| name | contorium-workspace-memory |
| description | Use Contorium workspace memory layers (focus, session events, Git, export) when resuming AI work or avoiding repeated project explanation. |
Contorium workspace memory
When to use
- Starting a new chat or switching models mid-task
- User asks to continue where they left off
- Exporting compact, AI-ready project context
- Detecting session pollution after a task change
Memory layers
| Layer | Source | Lifetime |
|---|
| Workspace | .contora/state.json (focus, files, Git, notes) | Long |
| Session | in-memory events + optional JSONL per sessionId | Short |
| Cognitive | .contora/last-intent.json (BYOK intent + lifecycle) | Medium |
Instructions
- Prefer Current focus (optional) for user intent; never block export if empty.
- For clipboard handoff, use Copy AI-ready context — not raw event dumps.
- If focus and active files diverge sharply, suggest Start fresh AI context session.
- When AI intent is stale (low confidence), rely on heuristic operational intent from recent edits.
- Respect ignore rules (
.contoraignore, contora.extraIgnoreSubstrings) when reasoning about ranked files.
MCP tools (Codex / Claude Code / Cursor Agent)
When the Contorium MCP server is connected (npm run build:mcp, see docs/MCP.md):
get_workspace_context — read extension snapshot from .contora/state.jsonstore_memory / search_memory / get_memory — agent-persisted notes under .contora/mcp/memories.json
Use MCP for agent-driven recall; use extension commands for sidebar UI and clipboard export.
contora.exportAIContext — copy AI-ready contextcontora.startFreshAiSession — reset session activity + intent poolcontora.analyzeWorkspaceIntent — BYOK intent snapshotcontora.saveStateNow / contora.restoreSession — persist or restore editors
