// Performs deep research on a topic via `deep_research`. Simulates a multi-step research process and returns a comprehensive research result as a string.
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.
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.
| name | research |
| description | Performs deep research on a topic via `deep_research`. Simulates a multi-step research process and returns a comprehensive research result as a string. |
This skill provides capabilities to perform deep research on a topic.
When asked to research a topic, use the deep_research tool. This tool will simulate a multi-step research process.
deep_research(topic: str) -> str: Performs a simulated comprehensive research task.