Token and cost reports for 17 AI coding agents (Claude Code, Codex, Gemini CLI, OpenCode, Copilot, pi and more), read...
Copy the install, test the workflow, then decide if it earns a permanent slot.
Fresh repo activity plus visible builder pull. This is the kind of tool people test before it turns obvious.
Copy the install, test the workflow, then decide if it earns a permanent slot.
You can test this quickly and remove it cleanly if it misses.
GitHub health 57/100. no security policy. Fresh enough repo health and manageable issue load keep the risk controlled.
AI Agent
Codex
Model
Gemini
Fastest way to find out if llm-usage-metrics belongs in your setup.
Copy the install command, run a real test, and back it out cleanly if it slows you down.
git clone https://github.com/ayagmar/llm-usage-metrics ~/.claude/commands/llm-usage-metricsRun this first. You will know quickly if the workflow earns a permanent slot.
rm -rf ~/.claude/commands/llm-usage-metricsNo messy cleanup loop. If it misses, remove it and keep moving.
Install Location
~/ └─ .claude/ ├─ commands/ │ └─ llm-usage-metrics/ ← installs here ├─ skills/ ├─ agents/ └─ settings.json
Token and cost reports for 17 AI coding agents (Claude Code, Codex, Gemini CLI, OpenCode, Copilot, pi and more), read from local session logs across all your machines
Source: GitHub repository
Source check: October 23, 2026
Upstream commit: October 23, 2026
Repository state: Not marked archived
Honeystax upvotes are community interest signals, not star ratings. GitHub stars and repository health are source measurements; editorial risk and trial-cost notes are Honeystax analysis.