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claude-usage-analyst

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.

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daymade/claude-code-skills
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Files included (4)

  • SKILL.md2.8 KB
  • agents/openai.yaml228 B
  • references/explanation-guide.md1.6 KB
  • scripts/analyze_claude_usage.py10.9 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

  1. Verify ccusage is available:

    ccusage --version

    If missing, install or update with npm install -g ccusage@latest or run with npx ccusage@latest.

  2. 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.

  3. 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

  4. 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:

  1. Short conclusion in plain language.
  2. Evidence table: total tokens, cost, input, output, cache create, cache read.
  3. Model comparison table.
  4. 5-hour block table when quota exhaustion is discussed.
  5. Explanation of why the burn happened.
  6. Confidence and caveats.

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

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