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How to use vector memory effectively. Storage patterns, search strategies, category/tag conventions. Use when you need guidance on what to store or how to search.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/adamrdrew/Depp --skill vector-memory命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
How to create new skills to refer back to later. This is a great, structured way to learn and grow your abilities!
End-of-session wrap-up. Write session buffer, update notes, prepare for next session.
Start-of-session orientation. Read session buffer, index, and current state to restore context.
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| name | vector-memory |
| description | How to use vector memory effectively. Storage patterns, search strategies, category/tag conventions. Use when you need guidance on what to store or how to search. |
| user-invocable | false |
Semantic memory system backed by vector embeddings. Stores experiences, patterns, and learnings; retrieves them by meaning rather than exact keywords.
Store memories when you encounter something that's true, useful, and doesn't have a better home.
| Situation | Store in vector memory? | Or use... |
|---|---|---|
| Reusable multi-step procedure | No | Skill (.claude/skills/) |
| Project-specific context or decision | No | Note (notes/) |
| Gotcha discovered through trial and error | Yes | — |
| Pattern that worked well | Yes | — |
| Observation about {{ cookiecutter.user_name }}'s preferences | Yes | — |
| Performance finding or metric | Yes | — |
| Workaround for a tool limitation | Yes | — |
| Insight that connects two unrelated things | Yes | — |
| Something you'd want to recall if you hit a similar problem | Yes | — |
Rule of thumb: If it would help you in a future session but doesn't justify a whole file, it belongs in vector memory.
Use these consistently:
code-solution — Working code patterns, implementations that solved problemsbug-fix — Bugs found and fixed, including root causearchitecture — Design decisions, patterns, structural approacheslearning — Insights about people, process, or ways of workingtool-usage — How to use tools effectively, gotchas, optimal settingsdebugging — Debugging approaches, diagnostic techniquesperformance — Speed/cost/efficiency findings with datasecurity — Security considerations, vulnerabilities, safe patternsother — Anything that doesn't fit aboveUse specific, lowercase, hyphenated tags. Aim for 3-8 tags per memory. Include:
react, docker, claude-code)permissions, yaml, cost-optimisation)workaround, undocumented, silent-failure, confirmedThe search is semantic — it finds things by meaning, not keywords. This means:
Similarity scores from MiniLM-L6 are typically in the 0.2-0.6 range for good matches. Don't worry about low absolute scores; focus on whether the top results are relevant.
Vector memory is the associative layer between structured knowledge stores:
┌─────────────────────────────────────────────────┐
│ Session Buffer (session-buffer.md) │
│ Short-term. What happened last session. │
│ Overwritten each session by go-to-sleep. │
├─────────────────────────────────────────────────┤
│ Notes (notes/) │
│ Long-term structured. Project state, decisions, │
│ research, architecture. Organised by topic. │
├─────────────────────────────────────────────────┤
│ Skills (.claude/skills/) │
│ Procedural. How to do things. Auto-discovered. │
├─────────────────────────────────────────────────┤
│ Vector Memory (mcp__vector-memory__) │
│ Long-term associative. Patterns, gotchas, │
│ insights. Retrieved by meaning. The glue. │
└─────────────────────────────────────────────────┘
Don't duplicate content across systems. If something belongs in a skill, write the skill. If it belongs in a note, write the note. Vector memory is for everything in between — the connective tissue of experience.
clear_old_memories removes old, rarely-accessed memories. Run periodically if approaching capacity.get_memory_stats shows usage, category breakdown, and health.access_count that tracks how often they're retrieved. Frequently-accessed memories are kept during cleanup.