slm-session
FeaturedManage SuperLocalMemory session lifecycle — call session_init once at the start of every fresh session to load relevant project context and get a session_id; call close_session when work is meaningfully complete to commit temporal summaries. Correct lifecycle hygiene is what makes SLM's learning loop work.
Install
Quality Score: 87/100
Skill Content
Details
- Author
- qualixar
- Repository
- qualixar/superlocalmemory
- Created
- 7 months ago
- Last Updated
- 1 weeks ago
- Language
- Python
- License
- AGPL-3.0
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lpm-memory
Shared project memory for AI coding agents: save or recall work-session logs in `~/.lpm/memory/<project>/<session>.md` so another agent CLI (Claude Code, Codex, Gemini) or a future session can continue the work by session name. Invoke with a session id (e.g. `/lpm-memory auth-refactor`) to continue that session. Use when the user asks to remember or save the session or progress, hand off work, record what was done, or recall/continue/resume/join a named work session. This is per-project memory shared between agent CLIs — distinct from any CLI's own built-in memory.
load-session
Restore session context at the start of every new conversation. Auto-triggers on session start, or when user says 'continue', 'what were we doing', 'where did we leave off'.
slm-remember
Capture durable facts, decisions, constraints, and gotchas into SuperLocalMemory. Use when the user says "remember that", "save this decision", "note this constraint", or when a session produces a conclusion worth persisting across sessions. Always recall first to avoid duplicates.