.py \ --config-file myapiconfig.config \ --no-print-stream
Full options are listed below:
$ search_tweets.py -h
usage: search_tweets.py [-h] [--credential-file CREDENTIAL_FILE]
[--credential-file-key CREDENTIAL_YAML_KEY]
[--env-overwrite ENV_OVERWRITE]
[--config-file CONFIG_FILENAME]
[--account-type {premium,enterprise}]
[--count-bucket COUNT_BUCKET]
[--start-datetime FROM_DATE] [--end-datetime TO_DATE]
[--filter-rule PT_RULE]
[--results-per-call RESULTS_PER_CALL]
[--max-results MAX_RESULTS] [--max-pages MAX_PAGES]
[--results-per-file RESULTS_PER_FILE]
[--filename-prefix FILENAME_PREFIX]
[--no-print-stream] [--print-stream]
[--extra-headers EXTRA_HEADERS] [--debug]
optional arguments:
-h, --help show this help message and exit
--credential-file CREDENTIAL_FILE
Location of the yaml file used to hold your
credentials.
--credential-file-key CREDENTIAL_YAML_KEY
the key in the credential file used for this session's
credentials. Defaults to search_tweets_api
--env-overwrite ENV_OVERWRITE
Overwrite YAML-parsed credentials with any set
environment variables. See API docs or readme for
details.
--config-file CONFIG_FILENAME
configuration file with all parameters. Far, easier to
use than the command-line args version., If a valid
file is found, all args will be populated, from there.
Remaining command-line args, will overrule args found
in the config, file.
--account-type {premium,enterprise}
The account type you are using
--count-bucket COUNT_BUCKET
Set this to make a 'counts' request. Bucket size for counts endpoint. Options:, day, hour,
minute.
--start-datetime FROM_DATE
Start of datetime window, format 'YYYY-mm-DDTHH:MM'
(default: -30 days)
--end-datetime TO_DATE
End of datetime window, format 'YYYY-mm-DDTHH:MM'
(default: most recent date)
--filter-rule PT_RULE
PowerTrack filter rule (See: http://support.gnip.com/c
ustomer/portal/articles/901152-powertrack-operators)
--results-per-call RESULTS_PER_CALL
Number of results to return per call (default 100; max
500) - corresponds to 'maxResults' in the API. If making a 'counts' request with '--count-bucket', this parameter is ignored.
--max-results MAX_RESULTS
Maximum number of Tweets or Counts to return for this
session (defaults to 500)
--max-pages MAX_PAGES
Maximum number of pages/API calls to use for this
session.
--results-per-file RESULTS_PER_FILE
Maximum tweets to save per file.
--filename-prefix FILENAME_PREFIX
prefix for the filename where tweet json data will be
stored.
--no-print-stream disable print streaming
--print-stream Print tweet stream to stdout
--extra-headers EXTRA_HEADERS
JSON-formatted str representing a dict of additional
request headers
--debug print all info and warning messages
Working with the API within a Python program is straightforward both for Premium and Enterprise clients.
We'll assume that credentials are in the default location,
~/.twitter_keys.yaml.
from searchtweets import ResultStream, gen_rule_payload, load_credentialsenterprise_search_args = load_credentials("~/.twitter_keys.yaml",
yaml_key="search_tweets_enterprise",
env_overwrite=False)premium_search_args = load_credentials("~/.twitter_keys.yaml",
yaml_key="search_tweets_premium",
env_overwrite=False)There is a function that formats search API rules into valid json
queries called gen_rule_payload. It has sensible defaults, such as
pulling more Tweets per call than the default 100 (but note that a
sandbox environment can only have a max of 100 here, so if you get
errors, please check this) not including dates. Discussing the finer points of
generating search rules is out of scope for these examples; I encourage
you to see the docs to learn the nuances within, but for now let's see
what a rule looks like.
rule = gen_rule_payload("beyonce", results_per_call=100) # testing with a sandbox account
print(rule){"query":"beyonce","maxResults":100}
This rule will match tweets that have the text beyonce in them.
From this point, there are two ways to interact with the API. There is a
quick method to collect smaller amounts of Tweets to memory that
requires less thought and knowledge, and interaction with the
ResultStream object which will be introduced later.
We'll use the search_args variable to power the configuration point
for the API. The object also takes a valid PowerTrack rule and has
options to cutoff search when hitting limits on both number of Tweets
and API calls.
We'll be using the collect_results function, which has three
parameters.
- rule: a valid PowerTrack rule, referenced earlier
- max_results: as the API handles pagination, it will stop collecting when we get to this number
- result_stream_args: configuration args that we've already specified.
For the remaining examples, please change the args to either premium or enterprise depending on your usage.
Let's see how it goes:
from searchtweets import collect_resultstweets = collect_results(rule,
max_results=100,
result_stream_args=enterprise_search_args) # change this if you need toBy default, Tweet payloads are lazily parsed into a Tweet
object. An overwhelming
number of Tweet attributes are made available directly, as such:
[print(tweet.all_text, end='\n\n') for tweet in tweets[0:10]];Jay-Z & Beyoncé sat across from us at dinner tonight and, at one point, I made eye contact with Beyoncé. My limbs turned to jello and I can no longer form a coherent sentence. I have seen the eyes of the lord. Beyoncé and it isn't close. https://t.co/UdOU9oUtuW As you could guess.. Signs by Beyoncé will always be my shit. When Beyoncé adopts a dog 🙌🏾 https://t.co/U571HyLG4F Hold up, you can't just do that to Beyoncé https://t.co/3p14DocGqA Why y'all keep using Rihanna and Beyoncé gifs to promote the show when y'all let Bey lose the same award she deserved 3 times and let Rihanna leave with nothing but the clothes on her back? https://t.co/w38QpH0wma 30) anybody tell you that you look like Beyoncé https://t.co/Vo4Z7bfSCi Mi Beyoncé favorita https://t.co/f9Jp600l2B Beyoncé necesita ver esto. Que diosa @TiniStoessel 🔥🔥🔥 https://t.co/gadVJbehQZ Joanne Pearce Is now playing IF I WAS A BOY - BEYONCE.mp3 by ! I'm trynna see beyoncé's finsta before I die
[print(tweet.created_at_datetime) for tweet in tweets[0:10]];2018-01-17 00:08:50 2018-01-17 00:08:49 2018-01-17 00:08:44 2018-01-17 00:08:42 2018-01-17 00:08:42 2018-01-17 00:08:42 2018-01-17 00:08:40 2018-01-17 00:08:38 2018-01-17 00:08:37 2018-01-17 00:08:37
[print(tweet.generator.get("name")) for tweet in tweets[0:10]];Twitter for iPhone Twitter for iPhone Twitter for iPhone Twitter for iPhone Twitter for iPhone Twitter for iPhone Twitter for Android Twitter for iPhone Airtime Pro Twitter for iPhone
Voila, we have some Tweets. For interactive environments and other cases where you don't care about collecting your data in a single load or don't need to operate on the stream of Tweets or counts directly, I recommend using this convenience function.
The ResultStream object will be powered by the search_args, and
takes the rules and other configuration parameters, including a hard
stop on number of pages to limit your API call usage.
rs = ResultStream(rule_payload=rule,
max_results=500,
max_pages=1,
**premium_search_args)
print(rs)ResultStream:
{
"username":null,
"endpoint":"https:\/\/api.twitter.com\/1.1\/tweets\/search\/30day\/dev.json",
"rule_payload":{
"query":"beyonce",
"maxResults":100
},
"tweetify":true,
"max_results":500
}
There is a function, .stream, that seamlessly handles requests and
pagination for a given query. It returns a generator, and to grab our
500 Tweets that mention beyonce we can do this:
tweets = list(rs.stream())Tweets are lazily parsed using our Tweet Parser, so tweet data is very easily extractable.
# using unidecode to prevent emoji/accents printing
[print(tweet.all_text) for tweet in tweets[0:10]];gente socorro kkkkkkkkkk BEYONCE https://t.co/kJ9zubvKuf Jay-Z & Beyoncé sat across from us at dinner tonight and, at one point, I made eye contact with Beyoncé. My limbs turned to jello and I can no longer form a coherent sentence. I have seen the eyes of the lord. Beyoncé and it isn't close. https://t.co/UdOU9oUtuW As you could guess.. Signs by Beyoncé will always be my shit. When Beyoncé adopts a dog 🙌🏾 https://t.co/U571HyLG4F Hold up, you can't just do that to Beyoncé https://t.co/3p14DocGqA Why y'all keep using Rihanna and Beyoncé gifs to promote the show when y'all let Bey lose the same award she deserved 3 times and let Rihanna leave with nothing but the clothes on her back? https://t.co/w38QpH0wma 30) anybody tell you that you look like Beyoncé https://t.co/Vo4Z7bfSCi Mi Beyoncé favorita https://t.co/f9Jp600l2B Beyoncé necesita ver esto. Que diosa @TiniStoessel 🔥🔥🔥 https://t.co/gadVJbehQZ Joanne Pearce Is now playing IF I WAS A BOY - BEYONCE.mp3 by !
We can also use the Search API Counts endpoint to get counts of Tweets
that match our rule. Each request will return up to 30 days of results, and
each count request can be done on a minutely, hourly, or daily basis.
The underlying ResultStream object will handle converting your
endpoint to the count endpoint, and you have to specify the
count_bucket argument when making a rule to use it.
The process is very similar to grabbing Tweets, but has some minor differences.
Caveat - premium sandbox environments do NOT have access to the Search API counts endpoint.
count_rule = gen_rule_payload("beyonce", count_bucket="day")
counts = collect_results(count_rule, result_stream_args=enterprise_search_args)Our results are pretty straightforward and can be rapidly used.
counts[{'count': 366, 'timePeriod': '201801170000'},
{'count': 44580, 'timePeriod': '201801160000'},
{'count': 61932, 'timePeriod': '201801150000'},
{'count': 59678, 'timePeriod': '201801140000'},
{'count': 44014, 'timePeriod': '201801130000'},
{'count': 46607, 'timePeriod': '201801120000'},
{'count': 41523, 'timePeriod': '201801110000'},
{'count': 47056, 'timePeriod': '201801100000'},
{'count': 65506, 'timePeriod': '201801090000'},
{'count': 95251, 'timePeriod': '201801080000'},
{'count': 162883, 'timePeriod': '201801070000'},
{'count': 106344, 'timePeriod': '201801060000'},
{'count': 93542, 'timePeriod': '201801050000'},
{'count': 110415, 'timePeriod': '201801040000'},
{'count': 127523, 'timePeriod': '201801030000'},
{'count': 131952, 'timePeriod': '201801020000'},
{'count': 176157, 'timePeriod': '201801010000'},
{'count': 57229, 'timePeriod': '201712310000'},
{'count': 72277, 'timePeriod': '201712300000'},
{'count': 72051, 'timePeriod': '201712290000'},
{'count': 76371, 'timePeriod': '201712280000'},
{'count': 61578, 'timePeriod': '201712270000'},
{'count': 55118, 'timePeriod': '201712260000'},
{'count': 59115, 'timePeriod': '201712250000'},
{'count': 106219, 'timePeriod': '201712240000'},
{'count': 114732, 'timePeriod': '201712230000'},
{'count': 73327, 'timePeriod': '201712220000'},
{'count': 89171, 'timePeriod': '201712210000'},
{'count': 192381, 'timePeriod': '201712200000'},
{'count': 85554, 'timePeriod': '201712190000'},
{'count': 57829, 'timePeriod': '201712180000'}]
Note that this will only work with the full archive search option, which is available to my account only via the enterprise options. Full archive search will likely require a different endpoint or access method; please see your developer console for details.
Let's make a new rule and pass it dates this time.
gen_rule_payload takes timestamps of the following forms:
YYYYmmDDHHMMYYYY-mm-DD(which will convert to midnight UTC (00:00)YYYY-mm-DD HH:MMYYYY-mm-DDTHH:MM
Note - all Tweets are stored in UTC time.
rule = gen_rule_payload("from:jack",
from_date="2017-09-01", #UTC 2017-09-01 00:00
to_date="2017-10-30",#UTC 2017-10-30 00:00
results_per_call=500)
print(rule){"query":"from:jack","maxResults":500,"toDate":"201710300000","fromDate":"201709010000"}
tweets = collect_results(rule, max_results=500, result_stream_args=enterprise_search_args)[print(tweet.all_text) for tweet in tweets[0:10]];More clarity on our private information policy and enforcement. Working to build as much direct context into the product too https://t.co/IrwBexPrBA To provide more clarity on our private information policy, we’ve added specific examples of what is/is not a violation and insight into what we need to remove this type of content from the service. https://t.co/NGx5hh2tTQ Launching violent groups and hateful images/symbols policy on November 22nd https://t.co/NaWuBPxyO5 We will now launch our policies on violent groups and hateful imagery and hate symbols on Nov 22. During the development process, we received valuable feedback that we’re implementing before these are published and enforced. See more on our policy development process here 👇 https://t.co/wx3EeH39BI @WillStick @lizkelley Happy birthday Liz! Off-boarding advertising from all accounts owned by Russia Today (RT) and Sputnik. We’re donating all projected earnings ($1.9mm) to support external research into the use of Twitter in elections, including use of malicious automation and misinformation. https://t.co/zIxfqqXCZr @TMFJMo @anthonynoto Thank you @gasca @stratechery @Lefsetz letter @gasca @stratechery Bridgewater’s Daily Observations Yup!!!! ❤️❤️❤️❤️ #davechappelle https://t.co/ybSGNrQpYF @ndimichino Sometimes Setting up at @CampFlogGnaw https://t.co/nVq8QjkKsf
rule = gen_rule_payload("from:jack",
from_date="2017-09-20",
to_date="2017-10-30",
count_bucket="day",
results_per_call=500)
print(rule){"query":"from:jack","toDate":"201710300000","fromDate":"201709200000","bucket":"day"}
counts = collect_results(rule, max_results=500, result_stream_args=enterprise_search_args)[print(c) for c in counts];{'timePeriod': '201710290000', 'count': 0}
{'timePeriod': '201710280000', 'count': 0}
{'timePeriod': '201710270000', 'count': 3}
{'timePeriod': '201710260000', 'count': 6}
{'timePeriod': '201710250000', 'count': 4}
{'timePeriod': '201710240000', 'count': 4}
{'timePeriod': '201710230000', 'count': 0}
{'timePeriod': '201710220000', 'count': 0}
{'timePeriod': '201710210000', 'count': 3}
{'timePeriod': '201710200000', 'count': 2}
{'timePeriod': '201710190000', 'count': 1}
{'timePeriod': '201710180000', 'count': 6}
{'timePeriod': '201710170000', 'count': 2}
{'timePeriod': '201710160000', 'count': 2}
{'timePeriod': '201710150000', 'count': 1}
{'timePeriod': '201710140000', 'count': 64}
{'timePeriod': '201710130000', 'count': 3}
{'timePeriod': '201710120000', 'count': 4}
{'timePeriod': '201710110000', 'count': 8}
{'timePeriod': '201710100000', 'count': 4}
{'timePeriod': '201710090000', 'count': 1}
{'timePeriod': '201710080000', 'count': 0}
{'timePeriod': '201710070000', 'count': 0}
{'timePeriod': '201710060000', 'count': 1}
{'timePeriod': '201710050000', 'count': 3}
{'timePeriod': '201710040000', 'count': 5}
{'timePeriod': '201710030000', 'count': 8}
{'timePeriod': '201710020000', 'count': 5}
{'timePeriod': '201710010000', 'count': 0}
{'timePeriod': '201709300000', 'count': 0}
{'timePeriod': '201709290000', 'count': 0}
{'timePeriod': '201709280000', 'count': 9}
{'timePeriod': '201709270000', 'count': 41}
{'timePeriod': '201709260000', 'count': 13}
{'timePeriod': '201709250000', 'count': 6}
{'timePeriod': '201709240000', 'count': 7}
{'timePeriod': '201709230000', 'count': 3}
{'timePeriod': '201709220000', 'count': 0}
{'timePeriod': '201709210000', 'count': 1}
{'timePeriod': '201709200000', 'count': 7}
Any contributions should follow the following pattern:
- Make a feature or bugfix branch, e.g.,
git checkout -b my_new_feature - Make your changes in that branch
- Ensure you bump the version number in
searchtweets/_version.pyto reflect your changes. We use Semantic Versioning, so non-breaking enhancements should increment the minor version, e.g.,1.5.0 -> 1.6.0, and bugfixes will increment the last version,1.6.0 -> 1.6.1. - Create a pull request
After the pull request process is accepted, package maintainers will handle building documentation and distribution to Pypi.
For reference, distributing to Pypi is accomplished by the following commands, ran from the root directory in the repo:
python setup.py bdist_wheel
python setup.py sdist
twine upload dist/*How to build the documentation:
Building the documentation requires a few Sphinx packages to build the webpages:
pip install sphinx
pip install sphinx_bootstrap_theme
pip install sphinxcontrib-napoleonThen (once your changes are committed to master) you should be able to run the documentation-generating bash script and follow the instructions:
bash build_sphinx_docs.sh master searchtweetsNote that this README is also generated, and so after any README changes you'll need to re-build the README (you need pandoc version 2.1+ for this) and commit the result:
bash make_readme.sh