diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..a17bfa9
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,20 @@
+language: python
+env:
+  - PYTHON_VERSION=3.10 IPYTHON_KERNEL=python3
+before_install:
+    - wget -q http://repo.continuum.io/miniconda/Miniconda-3.6.0-Linux-x86_64.sh -O miniconda.sh
+    - chmod +x miniconda.sh
+    - ./miniconda.sh -b -p /home/travis/miniconda
+    - export PATH=/home/travis/miniconda/bin:$PATH
+    - conda update --yes --quiet conda
+install:
+    - conda create -n testenv --yes pip python=$PYTHON_VERSION
+    - source activate testenv
+    - pip install -q flake8 seaborn nbconvert[test]
+    - pip install -r requirements.txt
+script:
+    - flake8
+    - ramp-test
+    - jupyter nbconvert --execute variable_stars_starting_kit.ipynb --to html --ExecutePreprocessor.kernel_name=$IPYTHON_KERNEL
+notifications:
+  email: true
diff --git a/README.md b/README.md
index e9c993f..56605a8 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,14 @@
-# RAMP starting kit on classification of variable stars from light curves
+# Template Kit for RAMP challenge
 
-[![Build Status](https://travis-ci.org/ramp-kits/variable_stars.svg?branch=master)](https://travis-ci.org/ramp-kits/variable_stars)
+[![Build Status](https://travis-ci.org/ramp-kits/template-kit.svg?branch=master)](https://travis-ci.org/ramp-kits/template-kit)
 
-Most stars emit light steadily in time, but a small fraction of them has a
-variable light curve: light emission versus time. We call them variable stars.
-The light curves are usually periodic and highly regular. There are essentially
-two reasons why light emission can vary. First, the star itself can be
-oscillating, so its light emission varies in time. Second, the star that seems
-a single point at Earth (because of our large distance) is actually a binary
-system: two stars that orbit around their common center of gravity. When the
-orbital plane is parallel to our line of view, the stars eclipse each other
-periodically, creating a light curve with a characteristic signature.
-Identifying, classifying, and analyzing variable stars are hugely important for
-calibrating distances, and making these analyses automatic will be crucial in
-the upcoming sky survey projects such as LSST.
+## Introduction
 
-The challenge in this RAMP is to design an algorithm to automatically classify variable stars from light curves.
+Describe the challenge, in particular:
+
+- Where the data comes from?
+- What is the task this challenge aims to solve?
+- Why does it matter?
 
 ## Getting started
 
@@ -35,7 +28,7 @@ usage.
 ### Challenge description
 
 Get started on this RAMP with the
-[dedicated notebook](variable_stars_starting_kit.ipynb).
+[dedicated notebook](template_starting_kit.ipynb).
 
 ### Test a submission