contorium-workspace-memory

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

1. Prefer Current focus (optional) for user intent; never block export if empty.
2. For clipboard handoff, use Copy AI-ready context — not raw event dumps.
3. If focus and active files diverge sharply, suggest Start fresh AI context session.
4. When AI intent is stale (low confidence), rely on heuristic operational intent from recent edits.
5. 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.json
• store_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 context
• contora.startFreshAiSession — reset session activity + intent pool
• contora.analyzeWorkspaceIntent — BYOK intent snapshot
• contora.saveStateNow / contora.restoreSession — persist or restore editors