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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | graph-retrieval |
| description | 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. |
Expose graph-based retrieval as a tool capability without breaking the existing Antigravity execution chain.
query_graph(query, max_hops=2, workspace='.').antigravity/graph/.{
"summary": "...",
"triples": [["subject", "predicate", "object"]],
"evidence": [{"retrieval_id": "...", "tool_name": "..."}],
"nodes": [...],
"edges": [...]
}
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.
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.