Sub2API is an open-source proxy service that unifies access to Claude, OpenAI, Gemini, and Antigravity APIs. It enables cost-sharing through shared subscriptions and integrates natively with existing tools.
git clone https://github.com/Wei-Shaw/sub2api.githttps://demo.sub2api.org/
["Install Sub2API locally or use a hosted instance. Follow the [official setup guide](https://github.com/sub2api/sub2api) to configure your API keys for supported providers (Claude, OpenAI, etc.).","Specify the backend provider in your prompt. Example: 'Use Sub2API with OpenAI as the backend to...' or 'Use Sub2API with Antigravity as the backend to...'.","Provide your input data directly in the prompt or as a file attachment. For large datasets, pre-process the data to fit within token limits (e.g., summarize long documents before extraction).","Review the output for accuracy and adjust your prompt if needed. Sub2API supports streaming responses, so you can interrupt or refine the output in real-time.","For cost-sharing, ensure your API keys are linked to a shared subscription group in Sub2API. Monitor usage via the Sub2API dashboard to optimize provider selection based on cost/performance."]
Manage multiple AI service subscriptions from a single platform.
Distribute API keys to team members while tracking usage and costs.
Implement rate limiting and concurrency control for API requests.
Automate billing and usage reporting for AI service consumption.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/Wei-Shaw/sub2apiCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Use Sub2API to [TASK] with [SERVICE_PROVIDER] as the backend. Example: 'Use Sub2API to summarize the following research paper and extract key findings into a bullet-point list, using OpenAI as the backend model.' Include the text or data you want processed in your prompt.
### Research Paper Summary: Advances in Quantum Computing **Source:** *Nature, Vol. 623, Issue 7985* (Fictional) **Key Findings:** 1. **Qubit Stability Breakthrough:** A new error-correction method reduced decoherence by 40% in superconducting qubits, extending coherence times to 1.2 milliseconds. This addresses a critical bottleneck in scalable quantum computing. 2. **Hybrid Quantum-Classical Algorithms:** Researchers demonstrated a 3x speedup in molecular simulations (e.g., drug discovery) using a hybrid approach that offloads partial computations to quantum processors. The test case involved simulating a 128-qubit system for HIV protease inhibitors. 3. **Topological Qubits:** Microsoft’s Station Q team achieved a 99.9% fidelity rate in topological qubit operations, a 15% improvement over previous benchmarks. This paves the way for fault-tolerant quantum computers. 4. **Quantum Supremacy Milestone:** Google’s Sycamore processor solved a classically intractable problem (random circuit sampling) in 200 seconds—estimated to take 10,000 years on a supercomputer—using 53 qubits. **Limitations:** - Current systems require cryogenic temperatures (-273°C), limiting practical deployment. - Error rates remain high for NISQ (Noisy Intermediate-Scale Quantum) devices, necessitating repeated measurements. **Next Steps:** - Validate the error-correction method across larger qubit arrays. - Explore integration with classical HPC for hybrid workflows. *Generated using Sub2API (OpenAI backend) on 2024-06-15.*
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