Skillget

codeburn

skill
Guvenlik Denetimi
Basarisiz
Health Gecti
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Community trust — 376 GitHub stars
Code Basarisiz
  • exec() — Shell command execution in src/bash-utils.ts
  • process.env — Environment variable access in src/dashboard.tsx
  • execSync — Synchronous shell command execution in src/menubar.ts
  • process.env — Environment variable access in src/menubar.ts
  • network request — Outbound network request in src/models.ts
Permissions Gecti
  • Permissions — No dangerous permissions requested
Purpose
This tool provides an interactive terminal dashboard for tracking AI coding token usage and costs. It works by reading local session transcript files directly from your disk, categorized by task type, model, and project, with no need for a proxy or API keys.

Security Assessment
The overall risk is Medium. The tool accesses environment variables in its dashboard and menubar modules, which is common for TUI applications. However, there are two notable concerns: it performs shell command execution (both asynchronous and synchronous) in its bash and menubar utilities. Additionally, it makes outbound network requests in the models module—likely to fetch auto-cached pricing data from LiteLLM as mentioned in the documentation. No hardcoded secrets or dangerous permissions were found. While the core functionality safely reads local files, the inclusion of remote network calls and shell execution requires careful user discretion.

Quality Assessment
The project demonstrates strong health indicators. It is actively maintained (last pushed 0 days ago), has a clear description, and holds a standard MIT license. With nearly 400 GitHub stars, it has achieved a solid baseline of community trust and validation from users.

Verdict
Use with caution—the read-only file approach is safe, but you should review the shell execution and outbound network logic before running.
SUMMARY

See where your AI coding tokens go. Interactive TUI dashboard for Claude Code and Codex cost observability.

README.md

CodeBurn

CodeBurn

See where your AI coding tokens go.

npm version npm downloads license node version

CodeBurn TUI dashboard

By task type, tool, model, MCP server, and project. 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 Claude Code session transcripts directly from disk. No wrapper, no proxy, no API keys. Pricing from LiteLLM (auto-cached, all models supported).

Install

npm install -g codeburn

Or run without installing:

npx codeburn

Requirements

  • Node.js 20+
  • Claude Code (reads ~/.claude/projects/ session data)

Usage

codeburn                    # interactive dashboard (default: 7 days)
codeburn today              # today's usage
codeburn month              # this month's usage
codeburn report -p month    # same as above
codeburn status             # compact one-liner (today + month)
codeburn status --format json
codeburn export             # CSV with today, 7 days, 30 days
codeburn export -f json     # JSON export

Arrow keys switch between Today / 7 Days / Month. Press q to quit, 1 2 3 as shortcuts.

Menu Bar

CodeBurn SwiftBar menu bar widget 
codeburn install-menubar    # install SwiftBar/xbar plugin
codeburn uninstall-menubar  # remove it

Requires SwiftBar (brew install --cask swiftbar). Shows today's cost in the menu bar with a flame icon. Dropdown shows activity breakdown, model costs, and token stats for today, 7 days, and month. Refreshes every 5 minutes.

What it tracks

13 task categories classified from tool usage patterns and user message keywords. No LLM calls, fully deterministic.

Category What triggers it
Coding Edit, Write tools
Debugging Error/fix keywords + tool usage
Feature Dev "add", "create", "implement" keywords
Refactoring "refactor", "rename", "simplify"
Testing pytest, vitest, jest in Bash
Exploration Read, Grep, WebSearch without edits
Planning EnterPlanMode, TaskCreate tools
Delegation Agent tool spawns
Git Ops git push/commit/merge in Bash
Build/Deploy npm build, docker, pm2
Brainstorming "brainstorm", "what if", "design"
Conversation No tools, pure text exchange
General Skill tool, uncategorized

Breakdowns: daily cost chart, per-project, per-model (Opus/Sonnet/Haiku/GPT-4o/Gemini), per-activity with one-shot rate, core tools, MCP servers.

One-shot rate: For categories that involve code edits, CodeBurn detects edit/test/fix retry cycles (Edit -> Bash -> Edit patterns). The 1-shot column shows the percentage of edit turns that succeeded without retries. Coding at 90% means the AI got it right first try 9 out of 10 times.

Pricing: Fetched from LiteLLM model prices (auto-cached 24h at ~/.cache/codeburn/). Handles input, output, cache write, cache read, and web search costs. Fast mode multiplier for Claude. Fallback to hardcoded prices if fetch fails.

How it reads data

Claude Code stores session transcripts as JSONL at ~/.claude/projects/<sanitized-path>/<session-id>.jsonl. Each assistant entry contains model name, token usage (input, output, cache read, cache write), tool_use blocks, and timestamps.

CodeBurn reads these files, deduplicates messages by API message ID (prevents double-counting across sessions), filters by date range per entry (not per session), and classifies each turn.

Project structure

src/
  cli.ts          Commander.js entry point
  dashboard.tsx   Ink TUI (React for terminals)
  parser.ts       JSONL reader, dedup, date filter
  models.ts       LiteLLM pricing, cost calculation
  classifier.ts   13-category task classifier
  types.ts        Type definitions
  format.ts       Text rendering (status bar)
  menubar.ts      SwiftBar plugin generator
  export.ts       CSV/JSON multi-period export

License

MIT

Credits

Inspired by ccusage. Pricing data from LiteLLM.

Built by AgentSeal.

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