claude-usage-analyst
- 作者仓库星标 0
- 作者更新于 2026年8月24日 15:33
- 作者仓库 claude-code-skills
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
ccusageis available:ccusage --versionIf missing, install or update with
npm install -g ccusage@latestor run withnpx 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/ShanghaiDefault
--since/--untilis today in the selected timezone. For historical comparison, set--sinceto 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-4-8Read
references/explanation-guide.mdwhen writing the final answer.
Evidence Rules
- Base numeric claims on
ccusageoutput or the bundled analyzer output. - State the scope:
ccusage claudemeasures 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.
- 流狐分类
- 通用
- 作者声明 Agent
- 未找到明确声明；不据此推断已兼容或已测试
- 静态检查
- 88 / 100 · 启发式扫描，不代表运行安全
- 作者 / 版本 / 许可
- @daymade · 未声明 license
- 流狐 Token 估算
- 低消耗
- 流狐接入估算
- 即装即用
- 是否需要外部 API Key
- 未发现要求
- 检测到的系统要求
- macOS · Linux · Windows
- 底层运行要求
- 未声明
- 检测到的文件与系统行为
-
- 只读
- 允许写入 / 修改
- 检测到的网络行为
- 仅限本地
- 安装命令数
- 无（仅作为资料）
档案由构建时根据 SKILL.md 与安装命令自动衍生，可能与作者实际意图存在差异。
需要注意： 未限定 allowed-tools，默认拥有全部工具权限。
作者没有在当前 SKILL.md 中定义固定输出样例。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.
Verify ccusage is available: 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:
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…
Use this structure unless the user asks otherwise: Short conclusion in plain language. Evidence table: total tokens, cost, input, output, cache create, cache read.
# 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:
```bash
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:
```bash
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:
```bash
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.
…作者原文负责流程事实；流狐只索引当前章节、要点、文件与命令。
章节 -> Overview → Workflow → Evidence Rules → Output Shape
要点 -> Use this skill to produce evidence-based usage explanations from local ccusage data. · 4. Read references/explanation-guide.md when writing the final answer. · - Base numeric claims on ccusage output or the bundled analyzer output. · 1. Short conclusion in plain language. · Keep the answer readable for non-technical users.
文件/命令 -> ccusage · npm install -g ccusage@latest · npx ccusage@latest · --since/--until · --since · references/explanation-guide.md · ccusage claude
内容 SHA-256 -> ff3d52b15202
方法与流程
适用与边界
原文中的明确线索
ccusage、npm install -g ccusage@latest、npx ccusage@latest、--since/--until、--since、references/explanation-guide.md、ccusage claude