查询 GLM 编码套餐使用统计的 OpenClaw 技能。
OpenClaw skill for querying GLM coding plan usage statistics.
Quota Monitoring: View token usage (5-hour) and MCP usage (1-month)
Model Usage: Display token count and call count within 24 hours
Tool Usage: Track MCP tool usage within 24 hours
Auto Detection: Automatically detect GLM coding plan provider from OpenClaw configuration
Bilingual Support: Support Chinese and English output
如需安装 jq：
To install jq:
sudo apt-get install jq # Linux
brew install jq # macOS
Clone this repository to local:
git clone https://github.com/OrientLuna/openclaw-glm-plan-usage.git
cd openclaw-glm-plan-usage
Copy skill files to OpenClaw skills directory:
cp -r . ~/.openclaw/skills/glm-plan-usage/
chmod +x ~/.openclaw/skills/glm-plan-usage/scripts/query-usage.sh
Ensure GLM coding plan provider is configured (see Configuration below)
bash ~/.openclaw/skills/glm-plan-usage/scripts/query-usage.sh
openclaw /glm-plan-usage:usage-query
脚本会自动检测语言环境。您也可以通过环境变量强制指定语言：
The script automatically detects language environment. You can also force language via environment variable:
# 中文输出 / Chinese output
OPENCLAW_LANGUAGE=zh bash ~/.openclaw/skills/glm-plan-usage/scripts/query-usage.sh
# 英文输出 / English output
OPENCLAW_LANGUAGE=en bash ~/.openclaw/skills/glm-plan-usage/scripts/query-usage.sh
📊 GLM 编码套餐使用统计 / GLM Coding Plan Usage Statistics
提供商 / Provider: zhipu
统计时间 / Statistics Time: 2026-02-13 20:30:15
配额限制 / Quota Limits
---
Token 使用 (5小时) / Token Usage (5-hour): 45.2%
MCP 使用 (1个月) / MCP Usage (1-month): 12.3% (15000/120000 秒 / sec) [LEVEL_4]
模型使用 (24小时) / Model Usage (24 hours)
---
总 Token 数 / Total Tokens: 12,500,000
总调用次数 / Total Calls: 1,234
工具使用 (24小时) / Tool Usage (24 hours)
---
bash: 156 次 / times
file-read: 89 次 / times
web-search: 34 次 / times
技能会自动读取 ~/.openclaw/openclaw.json 中的提供商配置。
The skill automatically reads provider configuration from ~/.openclaw/openclaw.json.
{
"agents": {
"defaults": {
"model": {
"primary": "zhipu/glm-4-flash"
}
}
},
"models": {
"providers": {
"zhipu": {
"baseUrl": "https://open.bigmodel.cn/api/coding/paas/v4",
"apiKey": "your-api-key-here"
}
}
}
}
重要: baseUrl 必须包含 api/coding/paas/v4 或 open.bigmodel.cn，技能才能识别其为 GLM 编码套餐提供商。
Important: baseUrl must contain api/coding/paas/v4 or open.bigmodel.cn for the skill to recognize it as a GLM coding plan provider.
技能会检查以下条件来识别 GLM 编码套餐提供商：
The skill checks the following conditions to identify GLM coding plan providers:
baseUrl 包含 api/coding/paas/v4 或 open.bigmodel.cn baseUrl contains api/coding/paas/v4 or open.bigmodel.cn
coding、glm-coding、zhipu 或 bigmodel Provider name contains coding, glm-coding, zhipu, or bigmodel
技能查询三个监控端点：
The skill queries three monitoring endpoints:
| 端点 | Endpoint | 用途 | Purpose |
|---|---|---|---|
| ------ | ---------- | ------ | --------- |
/api/monitor/usage/quota/limit | 配额百分比（5小时 Token，1个月 MCP） | Quota percentage (5-hour token, 1-month MCP) | |
/api/monitor/usage/model-usage | 24小时模型使用统计 | 24-hour model usage statistics | |
/api/monitor/usage/tool-usage | 24小时 MCP 工具使用 | 24-hour MCP tool usage |
详见 API 文档。
See API Documentation for details.
脚本为常见问题提供友好的错误提示：
The script provides friendly error messages for common issues:
使用包管理器安装 jq：
Install jq using package manager:
sudo apt-get install jq # Linux
brew install jq # macOS
确保提供商的 baseUrl 包含 api/coding/paas/v4。更新配置：
Ensure the provider's baseUrl contains api/coding/paas/v4. Update configuration:
{
"models": {
"providers": {
"your-provider": {
"baseUrl": "https://open.bigmodel.cn/api/coding/paas/v4",
"apiKey": "your-key"
}
}
}
}
验证 API 密钥是否正确：
Verify API key is correct:
jq -r '.models.providers.zhipu.apiKey' ~/.openclaw/openclaw.json
欢迎贡献！请遵循以下步骤：
Contributions welcome! Please follow these steps:
git checkout -b feature/amazing-feature) | Create feature branchgit commit -m 'Add some amazing feature') | Commit changesgit push origin feature/amazing-feature) | Push to branchMIT License - 详见 LICENSE 文件。
MIT License - See LICENSE file for details.
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