Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Alegau03 · MCP Server · ★ 142
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is CTX safe to install? View the security audit →
CTX The context layer for AI coding agents Reduce token waste in OpenCode and AI coding workflows with graph memory, compact task packs, read-cache compression, command pruning, and a live right-sidebar dashboard. OpenCode-first runtime · Local MCP tools · Read cache + delta-aware indexing · Single Rust binary Install · OpenCode Usage · Dashboard · Benchmarks · Docs · Security CTX is a local context runtime for OpenCode and AI coding agents. It keeps repository knowledge, reusable rules, shell output, diffs, and file rereads compact before they ever bloat the model context. See It In Action ![Read
| Stars | 142 |
| Forks | 12 |
| Language | Rust |
| Category | MCP Server |
| Quality Score | 63.0033880907317/100 |
| Open Issues | 2 |
| Last Updated | 2026-05-11 |
| Created | 2026-04-23 |
| Platforms | claude-code, mcp, rust |
| Est. Tokens | ~1666k |
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CTX is CTX - Context Runtime Engine for Coding Agents. It is categorized as a MCP Server with 142 GitHub stars.
CTX is primarily written in Rust. It covers topics such as ai-agent, ai-agents, ai-tools.
You can find installation instructions and usage details in the CTX GitHub repository at github.com/Alegau03/CTX. The project has 142 stars and 12 forks, indicating an active community.
The top alternatives to CTX on Agent Skills Hub include ctxport, LAP, wcgw. 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: