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 tigerless-labs · Codex Skill · ★ 629
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is cost-xray safe to install? View the security audit →
cost-xray See what your AI coding agent actually sends to the API — and what each part costs. Most usage tools read local logs. That shows the total cost of a call or session, but it misses the request-time context assembled before the model is invoked: system prompts, tool schemas, MCP blocks, tool results, cache reads/writes, and previous thinking blocks. cost-xray captures the actual local API traffic for Claude Code and Codex, then attributes tokens and dollars back to the sources inside the request. It shows not just how much a turn cost, but why it cost that much. Home — every session by agent · project · session, with token and dollar totals Drill a session — context-win
| Stars | 629 |
| Forks | 80 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 73.6063391963271/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-01 |
| Created | 2026-06-06 |
| Platforms | claude-code, cli, codex, python |
| Est. Tokens | ~16k |
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cost-xray is See what Claude Code and Codex actually send to the API — and what each part costs.. It is categorized as a Codex Skill with 629 GitHub stars.
cost-xray is primarily written in Python. It covers topics such as claude-code, codex-cli, cost-tracking.
You can find installation instructions and usage details in the cost-xray GitHub repository at github.com/tigerless-labs/cost-xray. The project has 629 stars and 80 forks, indicating an active community.
cost-xray is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to cost-xray on Agent Skills Hub include inferock-bench, clawmetry, ai-observer. 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: