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 egorfedorov · Agent Tool · ★ 111
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is claude-context-optimizer safe to install? View the security audit →
Stop burning tokens on weak prompts and redundant reads. Tuned for Claude Opus 4.7 — including the 1M-context tier. The Problem The average Claude Code session wastes 30-50% of tokens on files that are read but never actually used. Every call consumes context — whether the file was relevant or not. A 200-line config file? 800 tokens gone. A README you glanced at once? 2,400 tokens burned. That Claude reads "just
| Stars | 111 |
| Forks | 11 |
| Language | JavaScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 73.1409807490205/100 |
| Last Updated | 2026-09-15 |
| Created | 2026-03-09 |
| Platforms | claude-code, cli, node |
| Est. Tokens | ~18k |
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claude-context-optimizer is Claude Code plugin that tracks token usage, identifies wasted context, and saves 30-50% on API costs. Heatmaps, ROI reports, budget alerts, efficiency scores, git-aware suggestions — all local, zero c. It is categorized as a Agent Tool with 111 GitHub stars.
claude-context-optimizer is primarily written in JavaScript. It covers topics such as ai-tools, anthropic, claude-code.
You can find installation instructions and usage details in the claude-context-optimizer GitHub repository at github.com/egorfedorov/claude-context-optimizer. The project has 111 stars and 11 forks, indicating an active community.
claude-context-optimizer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to claude-context-optimizer on Agent Skills Hub include claude-code-hooks, claude-cognitive, navigator. 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: