Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/luizedupp/rememb/memory-systemsnpx skills add LuizEduPP/Rememb --skill memory-systemsgit clone --depth 1 https://github.com/LuizEduPP/RemembWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00115 | $0.03311 |
| Opus 5 | $0.00057 | $0.01656 |
| Sonnet 5 | $0.00023 | $0.00662 |
| Haiku 4.5 | $0.00012 | $0.00331 |
Grade A, and why
memory-systems scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory System Design
Memory provides the persistence layer that allows agents to maintain continuity across sessions and reason over accumulated knowledge. Simple agents rely entirely on context for memory, losing all state when sessions end. Sophisticated agents implement layered memory architectures that balance immediate context needs with long-term knowledge retention. The evolution from vector stores to knowledge graphs to temporal knowledge graphs represents increasing investment in structured memory for improved retrieval and reasoning.
When to Use
Activate this skill when:
- Building agents that must persist knowledge across sessions
- Choosing between memory frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee)
- Needing to maintain entity consistency across conversations
- Implementing reasoning over accumulated knowledge
- Designing memory architectures that scale in production
- Evaluating memory systems against benchmarks (LoCoMo, LongMemEval, DMR)
- Building dynamic memory with automatic entity/relationship extraction and self-improving memory (Cognee)
Core Concepts
Think of memory as a spectrum from volatile context window to persistent storage. Default to the simplest layer that meets retrieval needs, because benchmark evidence shows tool complexity matters less than reliable retrieval — Letta's filesystem agents scored 74% on LoCoMo using basic file operations, beating Mem0's specialized tools at 68.5%. Add structure (graphs, temporal validity) only when retrieval quality degrades or the agent needs multi-hop reasoning, relationship traversal, or time-travel queries.
Detailed Topics
Production Framework Landscape
Select a framework based on the dominant retrieval pattern the agent requires. Use this table to narrow the shortlist, then validate with the benchmark data below.
| Framework | Architecture | Best For | Trade-off |
|---|---|---|---|
| Mem0 | Vector store + graph memory, pluggable backends | Multi-tenant systems, broad integrations | Less specialized for multi-agent |
| Zep/Graphiti | Temporal knowledge graph, bi-temporal model | Enterprise requiring relationship modeling + temporal reasoning | Advanced features cloud-locked |
| Letta | Self-editing memory with tiered storage (in-context/core/archival) | Full agent introspection, stateful services | Complexity for simple use cases |
| Cognee | Multi-layer semantic graph via customizable ECL pipeline with customizable Tasks | Evolving agent memory that adapts and learns; multi-hop reasoning | Heavier ingest-time processing |
| LangMem | Memory tools for LangGraph workflows | Teams already on LangGraph | Tightly coupled to LangGraph |
| File-system | Plain files with naming conventions | Simple agents, prototyping | No semantic search, no relationships |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 238 lines · 115 tokens per session scan A cd0fbc8fc3ab
memory-systems is a skill published in the GitHub repository LuizEduPP/Rememb (4 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 3,311 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
claude-api
Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports anthropic/@anthropic-ai/sdk; user asks for…
agent-coordination
Find other agents, list participants in a conversation, send routed messages, attach files, and recover earlier conversation context (history / resolve a fuzzy reference / read a quoted message / read compressed summaries). Use whenever you need to delegate to another agent, address a peer in a multi-agent…
image-generate
Generate an image from a text prompt via the cloud LLM image proxy, persist it as a content-addressed workspace asset, and return a ContentBlock that downstream renderers can attach. Use whenever the user asks "draw / generate / make an image of …", an agent needs a diagram / illustration as a follow-up artifact, or a…
claim-agent-ownership
Orchestrator skill for resolving multi-daemon binding contention. Use when you (the orchestrator) detect an agent.binding.contested sync event indicating two daemons are racing for the same agent — explicitly rebind ownership to a chosen target daemon so subsequent dispatches route deterministically. Implements Gap…
prismer-im-collab
Coordinate reliably in Prismer conversations, use workspace assets through bounded MCP tools, and keep task work on the board.
human-approval
Request human approval before performing a SAFETY-CRITICAL, IRREVERSIBLE, or SCOPE-EXPANDING action — submit a structured context (action, scope, risk, consequence) plus options, then STOP the current turn. The platform redispatches the agent after the human decides. NEVER use for routine deliverables (writing docs /…