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prompt-improver
Improve prompts using best practices for clarity, specificity, and structure. Use for optimizing AI agent and bot instructions.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Improve prompts using best practices for clarity, specificity, and structure. Use for optimizing AI agent and bot instructions.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | prompt-improver |
| description | Improve prompts using best practices for clarity, specificity, and structure. Use for optimizing AI agent and bot instructions. |
| context | fork |
| globs | [] |
| alwaysApply | false |
Improve prompts using best practices for clarity, specificity, and structure. Includes simplification patterns for reducing token waste.
Vague:
Make this code better
Specific:
Refactor this function to:
1. Use async/await for all I/O
2. Add type hints to parameters and return
3. Extract the validation logic into a separate function
4. Keep the same behavior (tests must pass)
Wordy:
I need you to please help me understand what this code does because I'm not sure and it's kind of confusing and there are a lot of parts and I don't know where to start looking at it
Concise:
Explain what this code does. Summarize in 3 bullet points.
# Context
[What is the background? What problem are we solving?]
# Task
[What exactly should be done? Be specific.]
# Constraints
- [Constraint 1: e.g., "Use only existing libraries"]
- [Constraint 2: e.g., "Do not change the API"]
# Output Format
[How should the response be structured?]
# Examples (optional)
[Show expected input/output if helpful]
Level 2 — Pantheon-native context compression with priority scoring, semantic summarization, downstream-aware compression, budget allocation, and cross-references
Multi-agent orchestration with model routing, category delegation, and sprint management. Use for coordinating Pantheon agents.
Auto-continue through todos with idle detection and safety gates. Use for multi-step orchestration.
Level 2 — Pantheon-native context compression with priority scoring, semantic summarization, downstream-aware compression, budget allocation, and cross-references
MCP security hardening — credential leakage prevention, input sanitization, and tool access control. Use for reviewing agent MCP configurations.
Multi-model routing with cost/quality selection and provider fallbacks. Use for AI gateway abstraction.