token-optimizer — security grade CAUTION, quality 63/100

Security audit verdict: CAUTION · quality 63/100

Flagged: reads agent memory. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by alexgreensh · Codex Skill · ★ 2.4k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is token-optimizer safe to install? View the security audit →

About token-optimizer

Audit your setup. Protect your sessions. Measure what matters.Find the ghost tokens. Score your context quality. Survive compaction. NEW in v2.0: Active Session Intelligence v1.x found the ghost tokens. v2.0 p

agentskillsclaude-codeclaude-code-skillclaude-plugincodexcontext-engineeringcontext-windowcost-managementcost-optimizationcost-tracking

Quick Facts

Stars2,352
Forks180
LanguagePython
CategoryCodex Skill
Quality Score63.4642856202249/100
Open Issues2
Last Updated2026-09-22
Created2026-02-26
Platformsclaude-code, codex, python
Est. Tokens~20k

token-optimizer alternative? Top 6 similar tools

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Frequently Asked Questions

What is token-optimizer?

token-optimizer is Find the ghost tokens. Fix them. Survive compaction. Avoid context quality decay.. It is categorized as a Codex Skill with 2.4k GitHub stars.

What programming language is token-optimizer written in?

token-optimizer is primarily written in Python. It covers topics such as agentskills, claude-code, claude-code-skill.

How do I install or use token-optimizer?

You can find installation instructions and usage details in the token-optimizer GitHub repository at github.com/alexgreensh/token-optimizer. The project has 2.4k stars and 180 forks, indicating an active community.

What are the best alternatives to token-optimizer?

The top alternatives to token-optimizer on Agent Skills Hub include cocoindex-code, jcodemunch-mcp, tokscale. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

View on GitHub → Browse Codex Skill tools