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 j4flmao/agent-skills --skill memory-systemsgit clone --depth 1 https://github.com/j4flmao/agent-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/j4flmao/agent-skills/memory-systems)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/memory-systems"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/memory-systems.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.00647 |
| Opus 5 | $0.00013 | $0.00324 |
| Sonnet 5 | $0.00005 | $0.00129 |
| Haiku 4.5 | $0.00003 | $0.00065 |
Grade A, and why
AI Agent Memory Paradigms 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 6d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Paradigms: The Architecture of Continuity
An AI Agent without memory is temporally blind; its existence is constrained to the immediate context window. True autonomy requires a multi-layered memory architecture to simulate the continuity of consciousness and enable long-horizon coherence. This architecture is strictly categorized into three fundamental tiers.
1. Working Memory (The Context Window)
The immediate, transient cognitive space. This is the absolute limit of the agent's active reasoning capacity, defined by the underlying LLM's context window.
- Nature: Highly volatile, exact retrieval, strictly bounded.
- Function: Holds the current goal, immediate environmental state, recent observations, and the active reasoning trace.
- First Principle: Context is a scarce resource. Information must be aggressively compacted or evicted to prevent attention degradation and catastrophic forgetting of immediate instructions.
2. Semantic Memory (The Knowledge Base)
The vast, static repository of facts, concepts, and externalized knowledge. This is typically implemented via dense vector embeddings and approximate nearest neighbor search.
- Nature: Persistent, associative retrieval, theoretically unbounded.
- Function: Provides domain-specific context injected dynamically into Working Memory based on semantic proximity to the current cognitive state.
- First Principle: Semantic memory lacks temporal coherence. It provides "what is", not "what happened". It is highly dependent on embedding quality and chunking strategy to minimize retrieval noise.
3. Episodic Memory (The Experiential Ledger)
The chronological sequence of past events, actions, and outcomes. This is the agent's autobiographical memory, essential for complex reasoning across temporal gaps and learning from past failures.
- Nature: Persistent, temporal/sequential retrieval.
- Function: Enables reflection, trajectory evaluation, and the synthesis of abstract rules from concrete experiences.
- First Principle: Raw logs are not episodic memory. True episodic memory requires the distillation of continuous state transitions into discrete, semantic narratives ("experiences") that can be queried by similarity or sequence.
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.
- 6d ago First seen · 48 lines · 25 tokens per session scan A 708a2673e807
AI Agent Memory Paradigms is a skill published in the GitHub repository j4flmao/agent-skills (21 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 647 once invoked, about $0.0001 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 2026-08-30.
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