Skills agent-memory-systems
📦

agent-memory-systems

Content revision r2 Safe ⚙️ External commands🌐 Network access🔑 Env variables

Design Reliable Agent Memory Systems

Agents lose useful context across interactions and may retrieve stale or irrelevant memories. This skill provides architectures, storage patterns, retrieval controls, decay methods, and evaluation guidance.

Supports: Claude Codex Code(CC)
📊 76 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "agent-memory-systems" from https://skillstore.io/skills/sickn33-agent-memory-systems.md and its manifest at https://skillstore.io/api/skills/sickn33-agent-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.

Markdown detail GET /skills/sickn33-agent-memory-systems.md Signed manifest GET /api/skills/sickn33-agent-memory-systems/manifest Signed lockfile GET /api/skills/sickn33-agent-memory-systems/lockfile

Test it

Using "agent-memory-systems". Design memory for a customer support assistant with multiple organizations.

Expected outcome:

Use separate organization and user namespaces. Store stable facts as semantic memory and resolved cases as episodic memory. Filter every query by both identifiers.

Using "agent-memory-systems". Improve retrieval for product manuals and troubleshooting questions.

Expected outcome:

Split by document headings, preserve section context, and test several chunk sizes. Combine metadata filters with hybrid retrieval, then measure recall and answer relevance.

Security Audit

Safe
v5 • 7/23/2026 Open versioned report

All 12 static findings are false positives caused by educational examples, ordinary prose, or Markdown formatting. No executable payload, credential exposure, malicious network activity, or prompt injection was found in the skill.

1
Files scanned
1,089
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

⚙️ External commands (1)
SKILL.md:1069
🌐 Network access (1)
SKILL.md:243
🔑 Env variables (3)
SKILL.md:116 SKILL.md:209 SKILL.md:209
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
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Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.

Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). agent-memory-systems security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-agent-memory-systems/audits/5

BibTeX citation

@techreport{sickn33-sickn33-agent-memory-systems-2026, author = {sickn33}, title = {agent-memory-systems security audit report (audit version 5)}, institution = {Skillstore}, year = {2026}, number = {5}, url = {https://skillstore.io/skills/sickn33-agent-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: "agent-memory-systems security audit report (audit version 5)" version: "unspecified" type: report authors: - name: "sickn33" date-released: "2026-07-23" url: "https://skillstore.io/skills/sickn33-agent-memory-systems/audits/5" identifiers: - type: other value: "skillstore:sickn33-agent-memory-systems:audit:5" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
85
Maintainability
87
Content
69
Community
70
Spec Compliance

What You Can Build

Plan a Persistent Assistant

Define memory types, storage boundaries, retrieval filters, and update rules for an assistant that remembers across sessions.

Improve Retrieval Quality

Select chunking, metadata, reranking, recency, and evaluation methods for documents and conversation history.

Review Memory Architecture

Identify stale-memory, context-budget, model-migration, and cross-user leakage risks before production deployment.

Try These Prompts

Choose Memory Types 
Explain semantic, episodic, procedural, working, and short-term memory for [agent]. Recommend what each type should store and explain why.
Design the Architecture 
Design a memory architecture for [application] with [users] and [data scale]. Compare suitable stores, define namespaces, and include user isolation.
Plan Retrieval and Chunking 
Create a retrieval and chunking plan for [content types] and [query patterns]. Include filters, chunk sizes, overlap, reranking, token budgets, and evaluation metrics.
Audit a Production Design 
Audit this memory design: [design]. Identify privacy, cross-user leakage, stale-memory, conflict, context-window, and embedding migration risks. Prioritize fixes and propose validation tests.

Best Practices

  • Filter every memory query by tenant and user identifiers before applying similarity search.
  • Measure retrieval quality with representative queries before selecting chunk sizes, stores, or embedding models.
  • Track timestamps, memory type, source, importance, and embedding version in metadata.

Avoid

  • Do not store every interaction forever without consent, retention rules, consolidation, or decay.
  • Do not rely on semantic similarity alone when tenant, user, type, or time filters are available.
  • Do not mix query and document embeddings from different models or untracked versions.

Frequently Asked Questions

What memory types does this skill cover?
It covers short-term, working, semantic, episodic, and procedural memory, with guidance for assigning information to each type.
Does this skill select a vector database automatically?
No. It compares common options and helps you choose based on scale, filtering, hosting, latency, and cost requirements.
Can it build a complete memory service?
No. It provides architecture patterns and illustrative snippets that require implementation, integration, testing, and operational controls.
How does it address cross-user data leakage?
It recommends tenant and user metadata on stored memories, plus matching filters on every retrieval query.
How should stale or conflicting memories be handled?
Use timestamps, recency scoring, explicit versioning, conflict checks, consolidation, and replacement rules for mutable facts.
Does it cover retrieval evaluation?
Yes. It recommends representative test queries, recall measurement, chunk-size experiments, result limits, and context-budget checks.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

r2

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/agent-memory-systems

Ref

ebdfe608f5de2b66ff37ab4af12af8ac4f5e8006

Maintenance freshness

7/26/2026

Usage

10 downloads · 127 views

File structure

📄 SKILL.md

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