linkhut-automation
by ComposioHQlinkhut-automation helps agents automate Linkhut bookmark workflows through Composio Rube MCP, with tool discovery, connection checks, and current-schema execution guidance.
This skill scores 68/100, which means it is acceptable for directory listing but should be presented as a lightweight connector workflow rather than a fully self-contained automation package. Directory users get enough information to understand when to use it and how an agent should begin execution through Rube MCP, but the repository evidence shows limited Linkhut-specific operational depth and no supporting files or install command.
- Valid skill frontmatter declares the required Rube MCP dependency and a concise trigger: automating Linkhut tasks through Composio/Rube.
- Provides concrete prerequisite and setup steps, including checking `RUBE_SEARCH_TOOLS`, using `RUBE_MANAGE_CONNECTIONS`, and confirming an ACTIVE Linkhut connection before execution.
- Emphasizes tool discovery first, which should help agents use current Linkhut schemas instead of guessing stale tool names or parameters.
- No install command or support files are provided; adoption depends on manually configuring the Rube MCP endpoint and Linkhut connection.
- The workflow content appears mostly discovery-oriented and Rube-generic, with limited Linkhut-specific task examples or edge-case guidance.
Overview of linkhut-automation skill
What linkhut-automation does
linkhut-automation is a Claude skill for automating Linkhut bookmark workflows through Composio’s Rube MCP server. Instead of assuming a fixed API shape, the skill instructs the agent to search Rube tools first, confirm the active Linkhut connection, and then run the correct current tool schema for the requested task.
This is useful when you want an AI assistant to create, update, organize, retrieve, or otherwise operate on Linkhut data without manually checking Composio toolkit details each time.
Best fit for Linkhut and Rube MCP users
The linkhut-automation skill is best for users who already use, or are willing to use, Rube MCP as the automation layer. It is not a standalone Linkhut client and does not include local scripts. Its value is in giving the agent a safe workflow: discover tools, verify connection, execute with current schemas, and avoid stale assumptions.
Choose it if your assistant environment supports MCP tools and you want Linkhut operations handled inside a broader workflow automation session.
Main differentiator: schema discovery first
The most important detail is the “search tools first” rule. Composio/Rube tool names and input schemas can change, so linkhut-automation is designed to call RUBE_SEARCH_TOOLS before execution. That makes it more reliable than a generic prompt that guesses tool slugs, required fields, or execution order.
How to Use linkhut-automation skill
linkhut-automation install and setup context
To install from the skill directory repository, use:
npx skills add ComposioHQ/awesome-claude-skills --skill linkhut-automation
Then configure Rube MCP in your AI client by adding the MCP server endpoint:
https://rube.app/mcp
The skill requires the rube MCP server and expects RUBE_SEARCH_TOOLS to be available. Before asking for real Linkhut actions, use RUBE_MANAGE_CONNECTIONS with toolkit linkhut and complete the returned authentication flow if the connection is not ACTIVE.
Inputs the skill needs from you
For good linkhut-automation usage, describe the Linkhut task, the target data, and the desired safety level. A weak prompt is:
“Organize my Linkhut bookmarks.”
A stronger prompt is:
“Use linkhut-automation for Workflow Automation. First discover current Linkhut tools with Rube. Then find bookmarks tagged ai and suggest a cleanup plan before making changes. Do not delete anything. If edits are needed, ask for confirmation and show the exact fields you will update.”
This gives the agent a use case for RUBE_SEARCH_TOOLS, a filter, a change boundary, and an approval rule.
Recommended workflow for reliable execution
A practical linkhut-automation guide should follow this sequence:
- Call
RUBE_SEARCH_TOOLSwith a use case such as “Linkhut bookmark search and tag update.” - Reuse the returned session ID for follow-up discovery or execution planning.
- Check
RUBE_MANAGE_CONNECTIONSfor thelinkhuttoolkit. - If active, run only the tool schemas returned by Rube.
- Summarize what was found before making destructive or bulk changes.
This pattern matters because the skill’s source file has no helper scripts or extra reference files; the operational reliability comes from the MCP discovery process, not from bundled code.
Repository files to read first
Start with composio-skills/linkhut-automation/SKILL.md. It contains the prerequisites, setup sequence, discovery call pattern, connection check, and core workflow. There are no additional scripts/, resources/, rules/, or README.md files in this skill, so the main decision is whether your client can use Rube MCP and whether your Linkhut connection can be authenticated.
linkhut-automation skill FAQ
Is linkhut-automation a Linkhut API wrapper?
No. linkhut-automation is a skill that guides an AI agent through Composio’s Rube MCP tools. It does not ship a Linkhut SDK, CLI, or local automation script. The agent must use Rube tool discovery and the active Linkhut connection to perform actions.
When is this better than an ordinary prompt?
It is better when the assistant has access to MCP tools and you need dependable execution. A normal prompt may invent tool names or use outdated parameters. This skill explicitly requires RUBE_SEARCH_TOOLS first, which helps the agent retrieve current schemas, available tool slugs, recommended execution plans, and known pitfalls before acting.
Can beginners use this skill?
Yes, if they can configure MCP in their client. The main beginner blocker is not the skill text; it is the Rube MCP setup and Linkhut connection authorization. If your client cannot add MCP servers or expose RUBE_SEARCH_TOOLS, this skill will not be useful until that environment gap is solved.
When should I not use linkhut-automation?
Do not use it for offline bookmark editing, unsupported clients without MCP, or workflows where you need a packaged script you can run in CI. Also avoid giving broad destructive requests such as “clean up everything” without filters, previews, and confirmation rules.
How to Improve linkhut-automation skill
Improve prompts with scope and guardrails
The fastest way to improve linkhut-automation results is to provide scope. Include tags, bookmark titles, URLs, date ranges, or the exact type of operation. Also state whether the assistant may write changes or should only draft a plan.
Better prompt pattern:
“Search available Linkhut tools first. Find bookmarks matching example.com, list their current tags, propose normalized tags, and wait for approval before updating.”
This reduces accidental broad edits and makes tool selection easier.
Handle common failure modes
The most common failures are missing Rube MCP access, inactive Linkhut authentication, and stale assumptions about tool schemas. If execution fails, ask the agent to verify:
- Does
RUBE_SEARCH_TOOLSrespond? - Is the
linkhuttoolkit connectionACTIVE? - Did the agent use the latest schema returned in the same session?
- Is the requested action supported by the discovered Linkhut tools?
This troubleshooting path is more useful than retrying the same failed command.
Iterate after the first output
For read-heavy tasks, ask for a summary table before edits: URL, title, current tags, proposed tags, and reason. For write-heavy workflows, batch changes and require confirmation. After completion, ask the agent to report which Rube tools were used, what changed, and which items were skipped.
That audit trail makes linkhut-automation for Workflow Automation safer when Linkhut tasks are part of larger routines.
Extend the skill for your own standards
If you use consistent bookmark taxonomy, add your preferred tag rules to your prompt or local skill notes. Examples include lowercase tags only, no duplicate topic tags, required project tags, or “archive instead of delete.” The upstream skill is intentionally compact, so your best improvement is adding organization policy, confirmation thresholds, and examples of acceptable Linkhut changes.