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 mpecan · Agent Tool · ★ 196
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is tokf safe to install? View the security audit →
tokf tokf.net — reduce LLM context consumption from CLI commands by 60–90%. Commands like , , and produce verbose output packed with progress bars, compile noise, and boilerplate. tokf intercepts that output, applies a TOML filter, and emits only what matters — so your AI agent sees a clean signal instead of hundreds of wasted tokens. Before / After — 61 lines → 1 line: Without tokf With tokf
| Stars | 196 |
| Forks | 19 |
| Language | Rust |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 73.1963541943805/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-06 |
| Created | 2026-02-18 |
| Platforms | claude-code, cli, rust |
| Est. Tokens | ~34k |
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tokf is Config-driven CLI tool that compresses command output before it reaches an LLM context. It is categorized as a Agent Tool with 196 GitHub stars.
tokf is primarily written in Rust. It covers topics such as ai-tools, claude-code, cli.
You can find installation instructions and usage details in the tokf GitHub repository at github.com/mpecan/tokf. The project has 196 stars and 19 forks, indicating an active community.
tokf is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to tokf on Agent Skills Hub include omni, rlm-rs, homebrew-pandafilter. 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: