by promptise-com · MCP Server · ★ 893
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 893 |
| Forks | 136 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 58/100 |
| Last Updated | 2026-11-02 |
| Created | 2025-08-29 |
| Platforms | mcp, python |
| Est. Tokens | ~17k |
Promptise Foundry The foundation layer for agentic intelligence. Ship full-stack agentic systems the way they're meant to be built — production-ready, secure by default, with the developer experience modern Python deserves. <a href="https://github.com/promptise-com/foundry/commits
These tools work well together with Foundry for enhanced workflows:
Looking for a Foundry alternative? If you're comparing Foundry with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
The SmythOS Runtime Environment (SRE) is an open-source, cloud-native runtime for agentic AI. Secure, modular,
Open-source self-hosted AI agent runtime and multi-agent framework for autonomous agent swarms. Agent memory,
Open-source Agentic AI framework in Go for building, orchestrating, and deploying intelligent agents. LLM-agno
The Ruby-native AI framework. Chats, agents, tools, images, audio, and video through one consistent API, in pl
A set of tools that gives agents powerful capabilities.
Agent samples built using the Strands Agents SDK.
Explore other popular mcp server tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: