菜单
Use when starting ReMe over HTTP or MCP, or when embedding ReMeApp directly into an application without changing memory-type semantics.
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
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