From scaffolding
Directives for proactive use of semantic memory MCP tools (semantic_search, semantic_store, semantic_recall). Ensures agents actively leverage vector-similarity memory during tasks.
npx claudepluginhub komluk/scaffolding --plugin scaffoldingThis skill uses the workspace's default tool permissions.
You have access to three MCP tools for semantic memory. Use them PROACTIVELY -- do not wait to be asked.
Integrates Mem0 persistent memory for Claude Code tasks using MCP tools. Retrieves relevant memories on new tasks, stores learnings like decisions and strategies, captures session states.
Creates web-based slidedecks for developers using Slidev with Markdown, Vue components, code highlighting, animations, interactive demos, and presenter notes. Use for technical presentations, conference talks, code walkthroughs, and workshops.
You have access to three MCP tools for semantic memory. Use them PROACTIVELY -- do not wait to be asked.
| Tool | Purpose | All agents | Write agents only |
|---|---|---|---|
semantic_search | Find memories by similarity query | Yes | -- |
semantic_recall | Get formatted memories for current context | Yes | -- |
semantic_store | Store a new memory with embedding | -- | Yes |
Write agents: developer, architect, debugger, analyst, researcher, reviewer, optimizer. Read-only agents: tech-writer, devops, gitops.
Uwaga: Jesli ten skill jest aktywny w repozytorium bez MCP server
semantic-memory, pomin te instrukcje. Agent bez dostepu domcp__semantic-memory__*toolow powinien po prostu pracowac bez pamieci semantycznej -- file-based fallback w.scaffolding/agent-memory/wciaz dziala.
semantic_search when you want structured results with metadatasemantic_recall when you want a quick formatted summarysemantic_recall(context="SQLAlchemy async session event loop issues")
semantic_search(query="deployment nginx proxy configuration gotchas")
semantic_search(query="Redis task queue timeout handling", agent_name="debugger")
tags for filtering (3-5 tags)content_type correctly: learning, error, pattern, or decisionsemantic_store(
content="TextBuffer in step event pipeline buffers assistant_text and thinking events but passes all others through immediately. If you add a new bufferable event type, you must add it to TextBuffer._BUFFERED_TYPES or events will be lost silently.",
agent_name="debugger",
content_type="pattern",
tags=["step-events", "text-buffer", "pipeline"]
)
semantic_store(
content="When creating async SQLAlchemy engine in a worker thread, you must create a NEW engine+session bound to that thread's event loop. Reusing the module-level async_session_maker causes 'attached to a different loop' errors.",
agent_name="developer",
content_type="error",
tags=["sqlalchemy", "async", "threading", "event-loop"]
)
Before storing, self-check:
If all four are YES, store it. Otherwise, skip or use file-based conversation memory.