slm-governance
FeaturedEnterprise compliance and governed workspace behavior for SuperLocalMemory. Covers role-based access (admin/member/viewer), retention policies, audit trail, GDPR data export/erase, and how agents must behave when operating under workspace governance. Requires power MCP profile for audit/retention tools. Agents must never bypass governance controls.
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
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
superlocalmemory
AI agent memory with mathematical foundations. Store, recall, search, and manage memories locally. Local data root; optional networked features have separate behavior.
workspace-file-governance
Deep, autonomous workspace file governance. This skill should be used whenever an agent is about to create new files or directories (creation gate), and whenever a workspace needs organizing, refactoring, or cleanup at any depth — 工作区混乱/目录重构/文件整理/深度整理/随意新建文件/workspace cleanup/directory hygiene/refactor workspace. It requires the agent to first build a full understanding of the project (structure, tech stack, file purposes, dependencies), then act autonomously on all routine decisions, escalating only decisions that risk data loss or broken functionality. Model-agnostic and agent-agnostic.
slm-profile
Workspace isolation and runtime profile switching for SuperLocalMemory. Each profile is a fully independent memory namespace — separate facts, code graphs, and tool sets. Use switch_profile (MCP, requires code/full/power profile) to change the active workspace without restarting. Check the active profile with slm status. Required when working across multiple projects, clients, or tenants.