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Use when many tools look similar and you need ReMe to learn historical tool quality, parameter patterns, or retrieval guidance for future tool selection.
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Basierend auf der SOC-Berufsklassifikation
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 starting ReMe over HTTP or MCP, or when embedding ReMeApp directly into an application without changing memory-type semantics.
Use when you need ReMe to learn from prior task trajectories, extract reusable success or failure patterns, or retrieve procedural memory for similar tasks.
| name | reme-tool-memory |
| description | Use when many tools look similar and you need ReMe to learn historical tool quality, parameter patterns, or retrieval guidance for future tool selection. |
Use this skill for data-driven tool selection and parameter optimization in ReMe.
Use this skill when you need to:
Do not use this skill for personal memory, task memory, or generic service startup.
add_tool_call_resultsummary_tool_memoryretrieve_tool_memory