菜单
Bootstraps a new multi-agent repository from the Antigravity template via `init_agent_repo`. Supports quick scaffold and full runtime profile setup including MCP toggle, swarm preference, sandbox type, and optional git init. LLM configuration is handled later by ag-setup.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | agent-repo-init |
| description | Bootstraps a new multi-agent repository from the Antigravity template via `init_agent_repo`. Supports quick scaffold and full runtime profile setup including MCP toggle, swarm preference, sandbox type, and optional git init. LLM configuration is handled later by ag-setup. |
This skill bootstraps a new multi-agent repository from the Antigravity template.
When asked to initialize a fresh repository from this template, call init_agent_repo.
quick: Fast clean scaffold.full: Quick scaffold plus runtime profile setup (.env, mission, context profile, and init report).init_agent_repo(project_name, destination_root=".", mode="quick", enable_mcp=False, enable_swarm=True, sandbox_runtime="local", init_git=False) -> dict<destination_root>/<project_name>.full mode, review .context/agent_runtime_profile.md after generation./ag-setup in the generated project to choose and write the LLM endpoint.One-click initialization of a multi-agent repository from the Antigravity template. Use this skill when users want to scaffold a new project quickly (`quick` mode) or with runtime defaults (`full` mode) including MCP toggle, swarm preference context, sandbox type, and optional git init. LLM configuration is handled later by ag-setup.
Exposes graph-based retrieval as a tool capability via `query_graph`. Reads normalized graph store files, builds a query-relevant subgraph, and returns LLM-friendly semantic triples with replayable evidence metadata.
High-level deployment wrapper over Antigravity core with graph-first knowledge injection and all-file support. Exposes `refresh_filesystem` and `ask_filesystem` for building and querying the knowledge graph.
Performs deep research on a topic via `deep_research`. Simulates a multi-step research process and returns a comprehensive research result as a string.