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Agent Skill · 研究检索

proxy-token-optimizer(Agent 令牌优化器)

proxy-token-optimizer 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

proxy-token-optimizer 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 15,653。

总安装 7,688周安装 311GitHub Stars 1下载 2,413核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.1

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:proxy-token-optimizer(Agent 令牌优化器)
来源仓库:https://github.com/whyhit2005/proxy-token-optimizer
安装命令:
openclaw skills install proxy-token-optimizer
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 proxy-token-optimizer

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

proxy-token-optimizer 用于优化 openclaw-manager 平台的 LLM 令牌使用与 API 成本。适用于 OpenClaw 中希望降低大模型调用开销、提升简单任务处理效率的用户。

通过 clawhub 安装后自动路由提示词至低成本模型(如 glm-4.7-flashx),减少 token 消耗。使用前请确认路由策略不影响业务准确性,并监控实际节省效果以调整阈值参数。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install proxy-token-optimizer;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

proxy-token-optimizer 怎么安装?

在终端执行 openclaw skills install proxy-token-optimizer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

proxy-token-optimizer 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

proxy-token-optimizer 需要什么权限?

权限级别是需要联网。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。

proxy-token-optimizer 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

proxy-token-optimizer 用的人多吗?

公开数据是累计安装 7,688 次,近一周安装 311 次,下载 2,413 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

proxy-token-optimizer 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
proxy-token-optimizer

Agent 令牌优化器 · 本页

7,6881需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
proxy-token-optimizer
description
|
metadata
{"openclaw": {"always": true}}

Proxy Token Optimizer

Reduces LLM API costs for the openclaw-manager multi-tenant proxy platform through four strategies:

  1. Model-tier routing — Route prompts to the cheapest capable model
  2. Heartbeat optimization — Cheapest model + longer intervals for heartbeat calls
  3. Context lazy loading — Load only the context files each prompt actually needs
  4. Platform usage analytics — Real data from PostgreSQL, not estimates

Why these strategies matter

The openclaw-manager platform proxies LLM requests for multiple OpenClaw instances through providers like zai-proxy, zai-coding-proxy, and kimi-coding-proxy. Each provider offers models at different price points (e.g., glm-4.7 vs glm-4.7-flashx). Without optimization, every request — including simple greetings and heartbeat pings — uses the default (expensive) model, and every session loads the full context regardless of need. These four strategies target the highest-impact cost drivers.

Quick start

All instance-side scripts run locally with no dependencies. Platform-side scripts need DB access.

# Model routing — which model should handle this prompt?
python3 scripts/model_router.py "thanks!"
# → {"tier": "cheap", "recommended_model": "zai-proxy/glm-4.7-flashx"}

# Context optimization — which files does this prompt need?
python3 scripts/context_optimizer.py recommend "hi"
# → {"context_level": "minimal", "recommended_files": ["SOUL.md", "IDENTITY.md"]}

# Heartbeat config — generate openclaw.json patch
python3 scripts/heartbeat_config.py patch
# → {"agents": {"defaults": {"heartbeat": {"every": "55m", "model": "zai-proxy/glm-4.7-flashx"}}}}

# Unified CLI (all commands in one place)
python3 scripts/cli.py --help

Scripts reference

Instance-side (pure local, no network, no DB)

scripts/model_router.py

Routes prompts to the right model tier based on complexity analysis.

Tier logic:

  • cheap → glm-4.7-flashx: Greetings, acknowledgments, heartbeats, cron jobs, log parsing. Cost savings: 5-10x vs standard.
  • standard → glm-4.7: Code writing, debugging, explanations. Default for unclear prompts.
  • premium → glm-4.7 (or k2p5 for kimi): Architecture design, deep analysis, strategy planning.

Supports Chinese and English patterns. Provider-aware — works with zai-proxy, zai-coding-proxy, and kimi-coding-proxy.

python3 scripts/model_router.py "<prompt>" [provider]
python3 scripts/model_router.py compare  # show all provider models
scripts/context_optimizer.py

Analyzes prompt complexity to recommend which context files to load, reducing unnecessary token consumption.

Context levels:

LevelWhenFiles loadedToken savings
minimal"hi", "thanks", short msgsSOUL.md + IDENTITY.md (2)~80%
standard"write a function", normal work+ memory/TODAY.md + conditional~50%
full"design architecture", complex tasks+ MEMORY.md + all conditional~30%

Also generates an optimized AGENTS.md template with lazy-loading rules baked in:

python3 scripts/context_optimizer.py recommend "<prompt>"
python3 scripts/context_optimizer.py generate-agents  # creates AGENTS.md.optimized
scripts/heartbeat_config.py

Generates openclaw.json configuration patches for heartbeat optimization:

  • Forces heartbeat model to glm-4.7-flashx (cheapest available)
  • Sets interval to 55 minutes (keeps prompt cache warm within 1-hour TTL, avoids cache rebuild cost)
python3 scripts/heartbeat_config.py recommend [cache_ttl_minutes]
python3 scripts/heartbeat_config.py patch  # output JSON patch for openclaw.json

Platform-side (requires DB connection)

These scripts query the usage_records PostgreSQL table for real data. Run from the openclaw-manager project root with the virtualenv activated.

scripts/usage_report.py

Generates usage reports from actual database records — not estimates.

python3 scripts/usage_report.py overview [days]     # platform-wide summary
python3 scripts/usage_report.py instance <name> [days]  # single instance detail

Overview includes: total calls/tokens, per-provider breakdown, per-model breakdown, top 10 instances by consumption, 7-day daily trend.

Instance report includes: per-model distribution, daily trend, lifetime totals.

scripts/quota_advisor.py

Compares actual 24-hour usage against quota plan limits to find mismatches:

  • Wasteful: Usage below 20% of plan limit → suggest downgrade
  • Throttled: Usage above 80% of plan limit → suggest upgrade
python3 scripts/quota_advisor.py analyze  # check all instances
python3 scripts/quota_advisor.py plans    # show available quota plans

Unified CLI

scripts/cli.py wraps all the above into a single entry point:

python3 scripts/cli.py route "<prompt>"       # model routing
python3 scripts/cli.py context "<prompt>"     # context recommendation
python3 scripts/cli.py generate-agents        # generate AGENTS.md
python3 scripts/cli.py heartbeat              # heartbeat config
python3 scripts/cli.py overview [days]        # platform usage (needs DB)
python3 scripts/cli.py report <name> [days]   # instance report (needs DB)
python3 scripts/cli.py advisor                # quota advice (needs DB)

Project integration points

This skill works with existing openclaw-manager infrastructure:

ComponentFileHow this skill uses it
Provider configconfig/model.yamlModel names/endpoints for routing
Proxy routingconfig_service.pyWhere _inject_proxy_providers() registers models
Usage recordingproxy_common/usage_recorder.pySource of real usage data
Quota plansconfig/llm_proxy.yamlPlan definitions for quota advisor
Instance modelapp/models.pyInstance metadata for reports

Expected savings

StrategyMechanismImpact
Context lazy loadingFewer tokens per request50-80% context reduction
Model routing (flashx)Lower per-token price5-10x on simple tasks
Heartbeat → flashxLower heartbeat costSignificant per-instance savings
Heartbeat interval 55minFewer API calls~45% fewer heartbeat calls

平台分布

OpenClaw

71.1%
按下载量换算1,716

安全审计

VirusTotal

可疑

ClawScan

可疑

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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