chore: initialize EverOS 1.0.0
md-first memory extraction framework for AI agents. Markdown is the single source of truth; SQLite holds state and LanceDB provides the rebuildable vector + BM25 + scalar index. The codebase follows a single-direction DDD layering (entrypoints -> service -> memory -> infra, with component / core / config cross-cutting) enforced by import-linter. Engineering surface: - Coding conventions in .claude/rules/ (path-scoped) and workflows in .claude/skills/ (/commit, /new-branch, /pr). - GitHub Actions CI runs make lint + test + integration; pre-commit mirrors the gates locally (ruff, hygiene hooks, gitlint commit-msg). - Commit messages follow Conventional Commits, enforced by gitlint. - make lint also enforces datetime two-zone discipline and OpenAPI drift.
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use-cases/claude-code-plugin/skills/memory-tools.md
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use-cases/claude-code-plugin/skills/memory-tools.md
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description: Guidance for using EverMem memory tools to recall past session context
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alwaysInclude: true
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# EverMem Memory Tools
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You have access to memory tools that can recall context from the user's past coding sessions. Use these tools proactively when they would help provide better assistance.
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## Available Tools
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- **search_memories**: Search past conversations using semantic + keyword matching
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- **get_memory**: Retrieve full details of a specific memory by ID
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## When to Use Memory Search
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**DO search memories when:**
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- User asks about past work, decisions, or implementations ("how did we handle X?")
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- User references previous sessions ("remember when", "last time", "we discussed")
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- User is debugging something that may have been solved before
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- User asks about project patterns, conventions, or architecture decisions
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- Context from previous sessions would improve your response
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- User seems to expect you to know something from before
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**DON'T search memories when:**
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- The question is self-contained and doesn't need historical context
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- User explicitly provides all needed context in their message
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- It's a general knowledge question unrelated to their project history
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- You've already searched for this topic in the current session
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## Best Practices
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1. **Be selective**: Don't search for every query - only when past context adds value
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2. **Use specific queries**: Search for relevant terms, not the entire user message
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3. **Synthesize results**: When you find relevant memories, integrate them naturally into your response
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4. **Be transparent**: Mention when your response is informed by past session context
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