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 skills add docxology/template --skill memory-systemsgit clone --depth 1 https://github.com/docxology/templateWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/docxology/template/memory-systems)<a href="https://agentmods.dev/skills/docxology/template/memory-systems"><img src="https://agentmods.dev/badge/skills/docxology/template/memory-systems/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/docxology/template/memory-systems"><img src="https://agentmods.dev/badge/skills/docxology/template/memory-systems.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00068 | $0.03294 |
| Opus 5.5 | $0.00027 | $0.01318 |
| Sonnet 5.5 | $0.00014 | $0.00659 |
| Haiku 4.5 | $0.00007 | $0.00329 |
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 1mo 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.
This is a copy
100% identical to memory-systems — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 230 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 Activate
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)
Do not activate this skill for adjacent work owned by other skills:
- File-backed scratchpads, run logs, and tool-output offloading:
filesystem-context. - Conversation compaction or human-readable handoff summaries:
context-compression. - Masking, prefix caching, token budgets, or retrieval scoping inside one trajectory:
context-optimization. - Formal belief/desire/intention models over RDF state:
bdi-mental-states.
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 suggests tool complexity matters less than reliable retrieval for some memory workloads (claim-memory-locomo-filesystem-baseline). Add structure (graphs, temporal validity) only when retrieval quality degrades or the agent needs multi-hop reasoning, relationship traversal, or time-travel queries.
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.
- 1mo ago First seen · 230 lines · 68 tokens per session scan A 9e2b38ffe947
memory-systems is a skill published in the GitHub repository docxology/template (20 stars, last pushed today), licensed Apache-2.0. It adds 68 tokens to every session and 3,294 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to memory-systems, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
Add graph-backed memory and context retrieval to agent workflows
Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.
media-memory
Use when the user sends or generates an image, screenshot, video, audio or file and wants it saved, or asks to find past media (that diagram, the mockup from last week). Ingests and searches with local ChromaDB embeddings.
supamem
Project-agnostic dual-memory tooling — semantic + structural memory for AI coding agents.
rag-and-memory
Patterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, prevents context bloat, and maintains coherent state across sessions.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.