update readme

README.md

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 # Pypiper
 
 [![Documentation Status](https://readthedocs.org/projects/pypiper/badge/?version=latest)](http://pypiper.readthedocs.org/en/latest/?badge=latest)
-[![Build Status](https://github.com/databio/pypiper/actions/workflows/run-pytest.yml/badge.svg?branch=dev)](https://github.com/databio/pypiper/actions/workflows/run-pytest.yml?branch=dev)
+[![Build Status](https://github.com/databio/pypiper/actions/workflows/run-pytest.yml/badge.svg)](https://github.com/databio/pypiper/actions/workflows/run-pytest.yml)
+[![PEP compatible](http://pepkit.github.io/img/PEP-compatible-green.svg)](http://pepkit.github.io)
+[![pypi-badge](https://img.shields.io/pypi/v/piper)](https://pypi.org/project/piper)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
 A lightweight python toolkit for gluing together restartable, robust shell pipelines. Learn more in the [documentation](http://pypiper.databio.org).

docs/README.md

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 # <img src="img/pypiper_logo.svg" class="img-header"> a developer's pipeline framework
 
 [![PEP compatible](http://pepkit.github.io/img/PEP-compatible-green.svg)](http://pepkit.github.io)
+[![pypi-badge](https://img.shields.io/pypi/v/piper)](https://pypi.org/project/piper)
+[![Documentation Status](https://readthedocs.org/projects/pypiper/badge/?version=latest)](http://pypiper.readthedocs.org/en/latest/?badge=latest)
+[![Build Status](https://github.com/databio/pypiper/actions/workflows/run-pytest.yml/badge.svg)](https://github.com/databio/pypiper/actions/workflows/run-pytest.yml)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 
 ## What is pypiper?
 
 `Pypiper` is a **development-oriented** pipeline framework. It is a python package that helps you write robust pipelines directly in python, handling mundane tasks like restartability, monitoring for time and memory use, monitoring job status, copious log output, robust error handling, easy debugging tools, and guaranteed file output integrity.
 
 
+
 ## What makes pypiper better? 
 With Pypiper, **simplicity is paramount**. Prerequisites are few: base python and 2 common packages (`pyyaml` and `psutil`). It should take fewer than 15 minutes to build your first pipeline and only an hour or two to learn the advanced features. Pypiper pipelines are: