Divide memory into a durable USER profile and token-budgeted MEMORY context.
npx skills add https://github.com/RikyZ90/ShibaClaw --skill memory-rikyz90
Please help me install this Agent Skill. Skill: memory Source: https://github.com/RikyZ90/ShibaClaw/tree/main/shibaclaw/skills/memory Command: npx skills add https://github.com/RikyZ90/ShibaClaw --skill memory-rikyz90
Divide memory into a durable USER profile and a token-budgeted MEMORY context. The HISTORY.md logs provide an append-only trace while updates to MEMORY.md are truncated to fit token budgets.
Create USER.md for a durable profile and memory/MEMORY.md for current task context within the token budget.
High-intent search queries and answers about installing and using this skill.
Splitting memory into a durable USER.md profile for long-lived facts and a token-budgeted MEMORY.md for operational context manages AI context without exceeding the token budget. This division retains essential user data while keeping recent decisions constrained within fixed token limits.
Memory compaction is the automatic truncation of operational context in MEMORY.md to fit a fixed token budget. It preserves essential data by retaining critical recent decisions while discarding obsolete details, ensuring the AI agent stays within its operational constraints.
Create a USER.md file for long-lived user facts and preferences, and a memory/MEMORY.md file for current task context to set up durable user profiles and short-term context. This structure separates persistent profile data from operational memory injected into prompts.
You can keep an append-only history log for AI agent traceability by using memory/HISTORY.md. This log acts as a trace for retrospective analysis, recording sequential updates while the operational MEMORY.md context is truncated to fit token budgets.
Dividing memory into a durable USER.md profile and a token-budgeted MEMORY.md context is the best way to structure long-lived user facts separately from recent AI decisions. This approach isolates permanent preferences from volatile operational data and history logs.
AirmomoGenerate OpenClaw Agent workspace scaffolds with AGENTS.md, SOUL.md, IDENTITY.md, and USER.md.
yangkun19921001Maintain persistent context with MEMORY.md for long-term facts and HISTORY.md for event logs.
saddam-eng-techCalculate token usage and API costs for Claude models.
shalevaminDesigns and implements RAG systems with chunking, embedding generation, vector stores, hybrid search, reranking, and evaluation.
