by AgentSeal · Claude Skill · ★ 2.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is codeburn safe to install? View the security audit →
CodeBurn See where your AI coding tokens go. By task type, tool, model, MCP server, and project. Supports Claude Code and Codex (OpenAI) with a provider plugin system. Tracks one-shot success rate per activity type so you can see where the AI nails it first try vs. burns tokens on edit/test/fix retries. Interactive TUI dashboard with gradient charts, responsive panels, and keyboard navigation. macOS menu bar widget via SwiftBar. CSV/JSON export. Works by reading session transcripts directly from disk. No wrapper, no proxy, no API keys. Pricing from LiteLLM (auto-cac
npm install -g codeburn
npx codeburnTaken from the project's README on GitHub. Check the security audit before running it.
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 →
| Stars | 2,675 |
| Forks | 184 |
| Language | TypeScript |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 71.8905924288201/100 |
| Open Issues | 16 |
| Last Updated | 2026-04-18 |
| Created | 2026-04-13 |
| Platforms | claude-code, cli, codex, node |
| Est. Tokens | ~217k |
These tools work well together with codeburn for enhanced workflows:
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codeburn is See where your AI coding tokens go. Interactive TUI dashboard for Claude Code, Codex, and Cursor cost observability.. It is categorized as a Claude Skill with 2.7k GitHub stars.
codeburn is primarily written in TypeScript. It covers topics such as ai-coding, claude-code, cli.
The README on GitHub (github.com/AgentSeal/codeburn) gives: npm install -g codeburn , then npx codeburn. Usage details are in the same README.
codeburn is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to codeburn on Agent Skills Hub include codeburn, quint-code, tokscale. 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: