Agent Skills › daymade/claude-code-skills › claude-usage-analyst

claude-usage-analyst

GitHub

分析本地 Claude Code 和 Desktop 的 Token 用量、成本、配额消耗及模型混合情况。通过 ccusage 数据解释配额耗尽原因，提供模型对比与缓存使用详情，帮助用户理解资源消耗。

daymade-claude-code/claude-usage-analyst/SKILL.md daymade/claude-code-skills

Trigger Scenarios

用户询问为何 Claude 配额耗尽比较特定模型如 fable/opus/sonnet 的成本查询今日或历史 Token 消耗量需要本地 CLI/Desktop 用量的通俗解释

Install

npx skills add daymade/claude-code-skills --skill claude-usage-analyst -g -y

More Options

Non-standard path

npx skills add https://github.com/daymade/claude-code-skills/tree/main/daymade-claude-code/claude-usage-analyst -g -y

Use without installing

npx skills use daymade/claude-code-skills@claude-usage-analyst

指定 Agent (Claude Code)

npx skills add daymade/claude-code-skills --skill claude-usage-analyst -a claude-code -g -y

安装 repo 全部 skill

npx skills add daymade/claude-code-skills --all -g -y

预览 repo 内 skill

npx skills add daymade/claude-code-skills --list

SKILL.md

Frontmatter

{
    "name": "claude-usage-analyst",
    "description": "Analyze Claude Code and Claude Desktop Code token usage, cost, quota burn, model mix, cache read\/write, and 5-hour block consumption using ccusage evidence. Use when the user asks why Claude quota was exhausted, whether a model such as fable\/opus\/sonnet is unusually expensive, how many tokens were spent today or historically, or needs a human-friendly explanation of local Claude Code CLI\/Desktop usage."
}

Claude Usage Analyst

Overview

Use this skill to produce evidence-based usage explanations from local ccusage data. Separate observed numbers from interpretation, and explain quota burn in human terms.

Workflow

Verify ccusage is available:
```
ccusage --version
```
If missing, install or update with npm install -g ccusage@latest or run with npx ccusage@latest.
Run the bundled analyzer for the requested window:
```
python3 /path/to/claude-usage-analyst/scripts/analyze_claude_usage.py \
  --since YYYY-MM-DD --until YYYY-MM-DD --timezone Asia/Shanghai
```
Default --since/--until is today in the selected timezone. For historical comparison, set --since to an earlier date such as the first day of the month; otherwise rank/median fields only describe the single target day.

If the user asks about a specific model comparison, pass aliases:

python3 scripts/analyze_claude_usage.py --model-a fable --model-b opus-4-8

Read references/explanation-guide.md when writing the final answer.

Evidence Rules

Base numeric claims on ccusage output or the bundled analyzer output.
State the scope: ccusage claude measures local Claude Code usage logs, including Claude Desktop's Claude Code sessions when those local logs exist. It is not a complete ordinary Claude.ai chat bill.
Report dates with timezone.
Explain cache clearly: cache read tokens are still usage/quota pressure even though the user did not type those words.
Do not infer Anthropic plan quota rules from local token counts unless the user provides plan details. Say "quota-like pressure" or "ccusage estimated cost/token burn" when exact plan accounting is unknown.
When comparing models, compare both token volume and estimated cost. A model can have similar token volume but higher cost.

Output Shape

Use this structure unless the user asks otherwise:

Short conclusion in plain language.
Evidence table: total tokens, cost, input, output, cache create, cache read.
Model comparison table.
5-hour block table when quota exhaustion is discussed.
Explanation of why the burn happened.
Confidence and caveats.

Keep the answer readable for non-technical users. Avoid unexplained terms like "cache read" without a one-sentence translation.

Version History

e00a2ec Current 2026-08-20 11:26

Metadata

Files: 0
Version: e00a2ec
Hash: 2937e96a
Indexed: 2026-08-20 11:26

claude-usage-analyst