# design-agent

Teaches your AI assistant how to configure CrewAI agents: define roles, set goals and backstories, assign tools, and adjust execution limits without token waste.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 8300 installs · ★ 48
- Author: crewAIInc
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/crewAIInc/skills/tree/HEAD/skills/design-agent/SKILL.md
- Updated: 2026-10-06
- HTML: https://affpapa.org/en/nevibecode/skills/design-agent

## What it does

The skill guides agent architecture for CrewAI 1.15.23. Its core heuristic advises starting with a single agent. Splitting work across multiple agents makes sense only when steps require distinct tools, different LLM tiers, or separate output schemas. Routine multi-step loops run faster inside a single `Agent.kickoff()` without building an entire `Crew`.

It defines the Role-Goal-Backstory framework and separates persona prompts from task logic. Task implementation details do not belong in the backstory. The configuration sets reasoning bounds: `max_iter` defaults to 25 reasoning steps, while `max_execution_time` applies exclusively to crew tasks.

Keep in mind that the `Agent` class silently ignores unknown keyword arguments. A typo in parameter names will not raise a runtime error, so checking `Agent.model_fields` is required whenever a setting appears inactive.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add crewAIInc/skills --skill design-agent -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/design-agent/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/design-agent/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add crewAIInc/skills --skill design-agent -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/design-agent/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add crewAIInc/skills --skill design-agent -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/design-agent/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install crewAIInc/skills design-agent
```

2. **Manually: put the files in place**
- File: `.github/skills/design-agent/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add crewAIInc/skills --skill design-agent -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/crewAIInc/skills --path skills/design-agent
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/design-agent/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add crewAIInc/skills --skill design-agent -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/crewAIInc/skills/tree/HEAD/skills/design-agent/SKILL.md

## When to use it
- define specific role, goal, and backstory parameters for a CrewAI agent
- decide whether a workflow needs one agent or a multi-agent crew
- configure reasoning limits including max_iter and max_execution_time
- eliminate redundant agent handoffs to reduce token costs

## Example prompts
- Design a CrewAI researcher agent with a specific role, goal, and backstory for scraping news.
- Review these three CrewAI agents and check if they should be consolidated into one.
- Configure max_iter and tool assignments for an Agent.kickoff() step in a Flow.

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE 5567fe19b0ce207f
````text
---
name: design-agent
description: "CrewAI agent design and configuration. Use when creating, configuring, or debugging crewAI agents — choosing role/goal/backstory, selecting LLMs, assigning tools, tuning max_iter/max_rpm/max_execution_time, enabling planning/delegation, setting up knowledge sources, using guardrails, or configuring agents in YAML vs code."
---

# CrewAI Agent Design Guide

How to design effective agents with the right role, goal, backstory, tools, and configuration.

Verified against crewai 1.15.23 on 2026-10-01.
Live-tested with real LLMs on 2026-10-01.

Exact API forms (imports, defaults, provider extras, structured output) are in the **check-crewai-api** skill, and tools and MCP are in **connect-tools-and-mcp**. Where this skill and those disagree, follow them.

---

## The 80/20 Rule

**Spend 80% of your effort on task design, 20% on agent design.** A well-designed task elevates even a simple agent. But even the best agent cannot rescue a vague, poorly scoped task. Get the task right first (see the `design-task` skill), then refine the agent.

---

## 0. How Many Agents Do You Actually Need?

**Default to ONE agent.** Add more only when the task genuinely splits into work that requires:

- **Different tools or permissions** — e.g. one agent has Slack write access, another reads docs only.
- **Different personas the LLM must clearly switch between** — a writer's voice is not a researcher's voice.
- **Different LLMs** — a cheap model for mechanical steps, a stronger one for synthesis.
- **Different guardrails or output schemas** — separate agents make the contract per stage explicit.

**DO NOT add an agent just because the workflow has multiple steps.** A single agent can call multiple tools in sequence within one kickoff (search → scrape → summarize is one agent's loop), produce structured multi-section output in one response, and iterate via its own tool-use loop.

**Cost calculus:** every extra agent = at least one more LLM kickoff plus a context handoff. Splitting linear, single-persona work into multiple agents multiplies token cost and adds fragility for marginal quality wins.

### Anti-patterns

❌ Three agents for what is one researcher's job:
```python
source_finder = Agent(role="Finds URLs via Firecrawl search", tools=[firecrawl_search])
scraper       = Agent(role="Scrapes URLs via Firecrawl scrape", tools=[firecrawl_scrape])
writer        = Agent(role="Writes the report")  # plus goal, backstory, llm
```

✅ One researcher does the gathering loop; one writer synthesizes — two agents because the personas and LLMs genuinely differ:
```python
researcher = Agent(role="Web Researcher", tools=[firecrawl_search, firecrawl_scrape], llm="anthropic/claude-haiku-4-5")
writer     = Agent(role="Technical Report Writer",                                    llm="anthropic/claude-sonnet-4-6")
```
````
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