memory-systems
Design Persistent Memory for AI Agents
Agents lose continuity when memory architecture does not match persistence, retrieval, and reasoning needs. This skill guides selection and design across layered memory approaches.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "memory-systems" from https://skillstore.io/skills/sickn33-memory-systems.md and its manifest at https://skillstore.io/api/skills/sickn33-memory-systems/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "memory-systems". Choose memory for a support assistant that remembers preferences but needs no relationship queries.
Expected outcome:
Use working memory for the active case and a long-term key-value store for durable preferences. Add source, timestamp, confidence, and deletion controls.
Using "memory-systems". Design memory for customer addresses that change over time.
Expected outcome:
- Store customers and addresses as entities linked by time-bounded residence relationships.
- Close the previous relationship when a new address becomes valid.
- Filter retrieval by the requested date and define a policy for uncertain or overlapping periods.
Using "memory-systems". Plan consolidation for an agent with rapidly growing memories.
Expected outcome:
Trigger consolidation by schedule and retrieval quality. Merge duplicates, expire invalid facts, preserve provenance, archive obsolete records, and rebuild affected indexes.
Security Audit
SafeAll nine static findings are false positives caused by Markdown fences, documentation prose, a parameterized query example, and source metadata. No executable shell commands, active network requests, reconnaissance behavior, prompt injection, or malicious intent were found.
Risk Factors
⚙️ External commands (3)
🌐 Network access (1)
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APA citation
sickn33. (2026). memory-systems security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-memory-systems/audits/5BibTeX citation
@techreport{sickn33-sickn33-memory-systems-2026,
author = {sickn33},
title = {memory-systems security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-memory-systems/audits/5},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "memory-systems security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-08-04"
url: "https://skillstore.io/skills/sickn33-memory-systems/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-memory-systems:audit:5"
description: "Skillstore immutable audit report identifier"
Compare variants
4 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
sickn33-memory-systems
2026-08-21
asmayaseen-memory-systems
2026-08-21
chakshugautam-memory-systems
2026-08-21
muratcankoylan-memory-systems
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Preserve Assistant Preferences
Design cross-session memory for preferences while separating active context from durable facts.
Model Changing Customer Facts
Plan temporal entity memory that preserves relationship history and prevents outdated facts from overriding current data.
Evaluate Memory Tradeoffs
Compare file, vector, graph, and temporal approaches against query complexity, latency, persistence, and infrastructure constraints.
Try These Prompts
Recommend a memory architecture for an agent that must remember [information] for [duration]. Explain the simplest suitable layer and its tradeoffs.
Design working, short-term, and long-term memory for [agent]. Define stored information, retention, retrieval triggers, and movement between layers.
Compare vector RAG, knowledge graphs, and temporal graphs for [use case]. Evaluate semantic search, relationships, time validity, latency, and operational complexity.
Create a production design for temporal memory in [domain]. Include entities, relationships, validity periods, retrieval, consolidation, privacy, recovery, monitoring, and failure handling.
Best Practices
- Match the memory layer to required persistence, relationships, temporal validity, latency, and operational cost.
- Store provenance, timestamps, confidence, and validity periods so retrieval can resolve conflicting or outdated facts.
- Plan consolidation, privacy controls, deletion, backup, recovery, monitoring, and retrieval failure handling before production use.
Avoid
- Do not place every historical interaction in active context without retrieval or consolidation.
- Do not use vector similarity alone when answers depend on relationships or historical validity.
- Do not retain personal data indefinitely without consent, access controls, deletion rules, and retention limits.