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Test Omnigent’s native Antigravity integration with a live local server and the real agy CLI. Verify messages traveling between the web interface and terminal, mirrored replies, permission requests, interruption, session resumption, and process cleanup.
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指导安装和更新Antigravity CLI、管理插件，并排查认证与权限设置。还可提交单次代码审查或实现请求、恢复交互会话，以及检查配置文件和日志。
Test Omnigent’s Antigravity SDK integration against a live local server. Build test agents and run real conversations to check Gemini authentication, model selection, tool connections, and failures across the full execution path.
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Delegate repository analysis and coding tasks to Antigravity CLI without an interactive session. The skill checks structured results and errors, and supports comparing its work with results from another coding agent.
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Build Python APIs with FastAPI and Pydantic V2, including input validation, database operations that avoid blocking other work, and token-based authentication. Implement REST or WebSocket endpoints, then check their behavior with automated tests and generated API documentation.
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Install, configure, or update anti-slop plugins for Oxlint, a code checking tool. Preserves local customizations in copied plugin code while aligning dependency versions, merging configuration, running checks, and recording the installed source.
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Compress LLM weights using HQQ quantization, which stores weights with fewer bits without calibration datasets. Follow workflows for precision settings, model saving, inference integration, and LoRA fine-tuning of quantized models.
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Apply HQQ quantization, which stores language model weights using fewer bits without calibration examples. Covers precision settings, inference backends, saving compressed models, deployment with HuggingFace Transformers or vLLM, and further training with LoRA.
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Set up Antigravity 2.0 Agent Manager alongside Antigravity IDE to coordinate parallel development agents. Assign roles and folder boundaries, review their changes in the editor, and use Git checkpoints to manage shared work.
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Configure permission checks for unattended Antigravity CLI runs. The setup combines blocking rules, automatic approval for scoped operations, AI classification for ambiguous calls, and single-use approval tokens to control tool access within a verified workspace.
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使用JSON、正则表达式、Pydantic类型定义或固定选项约束LLM输出，将文字中提取的信息和分类结果整理为便于后续程序使用的格式。
从文本中提取所需信息，整理为固定字段，并用Pydantic验证类型和值。涵盖结构化大语言模型输出、验证失败后的重试，以及逐步接收生成中的结果。
测量并比较多台计算机达成一致结果的分布式共识方式，分析处理量、响应时间、扩展表现及资源使用情况，找出性能瓶颈并整理配置调整和改进建议。
Install and customize a status bar for Antigravity CLI that shows the model, usage quotas, reset times, context capacity and git changes. Check the installation, adjust themes and layouts, and export the configuration.
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Guides Python agent development with PydanticAI, covering tool calls and structured outputs checked against defined data types. Explains service injection, provider switching, streaming responses, and tests that run without calling a real language model.
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Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.
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Find published journal and conference papers through Semantic Scholar, filtering by year, field, type, or citation count. Compare publication details and abstracts, with DOI links, short summaries, and open access PDFs when available.
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Get independent code reviews of uncommitted changes, branch differences, or individual commits using Codex or Antigravity. Receive findings with severity, locations, impact, and suggested corrections, and compare where the two reviewers agree or differ.
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为FastAPI应用开发与审查提供支持，涵盖项目结构、输入验证、身份验证、权限检查、异步处理、数据库更新及测试的设计与实现。
Move Python application settings out of code and into environment variables. Use pydantic-settings to check value types and required fields at startup, organize development and production settings, and keep secrets out of version control.
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Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.
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Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.
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Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.
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Helps reduce GPU memory use by loading large language models in 4-bit or 8-bit formats, a technique called quantization. Covers bitsandbytes configuration, memory checks, QLoRA fine-tuning, and training with memory-saving optimizers.
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