Agent Skills › vivekchand/clawmetry › clawmetry-selfcheck

clawmetry-selfcheck

GitHub

用于在任务结束前通过 ClawMetry CLI 检查 AI Agent 的运行遥测数据，包括文件重复读取、循环执行及预算消耗，以优化行为并避免资源浪费。

docs/skills/clawmetry-selfcheck/SKILL.md vivekchand/clawmetry

Trigger Scenarios

Agent 需要自我监控运行效率时 Agent 怀疑陷入重复操作或循环时 Agent 需要检查 Token 消耗和预算使用情况时

Install

npx skills add vivekchand/clawmetry --skill clawmetry-selfcheck -g -y

More Options

Non-standard path

npx skills add https://github.com/vivekchand/clawmetry/tree/main/docs/skills/clawmetry-selfcheck -g -y

Use without installing

npx skills use vivekchand/clawmetry@clawmetry-selfcheck

指定 Agent (Claude Code)

npx skills add vivekchand/clawmetry --skill clawmetry-selfcheck -a claude-code -g -y

安装 repo 全部 skill

npx skills add vivekchand/clawmetry --all -g -y

预览 repo 内 skill

npx skills add vivekchand/clawmetry --list

SKILL.md

Frontmatter

{
    "name": "clawmetry-selfcheck",
    "description": "Read your own agent telemetry from ClawMetry (waste, progress, cost) and act on it before finishing a task. Use when ClawMetry is installed on this machine and you want to check whether you are re-reading files, spinning in loops, or burning budget."
}

ClawMetry self-check

ClawMetry observes AI agent sessions on this machine (read-only). Use its CLI to see your own telemetry and improve your behaviour mid-task.

All commands are read-only, exit 3 when ClawMetry has no data source running (safe to skip silently), and support --json.

The 3-step check (run before finishing a task)

# 1. Re-read tax: files you paid to read more than once
clawmetry waste --since 6h --json

# 2. Spinning check: forward progress + loop signals
clawmetry progress --json

# 3. Spend check
clawmetry usage --json

How to act on what you find

Signal	Action
A file with `reads > 3` in `waste`	Stop re-reading it. Write its structure into your working notes once; use offset-ranged reads for big files.
High `ratio` (tokens per state-delta) in `progress`	You are burning tokens without producing new state. Write down what you tried, change strategy.
`loop_signals` non-empty	You repeated near-identical calls. Do not retry the same thing; diagnose instead.
Cost spike in `usage`	Check `usage --by model`: are you using a bigger model than the task needs?

Digging deeper

clawmetry sessions --active                  # who is running
clawmetry sessions <SID> --transcript        # what actually happened
clawmetry sessions <SID> --cost              # where the money went (incl. sub-agents)
clawmetry sessions <SID> --errors            # error events only
clawmetry activity --follow --max-events 50  # live NDJSON event stream
clawmetry usage --efficiency                 # A-F efficiency grade

Exit codes: 0 ok, 3 no data source (skip), 4 feature needs an upgrade (stderr carries a JSON body with upgrade_url; surface it to your human).