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Use when starting ReMe over HTTP or MCP, or when embedding ReMeApp directly into an application without changing memory-type semantics.
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
Use when an agent needs a file-first ReMe workspace as the source of durable setup facts, stable preferences, or reusable daily decision records.
Use when an agent needs a file-first ReMe workspace as the source of durable setup facts, stable preferences, or reusable daily decision records.
Use when you need vector-backed personal memory in ReMe, including preference extraction, personal-memory retrieval, or PersonalMemoryService and ReMeApp integration.
Use when integrating ReMeLight into an agent loop and you need file-based context checks, compaction, tool-result offload, semantic search, or session memory.
Use when you need ReMe to learn from prior task trajectories, extract reusable success or failure patterns, or retrieve procedural memory for similar tasks.
Use when many tools look similar and you need ReMe to learn historical tool quality, parameter patterns, or retrieval guidance for future tool selection.
| name | reme-service |
| description | Use when starting ReMe over HTTP or MCP, or when embedding ReMeApp directly into an application without changing memory-type semantics. |
Use this skill for ReMe service startup and application integration surfaces.
Use this skill when you need to:
ReMeApp directly through Python importDo not use this skill to decide personal/task/tool memory policy or file-first workspace rules.
backend=httpbackend=mcpfrom reme_ai import ReMeAppChoose one integration surface:
ReMeApp import when Python code should call ReMe in-processworkspace_id convention per integration