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.

docs/overview.md

@@ -0,0 +1,85 @@
+# EverOS — Project Overview
+
+## Vision
+
+Build an open-source Python memory framework where **AI agents' long-term memory is plain Markdown files on the user's disk**, not opaque rows in a hosted database.
+
+## Scope
+
+**In scope (v1)**:
+
+- Local deployment for personal agents or small teams
+- Conversation, workflow, agent-trace, file-knowledge → structured memory
+- Hybrid retrieval (BM25 + vector + scalar filter)
+- Cascade index sync (md edit → LanceDB sub-second)
+- Dual-track memory (user-track / agent-track)
+- Offline memory evolution (Foresight / AtomicFact / Profile / Skill)
+- CLI + HTTP API
+
+**Out of scope (v1, future v2)**:
+
+- Multi-tenant / group / community deployment (10K+ users)
+- End-to-cloud sync (planned for v2)
+- Distributed deployment / sharding
+
+## Design philosophy
+
+### 1. Markdown as Source of Truth
+
+```
+delete all LanceDB / SQLite files → can rebuild from md
+delete any md file               → memory is gone
+```
+
+User trust comes from physical visibility — the user can `cat` / `vim` / `grep` their own memory at any time.
+
+### 2. Three-piece storage with clear job boundaries
+
+| Component | Role | Does NOT do |
+|---|---|---|
+| Markdown files | Truth source — entries, frontmatter | Search (grep is degraded fallback only) |
+| SQLite | Queue, cascade audit log, sensitive data isolation | Vector / full-text |
+| LanceDB | Vector ANN + BM25 + scalar filter, single-query hybrid | Be the source of truth (loss = rebuild from md) |
+
+### 3. Algorithm-orchestration separation
+
+[`everalgo`](https://github.com/EverMind-AI/EverAlgo) (a separate library, published as the `everalgo-*` PyPI packages) holds the extraction algorithms (MemCell extraction, Episode generation, Profile evolution). EverOS calls everalgo via the PromptSlot interface; everalgo knows nothing about storage.
+
+This boundary lets the same algorithm power both this open-source lightweight version and other product forms.
+
+### 4. DDD layered architecture
+
+```
+entrypoints  →  service  →  memory  →  infra
+                              ↓
+                        component / core / config
+```
+
+Strict single-direction dependency, enforced by `import-linter` in CI.
+
+## Why src layout (`src/everos/`)
+
+- Standard PyPA project structure used when shipping to PyPI
+- Avoid namespace collision with system packages named `memory`, `infra`, etc.
+- Avoid accidental import of working-tree code in dev (PyPA recommendation)
+
+## Comparable projects (where EverOS differs)
+
+| Project | Position | Difference |
+|---|---|---|
+| [mem0](https://github.com/mem0ai/mem0) | API-first memory service | mem0 stores in vector DB; we store in md files |
+| [Letta](https://github.com/letta-ai/letta) | Agent OS w/ Core/Recall/Archival | Letta uses Postgres; we use markdown filesystem |
+| [MemOS](https://github.com/MemTensor/MemOS) | Multi-classification memory | MemOS targets enterprise; we target lightweight (single-user / small team) |
+| [memsearch](https://github.com/zilliztech/memsearch) | md-first search engine | Closest to us; we add memory extraction (not just search) |
+
+## Roadmap
+
+- **v0.1 (MVP)** — Phase 1 core loop: markdown + lancedb + cascade + episode extraction
+- **v0.2** — Full extraction pipeline (workspace / agent / knowledge), evolution framework
+- **v0.3** — Production hardening, full CLI, HTTP API, Obsidian demo
+- **v1.0** — Stable API, PyPI release, comprehensive docs
+- **v2** (future) — Edge-to-cloud sync via EverMe (separate project)
+
+## Status
+
+**Alpha — v0.1.0 in active development**. Core API may change before v1.0.