No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by dcostenco · MCP Server · ★ 131
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is prism-mcp safe to install? View the security audit →
Prism Coder Persistent memory and reliable tool-routing for AI agents. (formerly Prism MCP) Prism Coder is a Model Context Protocol server that gives Claude, Cursor, and other AI tools long-term memory that survives across sessions — semantic search, cognitive routing, and a visual dashboard. It ships alongside the open-weight model fleet (1.7B-32B) for fast, offline tool-routing when you don't want a cloud round-trip. It runs fully local and free on SQLite + Ollama with no API keys. A paid subscription adds cloud sync, higher model tiers, and team features through the Synalux portal. Renamed in v14: the project is now Prism Coder to cover both the memory server and the model fleet. The npm package stays , so existing install URLs and entries kee
| Stars | 131 |
| Forks | 22 |
| Language | TypeScript |
| Category | MCP Server |
| Quality Score | 66.0808122910945/100 |
| Open Issues | 11 |
| Last Updated | 2026-04-28 |
| Created | 2026-02-12 |
| Platforms | mcp, node |
| Est. Tokens | ~14213k |
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prism-mcp is The Mind Palace for AI Agents - HIPAA-hardened Cognitive Architecture with on-device LLM (prism-coder:7b), Hebbian learning, ACT-R spreading activation, adversarial evaluation, persistent memory, mult. It is categorized as a MCP Server with 131 GitHub stars.
prism-mcp is primarily written in TypeScript. It covers topics such as agent-memory, ai-agent, anti-sycophancy.
You can find installation instructions and usage details in the prism-mcp GitHub repository at github.com/dcostenco/prism-mcp. The project has 131 stars and 22 forks, indicating an active community.
The top alternatives to prism-mcp on Agent Skills Hub include Ori-Mnemos, Zikkaron, mcp-local-rag. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: