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ai-agent-helper skill

safe

This skill helps you set up and optimize AI agents with prompt engineering, task decomposition, and agent loop design for better automation.

npx playbooks add skill openclaw/skills --skill ai-agent-helper

Review the files below or copy the command above to add this skill to your agents.

Files (2)

SKILL.md

861 B

---
name: ai-agent-helper
description: AI Agent 設定同優化助手 - Prompt Engineering、Task Decomposition、Agent Loop設計
version: 1.0.0
tags:
  - ai
  - agent
  - prompt
  - automation
  - productivity
---

# AI Agent Helper

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

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

Overview

This skill is an AI Agent setup and optimization assistant focused on prompt engineering, task decomposition, and agent loop design. It helps create high-quality system prompts, design effective ReAct/CoT loops, and optimize tool usage and token costs. The result is more reliable, predictable, and efficient agent behavior.

How this skill works

The skill inspects agent goals, constraints, and available tools, then crafts structured system prompts and few-shot examples to steer behavior. It decomposes complex tasks into manageable subtasks and recommends agent loop patterns (ReAct, Chain-of-Thought, or simple pipelines) that match the use case. It also defines output parsing schemas (JSON/structured), error handling patterns, and token optimization tactics to improve cost and reliability.

When to use it

Setting up a new AI agent from scratch
Improving an existing agent’s reliability or accuracy
Designing agent loops for multi-step reasoning or tool use
Defining structured outputs for downstream processing
Reducing token usage and execution cost

Best practices

Start with a clear role, goal, and constraints in the system prompt
Provide concise few-shot examples that demonstrate desired format and edge cases
Decompose complex tasks into explicit subtasks with acceptance criteria
Choose an agent loop aligned to task complexity: simple pipeline for deterministic tasks, ReAct/CoT for exploratory reasoning
Define strict output schemas and validate outputs with parsing rules to catch errors early
Instrument prompts for token-conscious phrasing and prefer compact examples

Example use cases

Create a system prompt and example set for a customer-support agent that calls knowledge-base tools
Design a multi-step data-extraction agent that decomposes invoices into line items and validates totals
Implement a ReAct loop for an agent that queries external APIs and decides when to retry or fallback
Optimize prompts and examples to reduce token cost for high-volume inference
Define JSON output schema and parser for reliable ingestion into downstream workflows

FAQ

Will this skill write production code for my agent?

I provide structured prompt templates, decomposition patterns, loop designs, and parsing schemas you can integrate into your codebase. I don’t deploy infrastructure but give actionable artifacts to implement.

How do you reduce hallucinations and errors?

By combining clear system constraints, few-shot examples demonstrating failure modes, strict output schemas with validation, and explicit error-handling directives inside the agent loop.