As part of the build process we run a few linters to catch common programming errors, stylistic errors and possible security issues. Linters are run only when working with the package (directory) structure.

All linters are run via the demisto-sdk:

demisto-sdk lint -h
Usage: demisto-sdk lint [OPTIONS]
Lint command will perform:
1. Package in host checks - flake8, bandit, mypy, vulture.
2. Package in docker image checks - pylint, pytest, powershell - test, powershell -
Meant to be used with integrations/scripts that use the folder (package) structure. Will
lookup up what docker image to use and will setup the dev dependencies and file in the target
-h, --help Show this message and exit.
-i, --input PATH Specify directory of integration/script
-g, --git Will run only on changed packages
-a, --all-packs Run lint on all directories in content repo
-v, --verbose Verbosity level -v / -vv / .. / -vvv [default: 2]
-q, --quiet Quiet output, only output results in the end
-p, --parallel INTEGER RANGE Run tests in parallel [default: 1]
--no-flake8 Do NOT run flake8 linter
--no-bandit Do NOT run bandit linter
--no-mypy Do NOT run mypy static type checking
--no-vulture Do NOT run vulture linter
--no-pylint Do NOT run pylint linter
--no-test Do NOT test (skip pytest)
--no-pwsh-analyze Do NOT run powershell analyze
--no-pwsh-test Do NOT run powershell test
-kc, --keep-container Keep the test container
--test-xml PATH Path to store pytest xml results
--failure-report PATH Path to store failed packs report
-lp, --log-path PATH Path to store all levels of logs

Note: this script is also used to run pytest. See: Unit Testing

An example of the result for running our lint checks on the HelloWorld package will look like:

➜ content git:(master) ✗ demisto-sdk lint -i Packs/HelloWorld/Integrations/HelloWorld
Execute lint and test on 1/1 packages
HelloWorld - Facts - Using yaml file /home/sb/dev/demisto/content/Packs/HelloWorld/Integrations/HelloWorld/HelloWorld.yml
HelloWorld - Facts - Pulling docker images, can take up to 1-2 minutes if not exists locally
HelloWorld - Facts - demisto/python3: - Python 3.8
HelloWorld - Facts - Tests found
HelloWorld - Facts - Lint file /home/sb/dev/demisto/content/Packs/HelloWorld/Integrations/HelloWorld/
HelloWorld - Facts - Lint file /home/sb/dev/demisto/content/Packs/HelloWorld/Integrations/HelloWorld/
HelloWorld - Flake8 - Start
HelloWorld - Flake8 - Successfully finished
HelloWorld - Bandit - Start
HelloWorld - Bandit - Successfully finished
HelloWorld - Mypy - Start
HelloWorld - Mypy - Successfully finished
HelloWorld - Vulture - Start
HelloWorld - Vulture - Successfully finished
HelloWorld - Flake8 - Start
HelloWorld - Flake8 - Successfully finished
HelloWorld - Image create - Trying to pull existing image devtestdemisto/python3:
HelloWorld - Image create - Found existing image devtestdemisto/python3:
HelloWorld - Image create - Copy pack dir to image devtestdemisto/python3:
HelloWorld - Image create - Image sha256:ba9f6ede55 created successfully
HelloWorld - Pylint - Image sha256:ba9f6ede55 - Start
HelloWorld - Pylint - Image sha256:ba9f6ede55 - exit-code: 0
HelloWorld - Pylint - Image sha256:ba9f6ede55 - Successfully finished
HelloWorld - Pytest - Image sha256:ba9f6ede55 - Start
============================= test session starts ==============================
platform linux -- Python 3.8.2, pytest-5.0.1, py-1.8.1, pluggy-0.13.1
rootdir: /devwork
plugins: json-0.4.0, forked-1.1.3, mock-2.0.0, asyncio-0.10.0, datadir-ng-1.1.1, requests-mock-1.7.0, xdist-1.31.0
collected 10 items .......... [100%]
-------------- generated json report: /devwork/report_pytest.json --------------
========================== 10 passed in 0.43 seconds ===========================
HelloWorld - Pytest - Image sha256:ba9f6ede55 - exit-code: 0
HelloWorld - Pytest - Image sha256:ba9f6ede55 - Successfully finished
Flake8 - [PASS]
Bandit - [PASS]
Mypy - [PASS]
Vulture - [PASS]
Pytest - [PASS]
Pylint - [PASS]
Pwsh analyze - [SKIPPED]
Pwsh test - [SKIPPED]
Passed Unit-tests:
- Package: HelloWorld
- Image: demisto/python3:
Packages: 1
Packages PASS: 1
Packages FAIL: 0


This is a basic linter. It can be run without having all the dependencies available and will catch common errors. We also use this linter to enforce the standard python pep8 formatting style. On rare occasions you may encounter a need to disable an error/warning returned from this linter. Do this by adding an inline comment of the sort on the line you want to disable the error:

# noqa: <error-id>

For example:

example = lambda: 'example' # noqa: E731

When adding an inline comment always also include the error code you are disabling for. That way if there are other errors on the same line they will be reported.

More info:


This linter is similar to flake8 but is able to catch some additional errors. We run this linter with error reporting only. It requires access to dependent modules and thus we run it within a docker image similar with all dependencies (similar to how we run pytest unit tests). On rare occasions you may encounter a need to disable an error/warning returned from this linter. Do this by adding an inline comment of the sort on the line you want to disable the error:

# pylint: disable=<error-name>

For example:

a, b = ... # pylint: disable=unbalanced-tuple-unpacking

Is is also possible to disable and then enable a block of code. For example (taken from

# pylint: disable=undefined-variable
if IS_PY3:
STRING_TYPES = (str, bytes) # type: ignore
STRING_TYPES = (str, unicode) # type: ignore
# pylint: enable=undefined-variable

Note: pylint can take both the error name and error code when doing inline comment disables. It is best to use the name which is clearer to understand.

More info:

For classes that generate members dynamically (such as goolgeapi classes) pylint will generate multiple no-member errors as it won't be able to detect the members of the class. In this case it is best to add a .pylintrc file which will include the following:

ignored-classes=<Class Name List>

See following example


Mypy uses type annotations to check code for common errors. It contains type information for many popular libraries (via typeshed project). Additionally, it allows you to define type annotations for your own functions and data structures. Type annotations are fully supported as a language feature in python 3.6 and above. In earlier versions type annotations are provided via the use of comments.

We run mypy in a relatively aggressive mode so it type checks also functions which don't contain type definitions. This may sometimes cause extra errors. If you receive errors you can always ignore the line with an inline comment of:

# type: ignore[<error-name>]

For example:

a = 1
b = "2"
a = b # type: ignore[assignment]

Note: mypy introduced the ignore[<error-name>] syntax only in version 0.730. See: error code docs. You may see in the code ignores of the form: type: ignore without the error-name. This would usually be from old code written before the support for error-name ignores. We do not recommend using this ignore style as it ignores all errors and increases the risk of ignoring unexpected serious errors.

Dealing with Need type annotation errors: If you receive such an error instead of simply adding an ignore comment it is better to define the type of the variable which is missing type annotation. This error is usually received when an empty dict or list is defined and mypy can not infer the type of the object. In this case it is better to define the type as dict or list. For example python 2 code:

my_list = [] # type: list

Or with python 3 annotations

my_list: list = []

If you know the type that the list will hold use the type constructor List that can specify also what type it holds. For example a list which we know that will hold strings in python 2 code:

my_list = [] # type: List[str]

Or with python 3 annotations

my_list: List[str] = []

Note: When using type constructors such as List or Dict there is need to import the type from the typing module in python 3. In python 2 as part of running mypy our wrapper script will include the typing module.

More info at:


Bandit is a tool designed to find common security issues in Python code.

We run bandit with a confidence level of HIGH. In the rare case that it reports a false positive, you can execlude the code by adding a comment of the sort: # nosec. See: .

Last updated on