Labby

Labby is a growing collection of automation functions that support Lambda School Labs operations.

Architecture

Labby is a serverless application built on the Serverless Framework currently deployed to AWS. Labby uses various AWS services to allow for scale and reliability.

Packages

The packages in Labby are broken up by the various services that Labby manages. Though, given that Labby enables integration between various services, the code in each package will probably reference other services.

See the README in each package for more details.

• labsaws: For talking to the AWS API
• labscodeclimate: For talking to the Code Climate API
• labssonarcloud: For talking to the Sonar Cloud API
• labsdao: A data access layer for retrieving various Labs-specific entities (e.g. students, projects)
• labsslack: For talking to the Slack API
• labssearchlight: For talking to the Searchlight API
• teambuilding: The Labs Team Builder algorithm

Configuration

Labby needs access to all sorts of APIs and data stores, which require secrets. All secrets are managed via AWS Secrets Manager.

Adding a secret:

aws secretsmanager create-secret --name <secret-name> --secret-string <secret-value>

Here are the secrets that need to be available for Labby:

Github Credentials

Labby interacts with Github as a Github App:

Secret Name	Secret Value
labby-github-api-app-id	Client ID from the app settings
labby-github-api-key	The downloaded app private key
labby-github-integration-id	The integration ID created when installing the app
labby-github-installation-id	The installation ID created when installing the app

Code Climate Credentials

Labby interacts with the Code Climate API using an API key:

Secret Name	Secret Value
labby-code-climate-api-key	Code Climate API key

Airtable Credentials

Labby interacts with Airtable using an API key:

Secret Name	Secret Value
labby-airtable-api-key	Airtable API key

Labby Assets

The assets folder contains various static assets for use by the functions. Be aware that during deployment the serverless-s3-deploy plugin will automatically ship everything in the assets folder to a public, readable by the whole wide world web S3 bucket.

Running Local

pyenv + pipenv

This project uses pyenv for ensuring Python versions are consistent and pipenv for handling dependencies.

AWS Profiles

If you are using aws profiles then you must have an env var AWS_PROFILE set to the profile name. (might as well add AWS_REGION to be safe)

export AWS_PROFILE=labby && export AWS_REGION=us-east-1

• install pipenv
• pipenv install --dev
• pipenv shell
• python --version # Python 3.7.3

TODO: Setup Serverless framework for python

• Getting Started
• npx install serverless
• [config]
• sls invoke cloudside -f codeclimate_enqueue_all_product_repos