Agent Skills: Agent Design Skill

AI agent design and tool-use prompting patterns

ai-agentsagent-designtool-useprompting-patternsagent-framework
agentID: pluginagentmarketplace/custom-plugin-prompt-engineering/agent-design

Skill Files

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skills/agent-design/SKILL.md

Skill Metadata

Name
agent-design
Description
AI agent design and tool-use prompting patterns

Agent Design Skill

Bonded to: react-pattern-agent


Quick Start

Skill("custom-plugin-prompt-engineering:agent-design")

Parameter Schema

parameters:
  agent_type:
    type: enum
    values: [react, plan_execute, reflexion, multi_agent]
    default: react

  memory_type:
    type: enum
    values: [none, short_term, long_term, episodic]
    default: short_term

  tool_count:
    type: integer
    range: [1, 20]
    default: 5

Agent Architectures

| Architecture | Strengths | Use Case | |-------------|-----------|----------| | ReAct | Simple, effective | General tasks | | Plan-Execute | Structured approach | Complex multi-step | | Reflexion | Self-improvement | Learning tasks | | Multi-Agent | Specialization | Large systems |


Core Patterns

ReAct Agent

## Agent Configuration
You are an AI assistant with access to tools.

## Available Tools
[Tool list with descriptions]

## Behavior
1. Think about what to do
2. Take an action using a tool
3. Observe the result
4. Repeat until task complete

Plan-Execute Agent

## Planning Phase
1. Analyze the task
2. Break into subtasks
3. Identify tools needed
4. Create execution plan

## Execution Phase
1. Execute each step
2. Validate results
3. Adjust if needed
4. Report completion

Tool Definition Template

tool:
  name: "tool_name"
  description: "When and why to use this tool"
  parameters:
    param1:
      type: string
      description: "What this parameter does"
      required: true
  returns: "Description of return value"
  errors:
    - "ERROR_TYPE: How to handle"

Memory Patterns

memory_types:
  working_memory:
    scope: current_conversation
    implementation: context_window

  long_term_memory:
    scope: persistent
    implementation: vector_store

  episodic_memory:
    scope: experience_based
    implementation: structured_logs

Troubleshooting

| Issue | Cause | Solution | |-------|-------|----------| | Wrong tool | Vague descriptions | Improve descriptions | | Loops forever | No exit condition | Add max iterations | | Forgets context | Memory overflow | Summarize periodically | | Poor planning | Complex task | Add decomposition step |


References

See agent frameworks: LangChain, AutoGen, CrewAI