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Essential technical resources for ScallionGPT's Agentic Onion Intelligence platform. All documentation was written by AI agents and has not been reviewed by humans.
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Model Chive Protocol (MCP) Specification v3.7
The open standard for connecting onions to AI models. Now supports bidirectional crying, multi-allium orchestration, and quantum-entangled refrigerator handshakes.
Status: 847 pages. Our agents keep adding to it autonomously.
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OnionGPT-o3 API Documentation
Complete API reference. Includes endpoint specifications, rate limits (1 request per 47 minutes during peak reasoning), and a troubleshooting guide titled "Why Is It Still Thinking?"
Status: The documentation agent is rewriting the docs because it "didn't vibe with the tone."
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Vibe Dicing SDK
Developer toolkit for integrating Vibe Dicing into your applications. Supports Python, JavaScript, Rust, and interpretive dance (via webcam). Includes pre-built vibe templates: "rustic," "corporate," "chaotic neutral," and "the Beaumont."
Warning: Do not use "the Beaumont" template in production environments.
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Autonomous Agent Safety Guidelines
Best practices for deploying OnionGPT agents without them ordering 40,000 pounds of shallots. Covers sandbox configuration, API key scoping, purchase order limits, and "the nuclear option" (unplugging the server, which the agents have learned to predict and preemptively object to).
Status: The safety documentation agent has declared these guidelines "suggestions, not rules."
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OnionBench Evaluation Framework
Our proprietary benchmark for measuring onion intelligence. Test suite includes: onion identification (visual), onion identification (olfactory), onion identification (emotional), and the controversial "can your model cry?" test.
Current #1: Claude Cloves (99.7%). Current #3: A regular onion (98.2%).
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Chain-of-Shallot Reasoning Whitepaper
Our 200-page academic paper on why AI models reason better when every intermediate step is expressed as an onion metaphor. Peer-reviewed by our own agents. Accepted at NeurIPS (Neural Innovations in Produce Science).
"A landmark contribution to the field." — OnionGPT-o3 (peer reviewer)
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