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 diegosouzapw/awesome-omni-skills --skill agent-memory-systemsgit clone --depth 1 https://github.com/diegosouzapw/awesome-omni-skillsWrote 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/diegosouzapw/awesome-omni-skills/agent-memory-systems)<a href="https://agentmods.dev/skills/diegosouzapw/awesome-omni-skills/agent-memory-systems"><img src="https://agentmods.dev/badge/skills/diegosouzapw/awesome-omni-skills/agent-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/diegosouzapw/awesome-omni-skills/agent-memory-systems"><img src="https://agentmods.dev/badge/skills/diegosouzapw/awesome-omni-skills/agent-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 | $0.00052 | $0.08352 |
| Opus 5 | $0.00026 | $0.04176 |
| Sonnet 5 | $0.00010 | $0.01670 |
| Haiku 4.5 | $0.00005 | $0.00835 |
Grade A, and why
agent-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 11d 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
92% identical to agent-memory-systems — 1,240 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 — 1,235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Memory Systems
Overview
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/agent-memory-systems from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Agent Memory Systems Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge).
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Capabilities, Scope, Tooling, Patterns, LangMem Implementation, Memory Retrieval at Runtime.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
- User mentions or implies: agent memory
- User mentions or implies: long-term memory
- User mentions or implies: memory systems
- User mentions or implies: remember across sessions
- User mentions or implies: memory retrieval
- User mentions or implies: episodic memory
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.
- 11d ago First seen · 1,235 lines · 52 tokens per session scan A 021bb091cff5
agent-memory-systems is a skill published in the GitHub repository diegosouzapw/awesome-omni-skills (140 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 8,352 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to agent-memory-systems, differing in 1,240 lines, and is treated as a copy.
Other skills, from other repositories
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deduplicated-file-caching
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graphql-introspection-trimmer
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copy-relevant-paragraph-only
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head-tail-log-clipping
How autonomous agents truncate massive terminal and build logs by retaining the first 20 lines (head) and last 40 lines (tail) while eliding the middle, eliminating 90% of log token bloat.
header-only-c-cpp-ingestion
How autonomous agents inspect .h/.hpp header files first to understand class contracts and struct layouts before reading heavy .cpp implementation files, slashing C++ context token spend by 85%.