Awesome

♚ Yarn workspaces: minimal monorepo package management (yarn, yarn build, yarn docs)
♛ Conventional Commits: human and machine readable meaning to commit messages (yarn commit)
♜ Jest: tests and test coverage for all libraries (yarn test:libraries)
♞ ESLint, Prettier: code quality and formatting (yarn prettier & yarn lint)
♝ Typedocs: documentation generator for TypeScript (yarn docs)
♟ Benny: simple benchmarking framework for JavaScript/TypeScript (yarn benchmarks)
♟ Github actions: software workflows for automatic testing, documentation deploy and code quality checks

🗂️ Repositories

• Circom: privacy-scaling-explorations/zk-kit.circom
• Javascript: privacy-scaling-explorations/zk-kit
• Noir: privacy-scaling-explorations/zk-kit.noir
• Rust: privacy-scaling-explorations/zk-kit.rust
• Solidity: privacy-scaling-explorations/zk-kit.solidity

📄 Papers

• LeanIMT (Download PDF)

📦 Packages

👥 Ways to contribute

• 🔧 Work on open issues
• 📦 Suggest new packages
• 🚀 Share ideas for new features
• 🐛 Create a report if you find any bugs in the code

🛠 Install

Clone this repository:

git clone https://github.com/privacy-scaling-explorations/zk-kit.git

and install the dependencies:

cd zk-kit && yarn

📜 Usage

Code quality and formatting

Run ESLint to analyze the code and catch bugs:

yarn lint

Run Prettier to check formatting rules:

yarn format

or to automatically format the code:

yarn format:write

Conventional commits

ZK-Kit uses conventional commits. A command line utility to commit using the correct syntax can be used by running:

yarn commit

It will also automatically check that the modified files comply with ESLint and Prettier rules.

Testing

Test the code:

yarn test

Build

Build all the packages:

yarn build

A dist folder will be created inside each JavaScript package.

Documentation

Generate a documentation website for each package:

yarn docs

The output will be placed on the docs folder.

Releases

1. Bump a new version of the package with:

yarn version:bump <package-name> <version>
# e.g. yarn version:bump utils 2.0.0

This step creates a commit and a git tag.

2. Push the changes to main:

git push origin main

3. Push the new git tag:

git push origin <package-name>-<version>
# e.g. git push origin utils-v2.0.0

After pushing the new git tag, a workflow will be triggered and will publish the package on npm and release a new version on Github with its changelogs automatically.

❓ FAQ

I have a library that could be reused in other projects. How can I integrate it on ZK-kit?

ZK-kit provides a set of pre-configured development tools. All you have to deal with is your own code, testing and documentation. To create a package follow these steps:

1. Fork this repository and clone it (or simply clone it directly if you are a collaborator),
2. Copy one of our current libraries and update the README.md and package.json files with your package name:

cd zk-kit
cp -r packages/smt packages/my-package
cd packages/my-package && rm -fr node_modules dist
grep -r -l "smt" . | xargs sed -i 's/smt/my-package/'
# Update the remaining description/usage sections, and write your code in the src & tests folders!

3. Create an issue for your package and open a PR.

How can I create benchmarks for my library?

You can see some examples in the benchmarks folder. All you have to do is create a file that exports a function to run your benchmark in that folder and add that function to the index.ts file. The yarn benchmarks command can be run with no parameters (it will run all the benchmarks), or you can specify the name of your benchmark file to run just that. When you run the command it will create a benchmarks/results folder with your results.

I need to use a Merkle Tree to prove the inclusion or exclusion of data elements within a set. Which type of Merkle Tree should I use?

Incremental: Ideal for applications where you frequently add new elements and need to update the tree efficiently.

Lean Incremental: A more memory-efficient version of the incremental Merkle tree.

Sparse: Particularly useful when you need proof of non-membership.

Following benchmarks data of zk-kit Merkle Trees implementations:

From the benchmark data we can take another criteria to evaluate which Merkle tree should be used:

• IMT have the best performance for medium and small size insert related operations.
• LeanIMT have the best performance for all the merkle tree sizes for update and generate proof related operations.
• Sparse is good for larger data insert, delete and verify proof.