“Agent memory”

Persist and retrieve repository-specific knowledge using AGENTS.md files.

Use when implementing or managing persistent, hierarchical memory systems for AI agents.

Implement agent memory with episodic, semantic, and procedural memory stores

Design agent memory systems with short-term, long-term, episodic, and semantic memory and retrieval strategies

Use Cognition-spec compliant Git-backed persistent memory to recall, write, and maintain durable cross-session knowledge across Universal and Project scopes.

Reviews agent memory and state-store configuration for correctness.

Design tenant-scoped memory, retrieval and retention for agents.

Use Nate Jones OB1 Agent Memory from OpenClaw with provenance, scope, review, and use-policy discipline.

Use when a plan, prompt, or doc still names agent-shared-memory.

Use this skill when you need evidence-bounded memory write/read/update/delete, retention, contamination, isolation, provenance, and forgetting behavior…

Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards.

Audit and clean up Claude Code auto-memory for a project: find stale, duplicated, or misplaced memories, move durable rules to their proper home, and apply…

Design memory provenance and tenant isolation.

Use when debugging code, tests, builds, deployments, CI failures, runtime errors, framework issues, API migrations, or UI state bugs where a coding Agent…

Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

Three-tier agent memory model (hot/cold/wiki) for context reduction per spawn

Audit the project's persistent agent memories for backfire-risk, stale/superseded state, contradictions, redundancy, and bloat.

Use when a plan finishes or agent memories have piled up.

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Decomposes and reorganizes agent memory files into focused, single-purpose components.

Use to extend an existing agent project with memory, app integration, VPC, multi-agent, migration, model, browser, code interpreter, payments, or resource…

AI agent memory with mathematical foundations.

Comprehensive guide for initializing or reorganizing agent memory into a deeply hierarchical file structure.

Build agent memory patterns that prevent the same mistakes — using session replay, error tracking, and a self-correcting knowledge base.