Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/cn-big-cabbage/github-skill-distiller/glancesnpx skills add CN-big-cabbage/github-skill-distiller --skill glancesgit clone --depth 1 https://github.com/CN-big-cabbage/github-skill-distillerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cn-big-cabbage/github-skill-distiller/glances)<a href="https://agentmods.dev/skills/cn-big-cabbage/github-skill-distiller/glances"><img src="https://agentmods.dev/badge/skills/cn-big-cabbage/github-skill-distiller/glances.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00013 | $0.01289 |
| Opus 5 | $0.00006 | $0.00645 |
| Sonnet 5 | $0.00003 | $0.00258 |
| Haiku 4.5 | $0.00001 | $0.00129 |
Grade A, and why
glances scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Glances 跨平台系统监控工具
技能概述
本技能帮助用户使用 Glances 进行系统性能监控与分析，支持以下场景：
- 实时监控: 在终端实时查看 CPU、内存、磁盘、网络等资源使用情况
- Web 界面: 通过浏览器远程访问系统监控仪表板
- API 服务: 通过 REST API 获取系统指标数据，便于集成
- 告警配置: 设置资源阈值，当超过时自动触发告警
- 导出分析: 将监控数据导出到 CSV、InfluxDB、Prometheus 等存储系统
- 服务器模式: 以客户端-服务端模式监控远程主机
支持平台: Linux、macOS、Windows、FreeBSD，支持 Docker 容器化部署
使用流程
AI 助手将引导你完成以下步骤：
- 安装 Glances（如未安装）
- 以合适的模式启动监控（终端/Web/API）
- 解读监控数据和告警信息
- 根据需要配置自定义参数
- 集成到现有工作流或监控系统
关键章节导航
AI 助手能力
当你向 AI 描述监控需求时，AI 会：
- 自动检测系统环境并选择合适的安装方式
- 启动 Glances 并解读当前系统状态
- 识别高负载进程和资源瓶颈
- 配置 Web 服务器模式并提供访问地址
- 设置导出插件将指标写入时序数据库
- 分析告警日志并给出优化建议
- 生成监控配置文件以满足特定需求
核心功能
- 实时监控 CPU、内存、磁盘 I/O、网络流量
- 进程列表（支持排序、过滤、终止）
- Web UI 仪表板（内置 HTTP 服务器）
- REST API / XML-RPC API 远程数据获取
- 客户端-服务端模式（通过 SSH 隧道监控远程主机）
- 告警系统（Careful / Warning / Critical 三级）
- 插件化架构（支持 30+ 内置插件）
- 导出支持：CSV、InfluxDB、Prometheus、Elasticsearch、MQTT 等
- Docker 和 Kubernetes 容器监控
- 支持 Python API 二次开发
快速示例
# 启动终端交互模式
glances
# 启动 Web 服务器模式（浏览器访问）
glances -w
# 以服务端模式运行（等待客户端连接）
glances -s
# 连接远程服务端
glances -c 192.168.1.100
# 以 REST API 模式运行
glances -w --disable-webui
# 查看特定进程
glances --process-filter python
# 将数据导出到 CSV
glances --export csv --export-csv-file /tmp/glances.csv
# 静默模式（仅记录日志，无界面）
glances -q --export influxdb
监控指标说明
| 指标类别 | 描述 | 告警颜色 |
|---|---|---|
| CPU 使用率 | 总体和每核心使用率 | 绿/蓝/黄/红 |
| 内存 / Swap | 物理内存和交换空间使用 | 绿/蓝/黄/红 |
| 磁盘 I/O | 读写速率和等待时间 | 绿/蓝/黄/红 |
| 网络接口 | 上下行速率和数据包数 | 绿/蓝/黄/红 |
| 进程列表 | CPU/内存占用最高的进程 | 可排序 |
| 系统负载 | 1/5/15 分钟平均负载 | 绿/蓝/黄/红 |
| 传感器 | CPU 温度（需要硬件支持） | 绿/黄/红 |
| Docker | 容器状态和资源使用 | 绿/红 |
告警级别
Glances 使用四种颜色标识资源状态：
- 绿色 (OK): 正常，无需关注
- 蓝色 (Careful): 需要关注，资源使用偏高
- 黄色 (Warning): 警告，资源使用较高
- 红色 (Critical): 严重，需要立即处理
默认阈值可通过配置文件 ~/.config/glances/glances.conf 自定义。
安装要求
- Python 3.8 或更高版本
- pip 包管理器
- （可选）psutil >= 5.3.0（核心依赖，通常自动安装）
- （可选）bottles/docker SDK（容器监控）
- （可选）InfluxDB / Prometheus 客户端（数据导出）
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 133 lines · 13 tokens per session scan A 0b55a77d5116
glances is a skill published in the GitHub repository CN-big-cabbage/github-skill-distiller (4 stars, last pushed 2mo ago), licensed MIT. It adds 13 tokens to every session and 1,289 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
inferring-a-spell-from-examples
Use when the user invokes /capture-this-chat or /build-spell --from-transcript. Reads a transcript and produces a context dictionary plus a draft SKILL.md, which hands off to building-a-spell at Stage 2.
building-a-subagent-spell
Use when the meta-builder routes to kind=subagent. Generates a spell that dispatches one or more subagents to do work in parallel or in isolation.
intuitive-interviewing
Use when conducting any interview-style flow with a user (building a spell, refining one, gathering requirements). Picks depth, filters questions by relevance, matches against known shapes, and detects mid-stream pivots.
building-a-workflow-spell
Use when the meta-builder routes to kind=workflow. Generates a workflow spell with explicit stages, checkpoints, and loop-back conditions.
chaining-spells
Use when composing multiple spells into a chain that runs end-to-end (e.g. brainstorm -> plan -> execute -> verify).
discovering-spells
Use when browsing what spells are available - bundled seeds, your personal library, or both. Filter by kind, audience, or update status.