Agent Memory
AI and machine learning skill, available on Zeplik
Agent Memory is a ready-to-run AI and machine learning skill on Zeplik. Memory and context management for LLM agents — short/long-term memory stores, conversation persistence, context-window strategies (summarization, trimming, retrieval) and prompt caching. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
The Agent Memory skill loads automatically when your request matches it, or you can invoke it directly by typing /agent-memory in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.
What the Agent Memory skill can do
- Design short-term, long-term, and episodic memory stores for agents
- Build context-window strategies like summarization, trimming, and routing
- Architect persistent conversation memory across sessions
- Implement prompt caching setups including Anthropic-style caching
Try these prompts on Zeplik
Pick a prompt to open it in the Zeplik app. If you are not signed in yet, your prompt is waiting for you the moment you do.
How the Agent Memory skill works
/agent-memory
Umbrella for agent memory and context management. The user's agent needs to remember or fit more into context; establish the memory horizon (turn, session, durable) and constraints, then deliver a memory/context strategy with code. For the underlying vector store route to vector-databases.
Dispatch table
Pick the reference file(s) that match the request, read them, then answer. Read at most 2-3 files per turn.
| Topic | Read |
|---|---|
| Memory is the cornerstone of intelligent agents. | references/agent-memory-systems.md |
| Strategies for managing LLM context windows including summarization, trimming, routing,… | references/context-window-management.md |
| Persistent memory systems for LLM conversations including short-term, long-term, and en… | references/conversation-memory.md |
| Caching strategies for LLM prompts including Anthropic prompt caching, response caching… | references/prompt-caching.md |
How to work
- Identify which leaf topic the request maps to from the dispatch table above; establish the concrete inputs (language, dataset, framework, file format) and the goal. Ask for a missing detail rather than guessing.
- Read the matching reference file(s) before answering. Read at most 2-3 per turn.
- Deliver runnable artifacts — code, configs, specs — with a short rationale, matching the user's existing conventions when they paste code.
- Confirm any decision the source flags (versions, thresholds, tradeoffs) with the user instead of guessing.
Usage
/agent-memory $ARGUMENTS
How to use the Agent Memory skill
Sign in to Zeplik
Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the Agent Memory skill right away.
Describe your AI and machine learning task
Ask in plain language, or type /agent-memory to invoke the skill directly. Zeplik recognizes the Agent Memory skill and applies its method.
Review and refine the result
Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.
Source and credit
- Author
- davila7 (D7 umbrella-consolidation)
- License
- MIT
Adapted from the open-source davila7/claude-code-templates project and tuned to run natively on Zeplik. View source on GitHub.
Frequently asked questions
- What is the Agent Memory skill?
- Agent Memory is a ready-to-run AI and machine learning skill on Zeplik. Memory and context management for LLM agents — short/long-term memory stores, conversation persistence, context-window strategies (summarization, trimming, retrieval) and prompt caching. Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
- How do I use Agent Memory on Zeplik?
- Sign in to Zeplik and ask in plain language, or type /agent-memory in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
- Which AI model does the Agent Memory skill use?
- Any model you choose. Zeplik works across every model in one chat, so the Agent Memory skill runs on your preferred model for the task.
- Where does the Agent Memory skill come from?
- The Agent Memory skill is adapted from the open-source davila7/claude-code-templates project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
- How much does the Agent Memory skill cost?
- Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.
Related ai and machine learning skills
- AI Safety GuardrailsSafety and moderation for LLM apps — Constitutional AI, Llama Guard input/output moderation, and NeMo Guardrails runtime rails. Use for "add safety/moderation/guardrails to my LLM app"; for evaluating safety see llm-evaluation-harnesses.
- Autonomous Agent PatternsFramework-agnostic design patterns for autonomous coding agents — goal decomposition, planning, parallel/multi-agent orchestration and operational modes. Use for "how should I architect an autonomous agent"; for concrete frameworks see ai-agent-frameworks.
- Gemini CLIRun Gemini CLI (Gemini 3 Pro) for code/plan review and huge >200k-token context analysis in the terminal
- InterpretabilityMechanistic interpretability of neural networks — TransformerLens, NNsight remote access, sparse autoencoders (SAELens), and causal interventions (pyvene). Use for "probe/intervene on model internals" or "train an SAE"; for architecture basics see llm-architectures.
- LLM App PatternsProduction patterns for LLM applications — RAG architecture, embeddings, LLMOps, and end-to-end app design. Use for "architect a production LLM/RAG app"; for the vector store see vector-databases, for agents see ai-agent-frameworks.
- LLM ArchitecturesTransformer and alternative model architectures — attention/transformers, long-context (RoPE, YaRN, ALiBi), Mamba/state-space, RWKV, and nanoGPT-scale implementations. Use for "understand/implement a model architecture"; for training it see llm-training-frameworks.
More on Zeplik
Try Agent Memory on Zeplik
Every model, one chat. Bring the Agent Memory skill into your next conversation and let the assistant do the work.