> For the complete documentation index, see [llms.txt](https://integrations.impact.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://integrations.impact.com/ai-solutions/mcp-skills/brand-skills/brand-channel-and-media-mix-brief-skill.md).

# Brand Channel & Media Mix Brief Skill

Use this skill to analyze how your program performance is distributed across different channels, media types, or networks. It generates percentage-based mix tables, period-over-period trends, and concentration callouts to help you evaluate your program's diversity and efficiency.

To see which specific partners are driving your volume, use the [Brand Partner Performance Scorecard](/ai-solutions/mcp-skills/brand-skills/brand-partner-performance-scorecard-skill.md) instead.

### Prerequisites

* MCP access enabled on your impact.com Brand account.
* Client connected per [MCP Quick Start](/ai-solutions/mcp-quick-start.md).

### Install the skill

{% file src="/files/tCefLFXYtX9Vil3s1GDy" %}

1. Download or copy the skill package above.
2. Install it in Claude, Cursor, or VS Code using [Install an MCP Skill](/ai-solutions/mcp-skills/install-an-mcp-skill.md).
3. Use this package's frontmatter when the AI client asks for metadata:
   * Name: `Brand channel / media mix brief`
   * Description: `Build a channel and media-type mix brief for an impact.com Brand program from live MCP data. Use to see how performance breaks down by channel and media type, compared to a prior period, with concentration risk called out.`
   * Folder name (Cursor / VS Code): `brand-channel-media-mix-brief`
4. With impact.com MCP connected, prompt: "Run the channel and media mix brief for last month."


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://integrations.impact.com/ai-solutions/mcp-skills/brand-skills/brand-channel-and-media-mix-brief-skill.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.