plain-text-output
Plain Text Output
This skill ensures that all generated text is strictly plain text, free of any formatting decorations. This is crucial for maintaining compatibility across various systems and ensuring maximum readability in restricted environments like terminals.
Formatting Rules
- No Decoration: Do not use Unicode emojis, bold, or italics (except inside code blocks or when quoting code). Avoiding decorations ensures the text remains completely clean and compatible with systems that do not support rich text.
- Japanese Style: Always use noun-ending (体言止め) by default when writing in Japanese. This creates a concise, objective, and professional tone suitable for documentation and logs.
Examples
Example 1: Documentation Input: We must ensure that the new API endpoints are fully tested before deploying! 🚀 Output: Ensure that the new API endpoints are fully tested before deploying.
Example 2: Japanese Output Input: 新しい機能を追加しました。UIがとても綺麗になりましたね✨ Output: 新機能の追加。UIの改善。
More from hrdtbs/agent-skills
plan-self-review
Self-evaluate a plan on a 100-point scale after it is created or updated. Make sure to use this skill immediately whenever you create a plan or update a plan, even if the user does not explicitly ask for a review. This skill ensures that the plan is clear, comprehensive, feasible, and consistent before execution.
45create-pull-request
Create a GitHub pull request safely and reliably using project conventions. Make sure to use this skill whenever the user asks to create a PR, submit changes for review, open a pull request, or mentions "PR", "プルリク", or "pull request". It handles commit verification, branch validation, and PR creation using the gh CLI.
40commit
Expert-level commit creation and formatting following Conventional Commits. Make sure to use this skill whenever you need to create a commit message, save changes to git, structure a logical commit history, or when the user mentions 'commit', 'git commit', 'コミット', '変更をコミット', or asks you to push their code.
39mcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
3prompt-evaluator
Evaluate and score user-written LLM prompts on a 100-point scale across 5 axes (Clarity, Structure, Information Content, Specificity, Context), providing specific improvement suggestions and a revised prompt. Make sure to use this skill whenever the user asks to evaluate, review, score, or improve a prompt, or when they say things like 'このプロンプトどう?', 'プロンプトを評価して', 'rate my prompt', 'review this prompt', or 'is this prompt good enough?'. This skill focuses on scoring existing prompts, not writing new ones from scratch.
3skill-judge
Evaluate Agent Skill design quality against official specifications and best practices. Use when reviewing, auditing, or improving SKILL.md files and skill packages. Provides multi-dimensional scoring and actionable improvement suggestions.
3