hypeauditor-automation

Overview of hypeauditor-automation skill

What hypeauditor-automation does

The hypeauditor-automation skill helps an AI agent automate HypeAuditor tasks through Composio’s Rube MCP server. Instead of asking the model to guess HypeAuditor API fields, the skill instructs it to discover the current Rube tools first, verify the HypeAuditor connection, and then execute the right tool with the live schema.

This is most useful for teams that already use HypeAuditor for influencer discovery, creator analysis, audience quality checks, campaign research, or related workflow automation, and want Claude or another MCP-capable agent to operate those tasks reliably.

Best fit for Workflow Automation users

Use hypeauditor-automation for Workflow Automation when your task depends on live HypeAuditor actions rather than static advice. Good fits include:

• finding the correct HypeAuditor tool before running an action
• checking whether the HypeAuditor account connection is active
• turning a marketing operations request into a tool-backed workflow
• reducing schema errors by making tool discovery mandatory

It is not a general influencer marketing strategy skill. Its value is in safely routing requests through Rube MCP and Composio’s HypeAuditor toolkit.

Key differentiator: search tools first

The most important behavior in this skill is the “always search tools first” rule. HypeAuditor tool schemas can change, and Composio may expose multiple related operations. The skill pushes the agent to call RUBE_SEARCH_TOOLS before execution so it can retrieve current tool slugs, input requirements, execution plans, and pitfalls.

That makes the hypeauditor-automation skill stronger than a plain prompt such as “use HypeAuditor to find creators,” because the agent is guided to inspect available tooling before making assumptions.

How to Use hypeauditor-automation skill

hypeauditor-automation install and setup context

Install the skill from the Composio skill collection:

npx skills add ComposioHQ/awesome-claude-skills --skill hypeauditor-automation

Then configure Rube MCP in your AI client by adding:

https://rube.app/mcp

The source skill requires the rube MCP server and expects RUBE_SEARCH_TOOLS to be available. Before any HypeAuditor workflow can run, the agent also needs an active HypeAuditor connection through RUBE_MANAGE_CONNECTIONS using toolkit hypeauditor.

A practical setup sequence is:

1. Confirm RUBE_SEARCH_TOOLS responds.
2. Ask the agent to call RUBE_MANAGE_CONNECTIONS for toolkit hypeauditor.
3. Complete the returned authentication link if the connection is not active.
4. Continue only after the connection status is ACTIVE.

Inputs the agent needs to run it well

For reliable hypeauditor-automation usage, give the agent more than a broad goal. Include the business objective, target market, creator constraints, audience criteria, output format, and whether it should only research or also take an action.

Weak prompt:

Find influencers for our campaign.

Stronger prompt:

Use the hypeauditor-automation skill. First search Rube tools for current HypeAuditor schemas. Check that the HypeAuditor connection is active. Then identify which available tool can support creator discovery for a US fitness apparel campaign targeting women aged 25-40. Do not execute irreversible actions. Return the tool selected, required fields, any missing inputs, and a proposed execution plan.

This improves output because the agent can map your goal to current Rube tooling instead of inventing HypeAuditor fields.

Recommended workflow for first run

A good first run should be discovery-heavy:

1. Ask the agent to read composio-skills/hypeauditor-automation/SKILL.md.
2. Have it call RUBE_SEARCH_TOOLS with a specific use case such as “HypeAuditor creator discovery for Instagram fitness influencers.”
3. Have it check the HypeAuditor connection with RUBE_MANAGE_CONNECTIONS.
4. Review the returned tool schema and required fields.
5. Provide missing fields before execution.
6. Ask for a short summary of the tool call, parameters used, and result limitations.

Because this repository path only includes SKILL.md, that file is the main source to inspect. There are no bundled scripts, references, or rule folders to audit before use.

Practical prompt pattern

For repeatable results, use this structure:

• Task: what you want done in HypeAuditor
• Discovery rule: “call RUBE_SEARCH_TOOLS first”
• Connection rule: “verify toolkit hypeauditor is active”
• Constraints: regions, platforms, audience, campaign type, budget, prohibited actions
• Output: table, execution plan, missing fields, or completed action summary

Example:

Use hypeauditor-automation for Workflow Automation. Search Rube tools first for HypeAuditor operations related to audience quality analysis. Verify the HypeAuditor connection. If a suitable tool exists, tell me the exact required inputs before running it. I need a concise table showing creator handle, audience country fit, engagement quality, risk flags, and missing data.

hypeauditor-automation skill FAQ

Is hypeauditor-automation beginner-friendly?

Yes, if your AI client already supports MCP and you can add the Rube MCP endpoint. The skill itself is short and direct. The main beginner hurdle is not prompt writing; it is making sure Rube MCP is available and the HypeAuditor connection is authenticated.

How is it different from an ordinary HypeAuditor prompt?

An ordinary prompt relies on the model’s memory or guesses. The hypeauditor-automation skill tells the agent to discover live Composio tool schemas with RUBE_SEARCH_TOOLS before acting. That matters when required fields, tool names, or recommended execution plans are not obvious.

When should I not use this skill?

Do not use it if you only need marketing advice, a campaign brief, or generic influencer selection criteria with no HypeAuditor action. Also avoid it when you cannot connect Rube MCP, cannot authenticate HypeAuditor, or need a fully custom API integration outside Composio’s toolkit.

Does it include ready-made scripts or templates?

No. The repository evidence shows a single SKILL.md file for this skill, with no support scripts, references, resources, or metadata files in the skill folder. Treat it as an agent instruction layer for Rube MCP, not a standalone automation package.

How to Improve hypeauditor-automation skill

Improve hypeauditor-automation results with better task framing

The biggest quality gain comes from making the HypeAuditor task specific before tool discovery. Instead of “analyze influencers,” specify whether you need discovery, account lookup, audience analysis, fraud checks, report retrieval, or campaign research. Add platform, geography, niche, audience demographics, minimum engagement requirements, and acceptable risk level.

This helps RUBE_SEARCH_TOOLS return tools and schemas aligned with the real workflow rather than a broad list of possible operations.

Common failure modes to watch for

The main failure modes are:

• skipping RUBE_SEARCH_TOOLS and guessing tool inputs
• attempting execution before the HypeAuditor connection is ACTIVE
• giving a vague campaign goal that does not map cleanly to a tool
• failing to ask for missing required fields before execution
• treating HypeAuditor output as final strategy without human review

A good guardrail is to require the agent to show the chosen tool, required schema fields, and missing inputs before running anything that changes state or consumes quota.

Iterate after the first output

After the first result, ask the agent to refine based on concrete criteria rather than rerunning the same broad request. Useful follow-ups include:

• “Filter out creators with weak audience-country fit.”
• “Explain which fields came from HypeAuditor and which are inferred.”
• “Run the same workflow for TikTok if a supported tool exists.”
• “Return only candidates that meet these campaign thresholds.”
• “List limitations from the Rube tool response before recommending next steps.”

This turns the hypeauditor-automation guide into a controlled workflow: discover tools, confirm connection, execute with explicit fields, review limitations, then narrow the result set.