omp-agents

name	omp-agents
description	Use when a task matches a pre-defined Pi Agent's expertise. Delegate via omp run instead of doing it yourself. Do NOT use for multi-agent orchestration (use team skill).

High-Frequency Agents

Agent	Expertise	Typical Tasks
researcher	Deep research, web search, information retrieval	"Research latest developments in X", "Compare approaches A vs B"
reviewer	Skill/Agent quality audit	"Review this SKILL.md", "Check if agent definition is compliant"
ux-engineer	UI audit, frontend design, style optimization	"Audit UI issues on this page", "Generate Tailwind component"
oss-researcher	开源项目代码研究与知识库沉淀	"pi-mono 如何加载 skill？", "langchain memory 模块怎么设计的？"

Above is a high-frequency subset. Get the full agent list:

omp list agent

Invocation

# Recommended: stream mode for real-time progress
omp run <agent-name> --mode stream "task description"

# Specify model
omp run <agent-name> --mode stream -m <model> "task description"

Recommended Models

Model	When to Use
litellm-local/qwen3.5-27b	Default choice, local inference, zero cost
openai-codex/gpt-5.4	When higher quality is needed

Do not use other models.

When to Delegate

Principle: delegate when possible — agents carry specialized skills that outperform raw LLM.

Delegate when:

• Task clearly falls within an agent's expertise
• Task benefits from the agent's specialized skills (e.g. ux-engineer has 18 frontend skills)
• You lack the relevant tools or domain knowledge

Do NOT delegate when:

• Simple task — you can do it faster directly
• Task requires multi-turn interaction — omp run is one-shot, no follow-up
• Task needs current conversation context — agent cannot see your chat history

Prompt Requirements

• Self-contained — agent has no context from you; include all necessary information in the prompt
• Explicit output format — tell the agent what format you expect (markdown / JSON / code)
• Single responsibility — one task per delegation