跳到正文
cccskills
免费在 GitHub 公开中文介绍

cost-tracking

从Claude Code的本地使用日志汇总令牌用量和美元预估费用,按模型、会话或日期查看支出,比较预算并导出CSV,避免重复计算会话累计值。

原文查看原文说明

Track and report Claude Code token usage, spending, and budgets from the local ECC cost-tracker metrics log. Use when the user asks about costs, spending, usage, tokens, budgets, or cost breakdowns by model, session, or date.

AI 评分90分/ 100查看安装方法

适用场景

  • 比较今天与昨天的支出
  • 按模型和会话查看费用
  • 比较预算与实际支出
  • 将使用记录导出为CSV

中文介绍

功能

cost-tracking从Claude Code的使用历史汇总令牌用量和以美元计价的预估费用。它读取ECC的stop:cost-tracker写入的本地日志,报告今天与昨天的支出、全部会话的费用总额、各模型的费用明细和会话数量。也可以按日期或会话查看,并将使用记录导出为CSV。

适用场景

适合想了解费用增长情况,或确认哪些模型、哪些任务产生费用的人。需要比较预算与支出,或将使用历史交给其他汇总分析工作时,也可以使用。

使用示例

  • “汇总今天、昨天的费用,以及目前的总费用。”
  • “按模型和会话查看费用。”
  • “将使用历史导出为CSV。”

注意事项

需要Node.js和~/.claude/metrics/costs.jsonl日志。启用钩子并结束第一个会话后,日志才会开始积累。每一行都是该会话的累计值,因此计算总额时只采用各会话的最新一行。费用优先使用日志中的estimated_cost_usd字段;日志不存在时,不会推测用量。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

客观评估

AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。

指令清晰度能否看出何时使用、做什么
A良好明确以费用、令牌和预算问题为使用场景,并说明日志汇总内容。
步骤具体性步骤、输入输出和示例是否具体
A良好提供检查日志和汇总的node命令、最新行选择规则及报告字段。
安全性发送数据、删除文件、执行命令等是否有说明
A良好示例node命令仅检查、读取本地日志并显示结果。
安装难易度所需工具和 API 密钥是否少且有说明
B部分不足需要node、日志及已启用的钩子,但未提供安装或启用步骤。

总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月19日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(1)

  • SKILL.md5.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Cost Tracking

Use this skill to analyze Claude Code cost and usage history from the metrics log that ECC's stop:cost-tracker hook writes.

Where the data lives

The tracker appends one JSON object per session-stop to ~/.claude/metrics/costs.jsonl. Each row is a cumulative snapshot for that session, so to total spend you take the latest row per session_id and sum across sessions — summing every row multiply-counts.

ECC also maintains internal per-session files under ~/.claude/metrics/cost-snapshots/ so runtime hooks can read the current session total without rescanning all history. Treat those files as a rebuildable cache; each snapshot stores a byte cursor so only newly appended rows are scanned. Stable reads are O(1), while updates are O(new bytes). Stale entries are pruned after 30 days or when the directory exceeds 512 sessions. Cold catch-up work is limited to 16 MiB per hook invocation, and malformed unterminated rows larger than 1 MiB are discarded with a resumable cursor. Reports and exports should continue to use costs.jsonl.

Row schema:

FieldMeaning
timestampISO timestamp of the snapshot
session_idClaude Code session identifier
transcript_pathPath to the session transcript
modelModel used
input_tokens / output_tokensToken counts
cache_write_tokens / cache_read_tokensPrompt-cache token counts
estimated_cost_usdPrecomputed cumulative cost in USD for the session

Prefer estimated_cost_usd over hand-calculating pricing — model and cache prices change, and the tracker is the source of truth.

When to Use

  • The user asks "how much have I spent?", "what did this session cost?", or "what is my token usage?"
  • The user mentions budgets, spending limits, overruns, or cost controls.
  • The user wants a cost breakdown by model, session, or date, or a CSV export.

How It Works

First verify the log exists (use node, not sqlite3 — the tracker writes JSONL, and node is cross-platform):

node -e 'const fs=require("fs"),os=require("os"),p=require("path");const f=p.join(os.homedir(),".claude","metrics","costs.jsonl");console.log(fs.existsSync(f)?"cost log found":"cost log not found: "+f)'

If the log is missing, do not fabricate usage data. Tell the user that cost tracking populates after the first session ends with the stop:cost-tracker hook enabled.

Example — summary, by model, last 7 days

node -e '
const fs=require("fs"),os=require("os"),path=require("path");
const f=path.join(os.homedir(),".claude","metrics","costs.jsonl");
if(!fs.existsSync(f)){console.log("cost log not found: "+f);process.exit(0);}
const rows=fs.readFileSync(f,"utf8").split(/\r?\n/).filter(Boolean).map(l=>{try{return JSON.parse(l)}catch{return null}}).filter(Boolean);
const bySession=new Map();
for(const r of rows){const k=r.session_id||r.transcript_path||r.timestamp;const p=bySession.get(k);if(!p||String(r.timestamp)>String(p.timestamp))bySession.set(k,r);}
const latest=[...bySession.values()];
const cost=r=>Number(r.estimated_cost_usd)||0, day=r=>String(r.timestamp||"").slice(0,10), sum=a=>a.reduce((s,r)=>s+cost(r),0), f4=n=>"$"+n.toFixed(4);
const today=new Date().toISOString().slice(0,10), yest=new Date(Date.now()-864e5).toISOString().slice(0,10);
console.log("today: "+f4(sum(latest.filter(r=>day(r)===today)))+" | yesterday: "+f4(sum(latest.filter(r=>day(r)===yest)))+" | total: "+f4(sum(latest))+" ("+latest.length+" sessions)");
const m=new Map();for(const r of latest){const k=r.model||"(unknown)";m.set(k,(m.get(k)||0)+cost(r));}
console.log("by model:");[...m.entries()].sort((a,b)=>b[1]-a[1]).forEach(([k,v])=>console.log("  "+f4(v)+"  "+k));
'

For a session drilldown or CSV export, iterate the same latest set (or the raw rows for CSV) and print the fields you need.

Reporting Guidance

When presenting cost data, include today's spend vs yesterday, total across all sessions, a by-model breakdown, and session count. Format sub-dollar amounts with four decimals, larger amounts with two.

Anti-Patterns

  • Do not sum every row — they are cumulative per session; reduce to the latest row per session_id first.
  • Do not estimate costs from raw token counts when estimated_cost_usd is present.
  • Do not assume the log exists without checking.
  • Do not hard-code current model pricing in user-facing answers.
  • Do not recommend installing unreviewed hooks or plugins that execute arbitrary code.

Related

  • /cost-report - Command-form report over the same metrics log.
  • cost-aware-llm-pipeline - Model-routing and budget-design patterns.
  • token-budget-advisor - Context and token-budget planning.
  • strategic-compact - Context compaction to reduce repeated token spend.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.

中文简介正在准备中。当前显示英语简介。

  • Adding descriptive labels to icon-only buttons
  • Reviewing keyboard navigation and modal focus
  • Checking contrast and control sizes
affaan-m/ECC28.3万2026年10月16日 更新

Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.

中文简介正在准备中。当前显示英语简介。

  • Investigating failures that appear only inside an agent application
  • Tracing old conversation content leaking into new sessions
  • Checking skipped or falsely claimed tool execution
affaan-m/ECC28.3万2026年10月16日 更新

agent-eval

免费有简介AI 评分 80分

用实际开发任务比较多个编程智能体,记录成功率、可获取的API费用、耗时和重复运行的稳定性,为工具选型及更新后的性能评估提供数据。

  • 用实际开发任务比较智能体
  • 采用新工具或模型前进行评估
  • 检查智能体更新后的性能
affaan-m/ECC28.3万2026年10月16日 更新

Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.

中文简介正在准备中。当前显示英语简介。

  • Define an AI agent’s tool set and input formats
  • Make tool results and next steps easier to interpret
  • Specify safe retries and stopping conditions
affaan-m/ECC28.3万2026年10月16日 更新

Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.

中文简介正在准备中。当前显示英语简介。

  • Diagnosing repeated failures in an agent run
  • Investigating loops through the same tool calls
  • Reviewing output degradation as context grows
affaan-m/ECC28.3万2026年10月5日 更新

agent-introspection-debugging

免费有简介AI 评分 100分

在AI代理反复失败或重复操作时,记录错误与环境前提、排查原因,采取影响有限的恢复操作,并汇总结果和证据,为有依据的重试与交接提供支持。

  • 排查重复调用工具的原因
  • 调查对话膨胀导致的质量下降
  • 核对文件路径与环境差异
affaan-m/ECC28.3万2026年10月16日 更新

查看 affaan-m 的全部技能

报告此技能的问题