Flagged: reads agent memory. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by TheRealSeanDonahoe · MCP Server · ★ 156
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is ijfw safe to install? View the security audit →
Your AI tools are brilliant, forgetful, undisciplined, and alone. One install fixes all four. IJFW is one shared brain, and a full operating layer, for every AI coding tool you use. Install it once and your tools remember across sessions, plan before they build, check each other's work for hallucinations, pull in the right specialist for the job, and quietly cut your token bill. All on your machine. All yours. bash npm install -g @ij
| Stars | 156 |
| Forks | 26 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 60.1303512053623/100 |
| Open Issues | 2 |
| Last Updated | 2026-05-01 |
| Created | 2026-04-16 |
| Platforms | claude-code, cli, codex, gemini, mcp, node |
| Est. Tokens | ~146k |
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ijfw is One install. Shared memory, smart routing, disciplined workflow, multi-AI audits -- across every AI coding agent. No BS.. It is categorized as a MCP Server with 156 GitHub stars.
ijfw is primarily written in JavaScript. It covers topics such as ai-agents, ai-coding, claude-code.
You can find installation instructions and usage details in the ijfw GitHub repository at github.com/TheRealSeanDonahoe/ijfw. The project has 156 stars and 26 forks, indicating an active community.
ijfw is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to ijfw on Agent Skills Hub include Overture, agent-skills, awesome-ai-agent-skills. 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: