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whereamiburningtokenswhereamiburningtokens 效率

Agent Skill

whereamiburningtokens 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

4,368

周安装

175

GitHub Stars

2

下载量

1,414
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:whereamiburningtokens(whereamiburningtokens 效率)
来源仓库:https://github.com/whooshinglander/whereamiburningtokens
安装命令:
openclaw skills install whereamiburningtokens
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install whereamiburningtokens

简介

whereamiburningtokens 用于只读查看 OpenClaw 会话中令牌消耗与成本分布情况。

  • 适合监控资源使用情况,辅助优化对话策略与成本控制。
  • 通过 clawhub 安装后可直接读取本地 sessions.json 文件,无需额外配置。
  • 仅提供诊断信息,不涉及任何写入或修改操作,安全性较高。
  • 数据更新可能存在延迟,建议定期手动核对以获取准确开销统计。

SKILL.md

name
whereamiburningtokens
description
Reads only ~/.openclaw/agents/main/sessions/sessions.json to show exactly where OpenClaw tokens and estimated cost are going by session type. Read-only diagnostic skill, no writes, no deletes, no command execution, no network exfiltration.

whereamiburningtokens

Reads only ~/.openclaw/agents/main/sessions/sessions.json and shows a token + cost breakdown by session category.

Safety boundary

  • Read-only skill
  • Reads only ~/.openclaw/agents/main/sessions/sessions.json
  • Does not read any other local files unless the user explicitly asks for the optional improvement log
  • Does not modify, delete, or execute anything
  • Does not send local data anywhere

Data source

~/.openclaw/agents/main/sessions/sessions.json

Each session entry has:

  • totalTokens, inputTokens, outputTokens, cacheRead, cacheWrite
  • estimatedCostUsd
  • model, modelProvider
  • startedAt, updatedAt (Unix ms timestamps)

Session keys: agent:main:<category>:<optional-id>

Categories (3rd segment of key):

  • main — interactive chat
  • cron — heartbeats + scheduled tasks
  • subagent — spawned sub-agents
  • paperclip — Paperclip logging (if installed)
  • anything else — plugins, integrations

Time windows

Detect from user's phrasing:

  • "this week" / default → last 7 days
  • "today" → last 24 hours
  • "this month" → last 30 days
  • "all time" / "all-time" → no filter

Filter by updatedAt >= cutoff_ms.

Steps

  1. Read and parse sessions.json. If missing, say so and stop.
  1. Filter by time window based on user's phrasing.
  1. Group by category (3rd : segment of key). Sum totalTokens and estimatedCostUsd.
  1. Sort by tokens descending. Calculate % of total for both tokens and cost.
  1. Flag anomalies:

- ⚠️ SINKHOLE: category >40% tokens but <15% cost (cheap model, high volume — likely a logging/cron drain) - ⚠️ EXPENSIVE: non-main category >35% cost but <15% tokens (expensive model, few calls — check model config)

  1. Output the table and 1-2 insight lines.

Output format

🔥 WHERE AM I BURNING TOKENS? (last 7 days)
66 sessions | 2.8M tokens | $100.65 est.

Category        Sess    Tokens    Tok%     Cost   Cost%
──────────────────────────────────────────────────────────
paperclip         26      997k   36.1%  $  5.79    5.8%  ⚠️ SINKHOLE
subagent          22      795k   28.8%  $  9.49    9.4%
cron              16      692k   25.1%  $ 48.61   48.3%  ⚠️ EXPENSIVE
main               1      274k   10.0%  $ 36.76   36.5%

💡 paperclip is eating 36% of tokens on a cheap model.
   High volume, low cost = lots of context for little output. Consider disabling.
💡 cron costs 48% of spend. Verify heartbeat model is Haiku or a local model, not Sonnet.

Format token counts: 1.2M / 692k / 344. Keep table tight, no padding.

Improvement log (optional)

Only if the user explicitly asks to log an improvement or track savings:

  • Read/create ~/.openclaw/workspace/memory/token-diet-log.md
  • Append entry: date, what changed, token % before/after, cost before/after
  • Show running total saved

Format:

## Token Diet Log
| Date | Change | Tokens Before | Tokens After | Cost Saved/wk |
|---|---|---|---|---|
| 2026-04-05 | Disabled Paperclip | 68% | 36% | ~$5.79 |

Notes

  • estimatedCostUsd is OpenClaw's estimate, not exact billing
  • main being expensive is expected (interactive Sonnet sessions), don't flag it
  • Sessions.json is cumulative, grows over time, no automatic reset
  • Do not read individual session .jsonl files, sessions.json has everything needed
  • Do not expand beyond the declared files above unless the user explicitly asks for the optional log

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

71.63%
按下载量换算1,013

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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