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daizhouchendaizhouchen/openclaw-skills/skills/terminal-dashboard/SKILL.md

terminal-dashboard

Skill0 starsChanged 48 days agoMITView on GitHub

根据 YAML 配置生成 Rich 终端仪表盘，显示系统资源或指定服务指标。用于创建或修改终端监控界面。

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use the specified data sources.

    line 23
  2. Very specific:

    Use scripts/generate_dashboard.py to produce a standalone dashboard from a YAML config:

    line 27
  3. Distinctive:

    Use Ctrl+C to stop the dashboard.

    line 59
  4. Distinctive:

    Run the bundled example dashboard directly:

    line 63

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Terminal Dashboard Skill

Resolve <skill_dir> to the directory containing this SKILL.md. Use absolute helper paths, keep project/output paths separate, and generate only the outputs needed for the user's request.

Workflow

Step 1: Determine Data Sources

Identify what the user wants to monitor. Common categories:

  • System resources: CPU, memory, disk, network, load average
  • Service health: HTTP endpoints, database connections, Redis, message queues
  • Project status: Git stats, CI/CD pipelines, deployment state
  • Custom metrics: API response times, error rates, queue depths
  • Logs: Tailing log files or journal entries

Use the specified data sources. Clarify missing requirements before adding service or network checks.

Step 2: Generate Dashboard Script

Use scripts/generate_dashboard.py to produce a standalone dashboard from a YAML config:

python3 "<skill_dir>/scripts/generate_dashboard.py" <config.yaml> --output <dashboard.py>

The generator reads the config and emits a self-contained Python script that uses the rich library for rendering. No runtime dependency on the generator itself.

Supported panel types:

  • metrics -- Key/value cards with trend arrows (up/down)
  • progress -- Progress bars for CPU, memory, disk, etc.
  • log -- Scrolling tail of a log file
  • status -- Green/yellow/red status lights for service checks

Step 3: Configure Layout

Edit the YAML config to adjust:

  • title -- Dashboard title shown at the top
  • refresh_interval -- Seconds between updates (default 2)
  • layout -- Grid arrangement, e.g. "2x2", "1x4", "3x1"
  • panels -- List of panel definitions (type, title, sources/checks)

A default config template lives at assets/config_template.yaml.

Step 4: Run and Iterate

python3 <generated_dashboard.py>

Use Ctrl+C to stop the dashboard. Do not leave a continuous monitor running unless requested.

Quick Start (no config needed)

Run the bundled example dashboard directly:

python3 "<skill_dir>/scripts/dashboard_example.py"

This shows CPU, memory, disk, load average, and top processes out of the box.

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  • It worked
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  • It didn't
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