AI Agent Helper

AI Agent 設定同優化助手 - Prompt Engineering、Task Decomposition、Agent Loop設計

Security Scan

The skill is an instruction-only prompt-engineering/agent-design helper and its requested footprint (no installs, no credentials, no file access) is consistent with that purpose.

Purpose & Capability

Name, description and SKILL.md content all describe prompt engineering, task decomposition and agent loop design. There are no unrelated requirements (no env vars, binaries, or installs) that would be disproportionate to this stated purpose.

Instruction Scope

SKILL.md contains guidance and examples for prompts, few-shot examples, output parsing and error handling. It does not instruct the agent to read files, access environment variables, call external endpoints, or collect/transmit data outside the agent—so the instructions stay within scope.

Install Mechanism

No install specification or code is provided (instruction-only). This minimizes on-disk code execution risk and is appropriate for a prompt-engineering helper.

Credentials

The skill requests no environment variables, credentials, or config paths. That is proportionate for a skill whose role is to provide prompt templates and design advice.

Persistence & Privilege

always is false and the skill is user-invocable. disable-model-invocation is false (the platform default) but that alone is not a red flag for this benign, instruction-only skill.

Assessment

This skill appears coherent and low-risk because it's just an instruction file for prompt/agent design and asks for no installs or secrets. Before installing: 1) verify the publisher/source if you need provenance; 2) avoid pasting secrets or private data into prompts or examples provided to the skill; 3) if you enable autonomous agent invocation, limit the agent's tool access and scopes so it cannot reach external services or exfiltrate data; and 4) treat outputs as guidance—review any generated agent code or tooling instructions before running them in your environment.

幫你setup同優化AI Agents既技能。

功能

• 📝 Prompt Engineering - 整高質量system prompts
• 🔄 Task Decomposition - 將複雜任務拆解
• ⚙️ Agent Loop設計 - ReAct/ReAct/Chain-of-Thought
• 🎯 Tool Selection - 最佳化agent既tool usage

使用場景

"帮我整prompt" / "點樣set AI agent" / "優化agent response"

技術

• System Prompt優化
• Few-shot examples
• Output parsing (JSON/structured)
• Error handling patterns
• Token優化

範例

# Good prompt structure
system = """你係{role}。
目標：{goal}
限制：{constraints}
Output格式：{format}"""