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Use when long-running agent loops are hitting context limits, accumulating large tool outputs, or need ReMe message offload and reload patterns to avoid context rot.
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Basé sur la classification professionnelle SOC
| name | reme-working-memory |
| description | Use when long-running agent loops are hitting context limits, accumulating large tool outputs, or need ReMe message offload and reload patterns to avoid context rot. |
Use this skill for ReMe's working-memory and context-engineering primitives.
Use this skill when you need to:
Do not use this skill for long-term semantic memory policy, personal/task/tool memory classification, or service startup.
This skill centers on two operation families:
Typical reload tools include grep-style search and targeted file reads over offloaded artifacts.
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.
