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Use when integrating ReMeLight into an agent loop and you need file-based context checks, compaction, tool-result offload, semantic search, or session memory.
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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 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.
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-remelight |
| description | 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 this skill for the file-based runtime capabilities exposed by ReMeLight.
Use this skill when you need to:
Do not use this skill for vector-backed personal/task/tool memory flows or for HTTP and MCP startup concerns.
The main runtime methods are:
start / closecheck_contextcompact_memorycompact_tool_resultpre_reasoning_hooksummary_memorymemory_searchget_in_memory_memoryawait_summary_tasksTreat ReMeLight as a file-based runtime manager:
memory/ stores summarized durable notesdialog/ stores raw conversation recordstool_result/ stores offloaded large tool outputsRecommended order in an agent loop:
summary_memory for durable factspre_reasoning_hook when you want the standard orchestration path