> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Team Skills

> Give the team leader direct access to skills without delegating to a member agent.

Pass a `Skills` instance to the `skills` parameter on `Team`. The team leader gets skill tools and system prompt snippets directly.

```python theme={null}
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.skills import Skills, LocalSkills
from agno.team import Team

implementer = Agent(
    name="Implementer",
    role="Write code based on review feedback",
    model=OpenAIResponses(id="gpt-5.4-mini"),
)

team = Team(
    name="Code Review Team",
    model=OpenAIResponses(id="gpt-5.4-mini"),
    members=[implementer],
    skills=Skills(loaders=[LocalSkills("/path/to/skills")]),
    instructions=[
        "Use your skills to review code, then delegate implementation to the Implementer.",
    ],
)

team.print_response("Review this function and improve it", stream=True)
```

The leader model receives:

* Skill summaries in its system prompt (via `get_system_prompt_snippet()`)
* Skill tools: `get_skill_instructions`, `get_skill_reference`, `get_skill_script`

## Team-Level vs. Member-Level Skills

| Approach | When to use |
| - | - |
| `Team(skills=...)` | The leader needs domain expertise to coordinate (e.g., review standards, routing rules) |
| `Agent(skills=...)` on a member | A specialist agent needs the expertise to do its own work |
| Both | The leader needs context to delegate, and members need context to execute |

## How It Works

Skills on a team follow the same pattern as knowledge, memory, and tools:

1. `skills.get_tools()` adds skill tools to the leader's tool list
2. `skills.get_system_prompt_snippet()` injects skill metadata into the leader's system prompt
3. The leader discovers, loads, and uses skills on demand during the run

## Complete Example

````python theme={null}
from pathlib import Path

from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.skills import Skills, LocalSkills
from agno.team import Team

skills_dir = Path(__file__).parent / "sample_skills"

implementer = Agent(
    name="Implementer",
    role="Write code based on the review feedback",
    model=OpenAIResponses(id="gpt-5.4-mini"),
    instructions=[
        "You write clean, well-tested Python code.",
        "When given review feedback, produce an improved version of the code.",
    ],
)

review_team = Team(
    name="Code Review Team",
    model=OpenAIResponses(id="gpt-5.4-mini"),
    members=[implementer],
    skills=Skills(loaders=[LocalSkills(str(skills_dir))]),
    instructions=[
        "You are a team leader with access to code review skills.",
        "Use your skills to review code, then delegate implementation work to the Implementer.",
    ],
    markdown=True,
    show_members_responses=True,
)

if __name__ == "__main__":
    review_team.print_response(
        "Review this Python code and suggest improvements, "
        "then have the Implementer write the improved version:\n\n"
        "```python\n"
        "def calculate_total(items):\n"
        "    total = 0\n"
        "    for i in range(len(items)):\n"
        "        total = total + items[i]['price'] * items[i]['quantity']\n"
        "    return total\n"
        "```",
        stream=True,
    )
````

## Next Steps

| Task | Guide |
| - | - |
| Create skills | [Creating Skills](https://agno-v2.mintlify.site/skills/creating-skills.md) |
| Load skills into agents | [Loading Skills](https://agno-v2.mintlify.site/skills/loading-skills.md) |
| Build teams | [Building Teams](https://agno-v2.mintlify.site/teams/building-teams.md) |

## Developer Resources

* [Team reference](https://agno-v2.mintlify.site/reference/teams/team.md)
* [Skills examples](https://agno-v2.mintlify.site/examples/agents/skills/basic-skills.md)
* [Team skills example](https://agno-v2.mintlify.site/examples/teams/skills/basic-skills-team.md)


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.