gleap-automation
by ComposioHQgleap-automation helps Claude automate Gleap workflows through Composio Rube MCP by discovering current tool schemas, checking the Gleap connection, and executing approved actions safely.
Score: 68/100. This is acceptable for listing because it gives agents a concrete trigger and a usable Rube MCP workflow for Gleap automation, especially the requirement to discover current tool schemas before acting. Directory users should view it as a lightweight connector skill rather than a rich Gleap playbook: useful if they already use Rube/Composio, but not very informative about specific Gleap tasks before installation.
- Valid skill frontmatter and a clear description identify the trigger: automate Gleap tasks through Rube MCP/Composio.
- Prerequisites and setup steps explain that Rube MCP must be connected, Gleap must be authorized via RUBE_MANAGE_CONNECTIONS, and connection status should be ACTIVE before use.
- The skill gives a repeatable execution pattern: call RUBE_SEARCH_TOOLS first, inspect schemas and pitfalls, then run discovered tools rather than guessing stale inputs.
- Provides no support files, scripts, or references beyond the SKILL.md, so adoption depends entirely on the embedded instructions and live Rube tool discovery.
- Workflow guidance appears mostly generic to Rube MCP/Gleap discovery rather than detailed Gleap-specific automations or task examples.
Overview of gleap-automation skill
What gleap-automation does
gleap-automation is a Claude skill for running Gleap operations through Composio’s Rube MCP server. Its core value is not a fixed list of Gleap commands; it teaches the agent to discover the current Gleap tool schemas first, verify the connection, and then execute the requested workflow with the right Rube tool calls.
Use this skill when you want an AI agent to help with Gleap-related workflow automation instead of manually navigating tool names, parameters, and authentication state.
Best fit for Workflow Automation users
The gleap-automation skill is best for teams already using, or willing to use, Rube MCP with Composio. It fits support, product, QA, and operations workflows where Gleap is part of the feedback or customer issue loop and you want Claude to act through available MCP tools.
It is especially useful when your task depends on current tool schemas. The skill’s strongest instruction is: call RUBE_SEARCH_TOOLS before execution so the agent does not guess outdated field names.
What makes this skill different
A normal prompt might say “use Gleap to do X,” but it may hallucinate tool names or skip connection checks. gleap-automation adds a safer execution pattern:
- discover available Gleap tools with
RUBE_SEARCH_TOOLS - check or create an active Gleap connection with
RUBE_MANAGE_CONNECTIONS - use the returned schema and execution plan rather than assumptions
- proceed only after the toolkit connection is active
That makes it more reliable for live tool use than a generic automation prompt.
Main adoption considerations
Before installing, confirm that your Claude or agent environment supports MCP and can add https://rube.app/mcp as a server. You also need a Gleap connection managed through Rube. This skill has no helper scripts or bundled references; nearly all behavior is in SKILL.md, so its quality depends on your MCP setup and how clearly you describe the Gleap task.
How to Use gleap-automation skill
gleap-automation install and setup context
Install the skill from the repository path used by your skill manager, for example:
npx skills add ComposioHQ/awesome-claude-skills --skill gleap-automation
Then configure Rube MCP in your client by adding:
https://rube.app/mcp
The upstream skill says no separate API key is needed for the MCP endpoint, but you must authorize the Gleap toolkit connection. In practice, ask the agent to verify that RUBE_SEARCH_TOOLS is available, then call RUBE_MANAGE_CONNECTIONS for toolkit gleap. If the status is not ACTIVE, complete the returned auth flow before asking for real execution.
Inputs the skill needs from you
Give the agent enough task context to search the right tools and avoid destructive or ambiguous actions. A weak prompt is:
“Automate Gleap.”
A stronger gleap-automation usage prompt is:
“Use the gleap-automation skill. First run
RUBE_SEARCH_TOOLSfor the specific use case: find recent Gleap feedback items related to billing bugs and summarize status. Check the Gleap connection withRUBE_MANAGE_CONNECTIONS. Do not modify records unless you show me the proposed tool call and fields first.”
Useful inputs include:
- the specific Gleap object or workflow you care about
- whether the agent should read, create, update, or only draft actions
- filters such as date range, customer segment, issue type, or status
- approval rules before changing data
- the output format you want, such as table, summary, or action plan
Practical workflow for first run
For a first run, keep the task read-only. Ask the agent to:
- inspect
SKILL.md - confirm Rube MCP availability
- call
RUBE_SEARCH_TOOLSwith your exact Gleap use case - check the
gleapconnection status - show the discovered tool names, schemas, and proposed plan
- execute only after you approve the plan
This sequence matters because the skill is intentionally schema-first. If the agent skips discovery and writes a tool call from memory, you lose the main benefit of the gleap-automation guide.
Repository files to read first
The repository path is composio-skills/gleap-automation, and the important file is SKILL.md. There are no visible README.md, rules/, resources/, references/, or scripts in the file tree preview, so do not expect a large implementation package. Treat it as a compact operational instruction for Rube MCP, not a standalone Gleap SDK.
gleap-automation skill FAQ
Is gleap-automation beginner friendly?
It is beginner friendly if your environment already supports MCP tools, but it is not a one-click Gleap integration. New users need to understand that Claude will call Rube MCP tools, not Gleap directly. The main setup tasks are adding the Rube MCP server and completing the Gleap toolkit authorization.
When should I not use this skill?
Do not use gleap-automation if you only need a written Gleap strategy, a help article, or a mock workflow with no live tool execution. It is also a poor fit if your organization cannot authorize Gleap through Rube MCP, or if you require a fully audited custom integration with fixed schemas checked into your own codebase.
How is it different from asking Claude directly?
Direct prompting relies on Claude’s general knowledge. The gleap-automation skill tells Claude to discover the current tools and schemas through RUBE_SEARCH_TOOLS before acting. That reduces guessing and makes the agent adapt to Composio’s current Gleap toolkit surface.
Does it support every Gleap action?
The skill does not hard-code a complete list of supported Gleap actions. Support depends on the current Composio Gleap toolkit exposed through Rube MCP. That is why the skill repeatedly emphasizes tool discovery before execution.
How to Improve gleap-automation skill
Improve gleap-automation prompts with clear guardrails
For better results, state both the goal and the limits. Instead of “update Gleap issues,” write:
“Use gleap-automation for Workflow Automation. Search current Gleap tools for updating feedback status. Find items matching
mobile crashfrom the last 14 days. Before making changes, present the matched records, the exact target status, and the Rube tool call you plan to use.”
This gives the agent a searchable use case, constraints, and an approval checkpoint.
Avoid common failure modes
The most common failure is letting the agent skip RUBE_SEARCH_TOOLS. Another is asking for a broad automation without specifying whether changes are allowed. A third is assuming the connection is active when it is not.
If output quality drops, restart with a smaller task: discover tools, verify connection, read one or two records, then expand. This makes errors easier to isolate.
Iterate after the first output
After the first plan or execution result, refine with concrete feedback:
- “Use a narrower date range.”
- “Only include records with customer email present.”
- “Do not update anything; produce a CSV-style summary.”
- “Group findings by product area and severity.”
- “Show tool responses before making a second call.”
These follow-up instructions improve accuracy because they shape the next tool search and execution plan.
Add local team conventions
Teams can improve the gleap-automation skill by documenting their own Gleap naming conventions, approval policy, safe read-only defaults, and preferred report formats in nearby project instructions. The upstream skill is intentionally minimal; adding local rules around which Gleap changes require human approval will make it safer for production workflow automation.