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Semantic memory and context - store and retrieve information with embeddings for similarity search. Use for long-term memory, context recall, and knowledge persistence.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
Use when evaluating, designing, or pressure-testing the business model of an AI agent product. Triggers on "agent business model", "agent economics", "agent canvas", "evaluate agent product", "agent pricing", "agent unit economics", "agentic business", "AI agent company", "agent cost structure".
Use when someone wants to launch a content brand from scratch on any platform. Triggers on "launch a channel", "start a YouTube channel", "build a podcast", "start a newsletter", "content brand", "creator launchpad", "build my audience", "start creating content", "grow on TikTok", "start a blog".
Use when you have an existing pipeline DOT file that needs review, fixing, or validation. Use when validation produces warnings or errors, when a DOT file was hand-written or generated by another tool, or when you want to verify a pipeline covers 100% of a spec before running it.
Use when you want to build a project but don't have a spec yet and need to brainstorm the idea into a design doc structured for pipeline DOT generation. Use when starting from a vague idea, project concept, or feature request that needs to become a headless autonomous build pipeline.
Use when you have a spec, design doc, structured plan, or prompt_plan and need to generate a pipeline Graphviz DOT file for headless autonomous building. The agent reads the spec, extracts build pipeline phases, determines tech stack, asks about human gates and model preferences, generates a validated DOT digraph with rich self-contained prompts, and saves it to the project root. Handles any language or framework.
Use when a pipeline DOT file has passed structural validation but may still have flow inefficiencies, spec fidelity gaps, or information loss in prompts. Use after dotfile-audit or dotfile-from-spec when you want to deeply verify a pipeline captures everything from the original spec. Use when prompts might be missing critical domain rules, data models, or invariants.
| name | memory |
| description | Semantic memory and context - store and retrieve information with embeddings for similarity search. Use for long-term memory, context recall, and knowledge persistence. |
Vector-based memory storage with embeddings for semantic similarity search.
| Tool | Purpose |
|---|---|
mcp__memory__store | Store a memory |
mcp__memory__search | Semantic similarity search |
mcp__memory__list | List recent memories |
mcp__memory__delete | Remove a memory |
mcp__memory__get_context | Get relevant context |
mcp__memory__store(
content="User prefers TypeScript over JavaScript for new projects",
tags=["preferences", "programming"]
)
mcp__memory__search(query="What does the user prefer for web development?", limit=5)
mcp__memory__get_context(topic="user's coding preferences")
mcp__memory__list(limit=10)
memory add "Content to remember" --tag preference
memory search "query" # Semantic search
memory list # Recent memories
memory export -f markdown # Export
~/.local/share/memory/memory.db (SQLite with embeddings as BLOB, respects XDG_DATA_HOME)