browseai-automation
by ComposioHQbrowseai-automation helps Claude run Browse AI workflows through Composio Rube MCP, with mandatory tool discovery, connection checks, and current schemas before execution.
This skill scores 70/100, which means it is acceptable for directory listing but should be presented as a lightweight MCP workflow guide rather than a complete Browseai playbook. Directory users get enough evidence to understand when to install it—automating Browseai through Composio/Rube MCP—but should expect dynamic tool discovery and connection setup to do much of the operational work.
- Valid skill frontmatter declares the required `rube` MCP and a concise Browseai automation purpose.
- Clear prerequisites and setup steps cover Rube MCP availability, Browseai connection via `RUBE_MANAGE_CONNECTIONS`, and confirming ACTIVE status before execution.
- Strong triggerability guidance tells agents to call `RUBE_SEARCH_TOOLS` first for current tool schemas rather than relying on stale embedded parameters.
- No install command or support files are included; setup is described manually as adding the Rube MCP endpoint.
- Workflow guidance is mostly a generic Rube discovery/check/execute pattern, so users may still need tool-search results to know exact Browseai actions and schemas.
Overview of browseai-automation skill
What browseai-automation is for
browseai-automation is a Claude skill for running Browse AI workflows through Composio’s Rube MCP server. It is designed for users who want an agent to automate Browse AI operations without guessing tool names, request schemas, or connection state. The key behavior is not “call Browse AI directly”; it is “discover the current Browse AI tools through Rube, confirm the connection, then execute with the latest schema.”
Best-fit users and jobs
This browseai-automation skill fits teams already using Claude with MCP and Browse AI for browser automation, web data extraction, monitoring, or robot-style workflows. It is most useful when your task depends on Browse AI’s connected account and the available Composio toolkit actions. If you frequently need an AI agent to check available Browse AI operations, prepare valid tool calls, and avoid stale parameter assumptions, this skill gives you a safer operating pattern than a plain prompt.
Main differentiator for Browser Automation
The practical differentiator is mandatory tool discovery. The skill instructs the agent to call RUBE_SEARCH_TOOLS before execution, because Composio tool schemas can change. That matters for Browser Automation workflows where incorrect fields, outdated tool slugs, or inactive connections can waste runs. The skill also emphasizes RUBE_MANAGE_CONNECTIONS so the agent checks that the browseai toolkit connection is active before attempting work.
Adoption considerations
Install this skill only if your client supports MCP and can connect to https://rube.app/mcp. The repository path contains only SKILL.md, so there are no helper scripts, examples, or local test fixtures to inspect. The skill is concise and operational, but you should expect to provide task-specific details such as the Browse AI robot, target operation, inputs, output expectations, and failure handling.
How to Use browseai-automation skill
browseai-automation install context
Install the skill from the Composio skill collection, then configure Rube MCP in your AI client:
npx skills add ComposioHQ/awesome-claude-skills --skill browseai-automation
Add https://rube.app/mcp as an MCP server. The skill expects Rube tools to be available, especially RUBE_SEARCH_TOOLS and RUBE_MANAGE_CONNECTIONS. After installation, ask the agent to verify that RUBE_SEARCH_TOOLS responds before planning any Browse AI action. Then have it call RUBE_MANAGE_CONNECTIONS for toolkit browseai and complete the returned auth flow if the connection is not ACTIVE.
Inputs the skill needs from you
A strong browseai-automation usage prompt should include the business goal, the Browse AI asset or robot you expect to use, the target data or action, timing requirements, and what the final answer should contain. Avoid prompts like “run my Browse AI task.” Prefer:
“Use browseai-automation to run a Browse AI workflow that checks my existing robot for product price changes. First discover the current Browse AI tools with RUBE_SEARCH_TOOLS, verify the browseai connection is active, then identify the correct tool schema before executing. Return the run status, any extracted fields, and any tool errors without retrying destructive actions.”
This gives the agent enough context to search for the right tool and avoid inventing fields.
Recommended workflow
Use a four-step workflow: discover, connect, execute, summarize. First, call RUBE_SEARCH_TOOLS with a use case matching the real task, such as “Browse AI robot run status” or “Browse AI data extraction results.” Second, check RUBE_MANAGE_CONNECTIONS for the browseai toolkit. Third, call the selected Browse AI tool using the schema returned by discovery, not remembered examples. Fourth, ask the agent to summarize exact tool calls, status, outputs, and next steps.
Files to read before relying on it
Read composio-skills/browseai-automation/SKILL.md first; it contains the full implementation guidance. There are no extra README.md, scripts/, references/, or rules/ folders in this skill, so the install decision depends mainly on whether the Rube MCP workflow matches your environment. For current Browse AI capability details, follow the linked Composio toolkit documentation at composio.dev/toolkits/browseai.
browseai-automation skill FAQ
Is browseai-automation beginner-friendly?
It is beginner-friendly only if your MCP client is already set up or you are comfortable adding an MCP server. The skill’s steps are simple, but the workflow depends on external connection state: Rube MCP must be reachable, and the Browse AI toolkit must be authenticated. Beginners should start by asking the agent to verify tools and connection status before requesting any automation.
How is it better than an ordinary prompt?
An ordinary prompt may ask the model to “use Browse AI,” but it can hallucinate tool names or rely on outdated schemas. The browseai-automation skill explicitly requires RUBE_SEARCH_TOOLS first, which gives the agent current tool slugs, input schemas, execution plans, and pitfalls. That makes it more reliable for Composio-backed Browser Automation than a generic instruction.
When should I not use this skill?
Do not use it if you need direct browser control through Playwright, Selenium, or a local scraping script. This skill is for Browse AI operations exposed through Composio’s Browseai toolkit via Rube MCP. It is also a poor fit if your organization cannot authorize Browse AI through Rube or if your workflow requires offline execution without MCP access.
Does it include ready-made Browse AI recipes?
No. The repository evidence shows a single SKILL.md file and no bundled recipes, scripts, or reference examples. The skill provides the execution pattern, not a catalog of robot-specific automations. Your prompt must supply the concrete Browse AI task and acceptance criteria.
How to Improve browseai-automation skill
Improve browseai-automation prompts with task specifics
The fastest way to improve browseai-automation results is to provide operational detail. Include the Browse AI robot name or identifier if known, the desired operation, required fields, output format, and retry policy. For example: “If the connection is inactive, stop and show the auth requirement. If a run fails, report the error and do not create a new robot.” This prevents the agent from making unsafe assumptions.
Common failure modes to prevent
Most failures will come from missing MCP access, inactive Browse AI authentication, stale schemas, or vague task descriptions. Require the agent to show the discovered tool name and required fields before execution when the action is important. If a tool call fails validation, have it re-run RUBE_SEARCH_TOOLS with the exact use case and compare the returned schema against the attempted payload.
Iterate after the first run
After the first output, refine the prompt around what was missing: field names, run IDs, extracted records, timestamps, or error details. Ask for a compact execution log: discovered tools, connection status, selected tool, parameters used, result status, and unresolved questions. This makes follow-up Browse AI automation easier to audit.
Extend the skill safely for your team
If you fork or customize the skill, add examples for your common Browse AI workflows, naming conventions for robots, approved retry behavior, and escalation rules for failed authentication. Keep the existing discovery-first rule. For a skill that depends on external tool schemas, preserving live discovery is more valuable than hard-coding examples that may become obsolete.