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 Mattbusel · MCP Server · ★ 57
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is tokio-prompt-orchestrator safe to install? View the security audit →
tokio-prompt-orchestrator Production-grade, multi-core Tokio orchestration for LLM inference pipelines. Five-stage bounded-backpressure DAG with deduplication, circuit breakers, rate limiting, prompt injection/jailbreak detection, provider arbitrage (cheapest provider meeting your latency SLA), adaptive worker pool sizing, and an optional autonomous self-improving control loop. Supports Anthropic, OpenAI, llama.cpp, vLLM, and any custom backend. Exposes REST, WebSocket, SSE, MCP (Claude Desktop), Prometheus metrics, and OpenTelemetry distributed tracing.
| Stars | 57 |
| Forks | 5 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 63.7379725213552/100 |
| Open Issues | 2 |
| Last Updated | 2026-03-23 |
| Created | 2025-10-07 |
| Platforms | mcp, rust |
| Est. Tokens | ~20012k |
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tokio-prompt-orchestrator is Multi-core, Tokio-native orchestration for LLM pipelines.. It is categorized as a MCP Server with 57 GitHub stars.
tokio-prompt-orchestrator is primarily written in Rust. It covers topics such as agent, async, backpressure.
You can find installation instructions and usage details in the tokio-prompt-orchestrator GitHub repository at github.com/Mattbusel/tokio-prompt-orchestrator. The project has 57 stars and 5 forks, indicating an active community.
tokio-prompt-orchestrator is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to tokio-prompt-orchestrator on Agent Skills Hub include omnicoreagent, gopher-mcp, sub-agents-mcp. 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: