by fajarhide · MCP Server · ★ 374
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
brew install fajarhide/tap/omniTaken from the project's README on GitHub.
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.
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 | 374 |
| Forks | 35 |
| Language | Rust |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 67/100 |
| Open Issues | 14 |
| Last Updated | 2026-10-29 |
| Created | 2026-03-15 |
| Platforms | claude-code, cli, mcp, rust |
| Est. Tokens | ~24k |
Less noise. More signal. Cut your AI token consumption by up to 90%. OMNI is a smart terminal layer that intelligently filters and prioritizes command output before it reaches your AI agent. By preventing your AI from getting confused by noisy output, you get accurate answers faster while saving massive amounts of token costs. Fully transparent. You're always in control.
Looking for a omni alternative? If you're comparing omni 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.
Reusable project context and specialist workflows for the AI tools you already use. Agents, skills, rules and
Cut AI context cost without trusting the compressor. Every reduction is reversible, byte-exact recoverable, an
The context intelligence layer for AI coding agents. Compressing noise, routing content to the right strategy,
The control plane for AI coding agents.
Local proxy that compresses your LLM API requests so you pay less, with no change to the answers. Trims wasted
Config-driven CLI tool that compresses command output before it reaches an LLM context
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: