{"id":24071127,"url":"https://github.com/lfl-lab/squadds","last_synced_at":"2026-04-21T07:05:15.169Z","repository":{"id":213407950,"uuid":"733220451","full_name":"LFL-Lab/SQuADDS","owner":"LFL-Lab","description":"A validated design database and simulation workflow software for superconducting quantum hardware","archived":false,"fork":false,"pushed_at":"2025-05-04T21:37:39.000Z","size":50409,"stargazers_count":28,"open_issues_count":3,"forks_count":13,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-05-04T22:19:37.779Z","etag":null,"topics":["circuit-design","design","qiskit","qiskit-metal","quantum-computing","quantum-hardware","qubits","resonators","superconducting-qubits","superconducting-resonators","transmon","transmon-qubit"],"latest_commit_sha":null,"homepage":"https://lfl-lab.github.io/SQuADDS/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LFL-Lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2023-12-18T20:47:50.000Z","updated_at":"2025-05-04T21:35:44.000Z","dependencies_parsed_at":"2023-12-20T20:46:15.211Z","dependency_job_id":"c7a1aec9-ace0-444e-b8b1-0c42cbb0806e","html_url":"https://github.com/LFL-Lab/SQuADDS","commit_stats":null,"previous_names":["lfl-lab/squadds"],"tags_count":20,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LFL-Lab%2FSQuADDS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LFL-Lab%2FSQuADDS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LFL-Lab%2FSQuADDS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LFL-Lab%2FSQuADDS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LFL-Lab","download_url":"https://codeload.github.com/LFL-Lab/SQuADDS/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252898854,"owners_count":21821700,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["circuit-design","design","qiskit","qiskit-metal","quantum-computing","quantum-hardware","qubits","resonators","superconducting-qubits","superconducting-resonators","transmon","transmon-qubit"],"created_at":"2025-01-09T16:38:37.885Z","updated_at":"2026-04-21T07:05:15.163Z","avatar_url":"https://github.com/LFL-Lab.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ccenter\u003e\n  \u003cimg src=\"docs/_static/images/squadds_not_transparent.png\" width=\"100%\" alt=\"SQuADDS Logo\" /\u003e\n\u003c/center\u003e\n\n# Superconducting Qubit And Device Design and Simulation Database ![Version](https://img.shields.io/github/v/release/LFL-Lab/SQuADDS) ![Pepy Total Downloads](https://img.shields.io/pepy/dt/squadds) ![Build Status](https://img.shields.io/github/actions/workflow/status/LFL-Lab/SQuADDS/ci.yml?branch=master) ![License](https://img.shields.io/github/license/LFL-Lab/SQuADDS) [![arXiv](https://img.shields.io/badge/arXiv-2312.13483-\u003cCOLOR\u003e.svg)](https://arxiv.org/abs/2312.13483) ![Alpha Version](https://img.shields.io/badge/Status-Alpha%20Version-yellow)\n\n\u003e :warning: **This project is an alpha release and currently under active development. Some features and documentation may be incomplete. Please update to the latest release.**\n\nThe SQuADDS (Superconducting Qubit And Device Design and Simulation) Database Project is an open-source resource aimed at advancing research in superconducting quantum device designs. It provides a robust workflow for generating and simulating superconducting quantum device designs, facilitating the accurate prediction of Hamiltonian parameters across a wide range of design geometries.\n\n**Paper Link:** [SQuADDS: A Database for Superconducting Quantum Device Design and Simulation](https://quantum-journal.org/papers/q-2024-09-09-1465/)\n\n**Docsite Link:** [https://lfl-lab.github.io/SQuADDS/](https://lfl-lab.github.io/SQuADDS/)\n\n**Hugging Face Link:** [https://huggingface.co/datasets/SQuADDS/SQuADDS_DB](https://huggingface.co/datasets/SQuADDS/SQuADDS_DB)\n\n**Contribution Portal Link:** [https://squadds-portal.vercel.app](https://squadds-portal.vercel.app)\n\n**Chat with the Codebase:** [https://deepwiki.com/LFL-Lab/SQuADDS/1-overview](https://deepwiki.com/LFL-Lab/SQuADDS/1-overview)\n\n## Table of Contents\n\n- [Citation](#citation)\n- [Installation](#installation)\n  - [Install from Source](#install-from-source-recommended-for-development)\n  - [Install using pip](#install-using-pip)\n  - [Run using Docker](#run-using-docker)\n- [Tutorials](#tutorials)\n- [MCP Server (AI Agent Integration)](#mcp-server-ai-agent-integration)\n- [ML Models](#ml-models)\n- [Contributing](#contributing)\n- [License](#license)\n- [FAQs](#faqs)\n- [Contact](#contact)\n- [Contributors](#contributors)\n- [Developers](#developers)\n\n---\n\n## Citation\n\nIf you use SQuADDS in your research, please cite the following paper:\n\n```bibtex\n@article{Shanto2024squaddsvalidated,\n  doi = {10.22331/q-2024-09-09-1465},\n  url = {https://doi.org/10.22331/q-2024-09-09-1465},\n  title = {{SQ}u{ADDS}: {A} validated design database and simulation workflow for superconducting qubit design},\n  author = {Shanto, Sadman and Kuo, Andre and Miyamoto, Clark and Zhang, Haimeng and Maurya, Vivek and Vlachos, Evangelos and Hecht, Malida and Shum, Chung Wa and Levenson-Falk, Eli},\n  journal = {{Quantum}},\n  issn = {2521-327X},\n  publisher = {{Verein zur F{\\\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},\n  volume = {8},\n  pages = {1465},\n  month = sep,\n  year = {2024}\n}\n```\n\n---\n\n## Installation\n\nSQuADDS uses [uv](https://docs.astral.sh/uv/) for fast, reliable Python package management.\n\n### Prerequisites\n\nInstall `uv` (if you don't have it already):\n\n```bash\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n```\n\n### Install from Source (Recommended for Development)\n\n```bash\ngit clone https://github.com/LFL-Lab/SQuADDS.git\ncd SQuADDS\nuv sync\n```\n\nVerify the installation:\n\n```bash\nuv run python -c \"import squadds; print(squadds.__file__)\"\n```\n\n### Install using pip\n\n```bash\npip install SQuADDS\n```\n\n### Optional Dependencies\n\nInstall GDS processing tools:\n\n```bash\nuv sync --extra gds\n```\n\nInstall documentation tools:\n\n```bash\nuv sync --extra docs\n```\n\nInstall development tools:\n\n```bash\nuv sync --extra dev\n```\n\nInstall contribution tools (for contributing data to SQuADDS):\n\n```bash\nuv sync --extra contrib\n```\n\nInstall all optional dependencies:\n\n```bash\nuv sync --all-extras\n```\n\n### Setting up Jupyter Notebook\n\nTo use SQuADDS in Jupyter notebooks (including VS Code/Cursor), register the kernel:\n\n```bash\nuv sync --extra dev  # Installs ipykernel\nuv run python -m ipykernel install --user --name squadds --display-name \"SQuADDS (uv)\"\n```\n\nThen select **\"SQuADDS (uv)\"** as your kernel in Jupyter/VS Code/Cursor.\n\n### Run using Docker:\n\n\u003cdetails\u003e\n\u003csummary\u003eClick to expand/hide Docker instructions\u003c/summary\u003e\n\u003cbr\u003e\n\nWe provide a pre-built Docker image that contains all dependencies, including `Qiskit-Metal` and the latest `SQuADDS` release.\n\n#### Pull the Latest Docker Image\n\nYou can pull the latest image of **SQuADDS** from GitHub Packages:\n\n```bash\ndocker pull ghcr.io/lfl-lab/squadds_env:latest\n```\n\nIf you'd like to pull a specific version (support begins from `v0.3.4` onwards), use the following command:\n\n```bash\ndocker pull ghcr.io/lfl-lab/squadds_env:v0.3.4\n```\n\nYou can find all available versions and tags for the **squadds_env** Docker image on [LFL-Lab Packages](https://github.com/LFL-Lab?tab=packages\u0026repo_name=SQuADDS).\n\n#### Run the Docker Container\n\nAfter pulling the image, you can run the container using:\n\n```bash\ndocker run -it ghcr.io/lfl-lab/squadds_env:latest /bin/bash\n```\n\nThis will give you access to a bash shell inside the container.\n\n#### Activate the Conda Environment\n\nInside the container, activate the `squadds-env` environment:\n\n```bash\nconda activate squadds-env\n```\n\n#### Run SQuADDS\n\nOnce the environment is active, you can run **SQuADDS** by executing your Python scripts or starting an interactive Python session.\n\n\u003c/details\u003e\n\n---\n\n## Tutorials\n\nThe following tutorials are available to help you get started with `SQuADDS`:\n\n- [Tutorial 0: Using the SQuADDS WebUI](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-0_Using_the_SQuADDS_WebUI.html)\n- [Tutorial 1: Getting Started with SQuADDS](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-1_Getting_Started_with_SQuADDS.html)\n- [Tutorial 2: Simulating Interpolated Designs](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-2_Simulate_interpolated_designs.html)\n- [Tutorial 3: Contributing Experimentally-Validated Simulation Data to the SQuADDS Database](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-3_Contributing_Validated_Simulation_Data_to_SQuADDS.html)\n- [Tutorial 4: Contributing Measured Devices' Data to the SQuADDS Database](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial_4_Contributing_Measured_Data_to_SQuADDS.html)\n- [Tutorial 5: Designing a \"fab-ready\" chip with SQuADDS](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-5_Designing_a_fab_ready_chip_with_SQuADDS.html)\n- [Tutorial 6: Adding Airbridges](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-6_Adding_Airbridges.html)\n- [Tutorial 7: Simulate designs with palace](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-7_Simulate_designs_with_palace.html)\n- [Tutorial 8: ML Interpolation in SQuADDS](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-8_ML_interpolation_in_SQuADDS.html)\n- [Tutorial 9: Learning the Inverse Map](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-9_Learing_the_Inverse_Design_Map.html)\n- [Tutorial 10: HFSS Driven-Modal Capacitance Extraction](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-10_DrivenModal_Capacitance_Extraction.html)\n- [Tutorial 11: Unified Driven-Modal Hamiltonian Extraction](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-11_DrivenModal_Combined_Hamiltonian_Extraction.html)\n\n---\n\n## MCP Server (AI Agent Integration)\n\nSQuADDS includes a built-in **Model Context Protocol (MCP)** server that lets AI coding agents interact with the entire database — searching designs, interpolating parameters, and exploring components — through a standardized protocol.\n\n### Agent Setup (Copy-Paste This to Your AI Agent)\n\n\u003e **If you're using an AI coding assistant**, just paste this prompt to have it set up SQuADDS MCP for you:\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eClick to copy the agent setup prompt\u003c/strong\u003e\u003c/summary\u003e\n\n```\nI need you to set up the SQuADDS MCP server so I can access the superconducting\nqubit design database through you. Here's what to do:\n\n1. Clone the repo and install:\n   git clone https://github.com/LFL-Lab/SQuADDS.git\n   cd SQuADDS\n   uv sync --extra mcp\n\n2. Add the MCP server to your config. The command to run the server is:\n   uv run --directory /path/to/SQuADDS squadds-mcp\n\n3. Once connected, read the `squadds://guide` resource for a quick overview\n   of available tools.\n\nThe server exposes these key tools:\n- `list_components` / `list_datasets` — explore the database\n- `find_closest_designs` — find designs matching target Hamiltonian parameters\n- `interpolate_design` — get physics-interpolated designs\n- `get_hamiltonian_param_keys` — discover valid search parameters\n\nTypical target parameter ranges:\n- qubit_frequency_GHz: 3–8\n- anharmonicity_MHz: −500 to −50\n- cavity_frequency_GHz: 5–12\n- kappa_kHz: 10–1000\n- g_MHz: 10–200\n\nPlease set this up and confirm you can access the SQuADDS tools.\n```\n\n\u003c/details\u003e\n\n### Manual Setup\n\n#### Install\n\n```bash\ngit clone https://github.com/LFL-Lab/SQuADDS.git\ncd SQuADDS\nuv sync --extra mcp\n```\n\n#### Run\n\n```bash\n# stdio mode (for local AI assistants)\nuv run squadds-mcp\n\n# HTTP mode (for networked/remote usage)\nSQUADDS_MCP_TRANSPORT=streamable-http uv run squadds-mcp\n```\n\n#### Connect Your AI Client\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eClaude Desktop\u003c/strong\u003e\u003c/summary\u003e\n\nAdd to `claude_desktop_config.json`:\n```json\n{\n  \"mcpServers\": {\n    \"squadds\": {\n      \"command\": \"uv\",\n      \"args\": [\"run\", \"--directory\", \"/path/to/SQuADDS\", \"squadds-mcp\"]\n    }\n  }\n}\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eClaude Code\u003c/strong\u003e\u003c/summary\u003e\n\n```bash\nclaude mcp add squadds -- uv run --directory /path/to/SQuADDS squadds-mcp\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eCursor\u003c/strong\u003e\u003c/summary\u003e\n\nAdd to `.cursor/mcp.json` in your project:\n```json\n{\n  \"mcpServers\": {\n    \"squadds\": {\n      \"command\": \"uv\",\n      \"args\": [\"run\", \"--directory\", \"/path/to/SQuADDS\", \"squadds-mcp\"]\n    }\n  }\n}\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eVS Code (Copilot)\u003c/strong\u003e\u003c/summary\u003e\n\nAdd to `.vscode/settings.json`:\n```json\n{\n  \"mcp\": {\n    \"servers\": {\n      \"squadds\": {\n        \"command\": \"uv\",\n        \"args\": [\"run\", \"--directory\", \"/path/to/SQuADDS\", \"squadds-mcp\"]\n      }\n    }\n  }\n}\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eAntigravity (Gemini)\u003c/strong\u003e\u003c/summary\u003e\n\nAdd to `~/.gemini/settings.json` (or project-level `.gemini/settings.json`):\n```json\n{\n  \"mcpServers\": {\n    \"squadds\": {\n      \"command\": \"uv\",\n      \"args\": [\"run\", \"--directory\", \"/path/to/SQuADDS\", \"squadds-mcp\"]\n    }\n  }\n}\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eGemini CLI\u003c/strong\u003e\u003c/summary\u003e\n\nAdd to `~/.gemini/settings.json`:\n```json\n{\n  \"mcpServers\": {\n    \"squadds\": {\n      \"command\": \"uv\",\n      \"args\": [\"run\", \"--directory\", \"/path/to/SQuADDS\", \"squadds-mcp\"]\n    }\n  }\n}\n```\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003eOpenAI Codex CLI\u003c/strong\u003e\u003c/summary\u003e\n\n```bash\ncodex --mcp-config mcp.json\n```\n\nWith `mcp.json`:\n```json\n{\n  \"mcpServers\": {\n    \"squadds\": {\n      \"command\": \"uv\",\n      \"args\": [\"run\", \"--directory\", \"/path/to/SQuADDS\", \"squadds-mcp\"]\n    }\n  }\n}\n```\n\u003c/details\u003e\n\n**Full MCP documentation:** [MCP_README.md](MCP_README.md) | **Developer guide:** [MCP_DEVELOPER_GUIDE.md](MCP_DEVELOPER_GUIDE.md)\n\n---\n\n## ML Models\n\nWe host ML models trained on SQuADDS on our [Hugging Face org](https://huggingface.co/SQuADDS), served through the [SQuADDS ML Inference API Space](https://huggingface.co/spaces/SQuADDS/squadds-ml-inference-api). Docsite page: [ML Models](https://lfl-lab.github.io/SQuADDS/source/ml_models.html).\n\nOur first production model is a **qubit-claw (TransmonCross) Hamiltonian-to-geometry inverse** model, developed in collaboration with Taylor Patti, Nicola Pancotti, Enectali Figueroa-Feliciano, Sara Sussman, Abhishek Chakraborty, Olivia Seidel, Firas Abouzahr, Eli Levenson-Falk, and Sadman Ahmed Shanto — with **Olivia Seidel and Firas Abouzahr** as the primary trainers.\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003etransmon_cross_hamiltonian_inverse — usage\u003c/strong\u003e\u003c/summary\u003e\n\n- Model repo: \u003chttps://huggingface.co/SQuADDS/transmon-cross-hamiltonian-inverse\u003e\n- Space / live API: \u003chttps://squadds-squadds-ml-inference-api.hf.space\u003e\n- Routes: `GET /health`, `GET /models`, `POST /predict`\n\nRecommended agent flow: `GET /models` → pick a model with `status=\"ready\"` → `POST /predict` with that `model_id` and the exact input keys it advertises.\n\n```bash\ncurl -X POST \\\n  https://squadds-squadds-ml-inference-api.hf.space/predict \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model_id\":\"transmon_cross_hamiltonian_inverse\",\"inputs\":{\"qubit_frequency_GHz\":4.85,\"anharmonicity_MHz\":-205.0}}'\n```\n\nInputs: `qubit_frequency_GHz`, `anharmonicity_MHz`.\nOutputs (SI units, meters): `design_options.connection_pads.readout.claw_length`, `design_options.connection_pads.readout.ground_spacing`, `design_options.cross_length`. Feed those straight into SQuADDS / Qiskit Metal downstream flows.\n\nFull contract, sample response, and manifest: see the [model repo README](https://huggingface.co/SQuADDS/transmon-cross-hamiltonian-inverse) and the [Space README](https://huggingface.co/spaces/SQuADDS/squadds-ml-inference-api).\n\n\u003c/details\u003e\n\nMore models are coming — resonator and qubit-cavity coupled-system inverses are next (the deployment tooling already knows about these families, so they drop in once checkpoints land). **If you've trained a well-performing SQuADDS-based model, please PR it in** — open an issue or PR against [SQuADDS/squadds-ml-inference-api](https://huggingface.co/spaces/SQuADDS/squadds-ml-inference-api) and we'll get it on the model page.\n\n---\n\n## Contributing\n\nWe welcome contributions from the community! Here is our [work wish list](wish_list.md).\n\nYou can use our [web portal](https://squadds-portal.vercel.app) to contribute your files - [https://squadds-portal.vercel.app](https://squadds-portal.vercel.app)\n\nPlease see our [Contributing Guidelines](CONTRIBUTING.md) for more information on how to get started and absolutely feel free to reach out to us if you have any questions.\n\n---\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\n## FAQs\n\nCheck out our [FAQs](https://lfl-lab.github.io/SQuADDS/source/getting_started.html#faq-s) for common questions and answers.\n\n---\n\n## Contact\n\nFor inquiries or support, please contact [Sadman Ahmed Shanto](mailto:shanto@usc.edu).\n\n---\n\n## Contributors\n\n\n| Name                  | Institution                        | Contribution                         |\n|:----------------------|:-----------------------------------|:-------------------------------------|\n| Clark Miyamoto        | New York University                | Code contributor                     |\n| Madison Howard        | California Institute of Technology | Bug Hunter                           |\n| Evangelos Vlachos     | University of Southern California  | Code contributor                     |\n| Kaveh Pezeshki        | Stanford University                | Documentation contributor            |\n| Anne Whelan           | US Navy                            | Documentation contributor            |\n| Jenny Huang           | Columbia University                | Documentation contributor            |\n| Connie Miao           | Stanford University                | Data Contributor                     |\n| Malida Hecht          | University of Southern California  | Data contributor                     |\n| Daria Kowsari, PhD    | University of Southern California  | Data contributor                     |\n| Vivek Maurya          | University of Southern California  | Data contributor                     |\n| Haimeng Zhang, PhD    | IBM                                | Data contributor                     |\n| Elizabeth Kunz        | University of Southern California  | Documentation  and  Code contributor |\n| Adhish Chakravorty    | University of Southern California  | Documentation  and  Code contributor |\n| Ethan Zheng           | University of Southern California  | Data contributor  and Bug Hunter     |\n| Sara Sussman, PhD     | Fermilab                           | Bug Hunter                           |\n| Priyangshu Chatterjee | IIT Kharagpur                      | Documentation contributor            |\n| Abhishek Chakraborty  | Rigetti Computing                  | Code contributor                     |\n| Saikat Das            | University of Southern California  | Reviewer                             |\n| Firas Abouzahr        | Northwestern                       | Bug Hunter                           |\n\n## Developers\n- [shanto268](https://github.com/shanto268) - 440 contributions\n- [elizabethkunz](https://github.com/elizabethkunz) - 17 contributions\n- [LFL-Lab](https://github.com/LFL-Lab) - 9 contributions\n- [NxtGenLegend](https://github.com/NxtGenLegend) - 1 contributions\n- [ethanzhen7](https://github.com/ethanzhen7) - 1 contributions\n- [PCodeShark25](https://github.com/PCodeShark25) - 1 contributions\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flfl-lab%2Fsquadds","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flfl-lab%2Fsquadds","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flfl-lab%2Fsquadds/lists"}