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 ramtinJ95 · Agent Tool · ★ 268
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is opencode-tokenscope safe to install? View the security audit →
OpenCode-Tokenscope, Token Analyzer Plugin Comprehensive token usage analysis and cost tracking for OpenCode AI sessions Track and optimize your token usage across system prompts, user messages, tool outputs, and more. Get detailed breakdowns, accurate cost estimates, and visual insights for your AI development workflow. Installation Option 1: npm (Recommended) Install globally: Add to your (create one in your project root or for global config): Create the command by creating : Restart OpenCode and run To always get the latest version a
| Stars | 268 |
| Forks | 16 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 80.5233888012902/100 |
| Last Updated | 2026-07-29 |
| Created | 2025-10-30 |
| Platforms | node |
| Est. Tokens | ~18k |
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opencode-tokenscope is Comprehensive token usage analysis and cost tracking for opencode sessions. It is categorized as a Agent Tool with 268 GitHub stars.
opencode-tokenscope is primarily written in TypeScript. It covers topics such as ai-tools, cost-tracking, developer-tools.
You can find installation instructions and usage details in the opencode-tokenscope GitHub repository at github.com/ramtinJ95/opencode-tokenscope. The project has 268 stars and 16 forks, indicating an active community.
opencode-tokenscope is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to opencode-tokenscope on Agent Skills Hub include claude-code-statusline, claudetop, toktrack. 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: