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 WillNigri · MCP Server · ★ 33
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Agentic-Tool-Optimization safe to install? View the security audit →
Local-first multi-runtime AI cockpit (CLI + desktop + MCP). War-rooms across Claude/Codex/Gemini, replay, regression detection, receipts in SQLite. MIT.
| Stars | 33 |
| Forks | 6 |
| Language | Rust |
| Category | MCP Server |
| License | MIT |
| Quality Score | 63.4958334263925/100 |
| Open Issues | 22 |
| Last Updated | 2026-08-17 |
| Created | 2026-03-17 |
| Platforms | claude-code, cli, codex, gemini, mcp, rust |
| Est. Tokens | ~25k |
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Agentic-Tool-Optimization is Local-first multi-runtime AI cockpit (CLI + desktop + MCP). War-rooms across Claude/Codex/Gemini, replay, regression detection, receipts in SQLite. MIT.. It is categorized as a MCP Server with 33 GitHub stars.
Agentic-Tool-Optimization is primarily written in Rust. It covers topics such as ai-agents, claude-code, codex.
You can find installation instructions and usage details in the Agentic-Tool-Optimization GitHub repository at github.com/WillNigri/Agentic-Tool-Optimization. The project has 33 stars and 6 forks, indicating an active community.
Agentic-Tool-Optimization is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Agentic-Tool-Optimization on Agent Skills Hub include vnx-orchestration, claude-code-open, agent-skill-bus. 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: