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Skills Troubleshooting & Best Practices ​

This page covers how to write high-quality Skills, optimize performance, and avoid common pitfalls. For setup instructions, see the Skills Usage Guide.

Writing High-Quality Skills ​

1. Clear naming and descriptions ​

TIP

The AI uses the Skill's description to decide when to apply it. A vague description leads to missed or incorrect matches.

  • Write specific descriptions: Clearly explain the purpose and scenarios for the Skill. Include important keywords and state prerequisites and expected outcomes.
  • Use consistent naming: Choose clear, descriptive names. Optionally add category prefixes such as k8s-, docker-, git-. Keep naming conventions consistent across all Skills.

2. Structured steps ​

  • Keep the logic clear

    • Order steps by execution sequence.
    • Use numbered lists to make order explicit.
    • Add necessary decisions and branches where behavior splits.

  • Be detailed and concrete

    • Include full commands and parameters.
    • Explain the intent of each step.
    • Provide examples and expected outputs where helpful.

3. Error handling and rollback ​

  • Handle failure cases

    • Identify likely error conditions.
    • Include steps for handling each error path.
    • Describe how to tell whether an operation succeeded or failed.

  • Define rollback procedures

    • For operations that might have impact, provide rollback steps.
    • Explain how to restore the system to its previous state.

WARNING

Skills that modify infrastructure or data should always include a rollback section. Without one, a failed operation can leave the system in an inconsistent state.

4. Maintainability ​

  • Use variables instead of hard-coding

    • Avoid hard-coded values where possible.
    • Use placeholders (e.g., <pod-name>, <namespace>) to keep the Skill reusable.
    • Document what each variable means and its valid range.

  • Design with modularity

    • Break complex workflows into smaller, focused sections.
    • Make each step responsible for a single concern.
    • Keep Skills easy to test and modify over time.

Performance Optimization ​

Control the number of enabled Skills ​

INFO

Every enabled Skill is injected into the AI system prompt. Too many enabled Skills increase token usage and can dilute the AI's focus.

  • Enable only what you need: Turn on only the Skills that are relevant to your current task. Periodically clean up Skills that are no longer used.
  • Load by scenario: Switch different Skill sets based on the current task. Use grouping to manage Skills by scenario and maintain different collections for different workflows.

Optimize Skill content ​

  • Streamline steps: Remove unnecessary steps, combine steps that can safely run in parallel, and optimize command order for faster feedback.
  • Reduce complexity: Avoid overly complex conditional logic inside a single Skill. Split very complex Skills into multiple simpler ones. Store large blocks of content in resource files instead of inline text.

Resources ​

Terminal Skills official repository ​

  • GitHub: chaterm/terminal-skills
  • Description: A curated collection of terminal-focused Skills covering a variety of operations and development use cases.