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 AssafWoo · Codex Skill · ★ 100
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is homebrew-pandafilter safe to install? View the security audit →
PandaFilter The context intelligence layer for AI coding agents. The layer between your tools and your AI. PandaFilter understands what's noise and what matters — compressing, routing, and preserving the right context so your agent thinks faster, costs less, and never loses its place. <img sr
| Stars | 100 |
| Forks | 9 |
| Language | Rust |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 69.6590912429901/100 |
| Open Issues | 2 |
| Last Updated | 2026-06-12 |
| Created | 2026-03-17 |
| Platforms | claude-code, cli, codex, gemini, rust |
| Est. Tokens | ~22k |
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homebrew-pandafilter is The context intelligence layer for AI coding agents. Compressing noise, routing content to the right strategy, preserving session state across compactions, and surfacing the files that actually matter. It is categorized as a Codex Skill with 100 GitHub stars.
homebrew-pandafilter is primarily written in Rust. It covers topics such as agentic-coding, ai-coding, anthropic.
You can find installation instructions and usage details in the homebrew-pandafilter GitHub repository at github.com/AssafWoo/homebrew-pandafilter. The project has 100 stars and 9 forks, indicating an active community.
homebrew-pandafilter is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to homebrew-pandafilter on Agent Skills Hub include omni, llmtrim, claude-code-open. 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: