memory-systems
memory-systems is a Memory stores & recall skill for AI agents, published by muratcankoylan in the Agent-Skills-for-Context-Engineering repository on GitHub. Skillspace rates memory-systems D (69/100) and it passed the safety scan; the rating is based on static analysis only.
This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.
- Grade
- D
- Quality
- 69 / 100
- Safety
- Passed
- GitHub stars
- 17,968
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 97 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- Low-risk patterns
- License
- Not declared. Check the repository before reusing it.
What the scanner flagged
- mentions secrets in text
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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