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 dmtrKovalenko · Agent Tool · ★ 10.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is fff safe to install? View the security audit →
A file search toolkit for humans and AI agents. Really fast. Typo-resistant path and content search, frequency-ranked file access, a background watcher, and a lightweight in-memory content index. Way faster than CLIs like ripgrep and fzf in any long-running process that searches more than once. Powers file search in opencode, nushell, and many more amazing projects! Originally started as Neovim plugin people loved, but it turned out that plenty of AI harnesses and code editors need the same thing: accurate, fast file search as a library. That is what fff is. Pick what you are interested in: MCP server Works with Claude Code, Codex, OpenCode, Cursor, Cline, and any MCP-capable client. Fewer grep roundtrips, less wasted context, faster answers. One-line install Linux / macOS: bash curl -L
| Stars | 10,776 |
| Forks | 448 |
| Language | Rust |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 68.7986065981801/100 |
| Open Issues | 82 |
| Last Updated | 2026-09-19 |
| Created | 2025-07-31 |
| Platforms | rust |
| Est. Tokens | ~31k |
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fff is The fastest and the most accurate file search SDK for AI agents, Neovim, Rust, C, Python, Bun and NodeJS. It is categorized as a Agent Tool with 10.8k GitHub stars.
fff is primarily written in Rust. It covers topics such as bun, fff, filesearch.
You can find installation instructions and usage details in the fff GitHub repository at github.com/dmtrKovalenko/fff. The project has 10.8k stars and 448 forks, indicating an active community.
fff is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to fff on Agent Skills Hub include oh-my-pi, mcp-for-beginners, sidekick.nvim. 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: