launch_darkly-automation
by ComposioHQlaunch_darkly-automation helps agents manage LaunchDarkly feature flags, environments, segments, and rollouts through Composio Rube MCP, with schema-first tool discovery before authenticated actions.
Score: 70/100. This is acceptable for listing because it gives agents enough trigger, setup, and tool-discovery guidance to automate LaunchDarkly through Rube MCP with less guesswork than a generic prompt. Directory users should understand that it is a lightweight MCP-routing skill rather than a deeply documented LaunchDarkly playbook, so adoption depends on having Rube and a working LaunchDarkly connection.
- Clear trigger and scope: the description and title identify LaunchDarkly automation for feature flags, environments, segments, and rollout management via Rube MCP.
- Provides prerequisite and setup steps, including adding the Rube MCP endpoint, checking RUBE_SEARCH_TOOLS, and activating the launch_darkly connection with RUBE_MANAGE_CONNECTIONS.
- Includes a tool-discovery pattern that tells agents to search tools first for current schemas, reducing the risk of stale hardcoded tool calls.
- Execution depends on an active Rube MCP and LaunchDarkly connection; the skill has no standalone scripts, references, or bundled examples beyond SKILL.md.
- Because it instructs agents to discover current schemas dynamically via RUBE_SEARCH_TOOLS, users get less fixed, concrete LaunchDarkly operation detail at install time.
Overview of launch_darkly-automation skill
What launch_darkly-automation does
launch_darkly-automation is a Claude skill for operating LaunchDarkly through Composio’s Rube MCP toolkit. It helps an agent discover and call current LaunchDarkly tools for feature flags, environments, segments, rollout management, and related project operations without hard-coding stale API schemas.
The practical value is not just “ask AI to manage flags.” The skill’s central workflow is: connect Rube MCP, authenticate the LaunchDarkly toolkit, search for the latest tool schemas with RUBE_SEARCH_TOOLS, then execute the relevant LaunchDarkly action with the correct inputs.
Best fit for Workflow Automation teams
This launch_darkly-automation skill is best for engineering, DevOps, platform, and release teams that already use LaunchDarkly and want AI-assisted workflow automation around repetitive flag administration. Common fits include creating or updating feature flags, checking environment-specific rollout settings, managing segments, preparing release toggles, and auditing configuration before a deployment.
It is most useful when your team wants controlled automation through an MCP connection rather than free-form instructions that may invent LaunchDarkly API fields.
Main differentiator: schema-first execution
The most important differentiator is the instruction to always run tool discovery first. LaunchDarkly and Composio tool schemas can change, so the skill avoids relying on memorized parameter names. This makes it safer than a generic prompt when the agent needs to call real tools.
The tradeoff is that the skill depends on Rube MCP availability and an active LaunchDarkly connection. If your client cannot use MCP tools, this skill can still inform prompt structure, but it will not execute LaunchDarkly operations.
How to Use launch_darkly-automation skill
launch_darkly-automation install and setup path
Install the skill from the repository path:
npx skills add ComposioHQ/awesome-claude-skills --skill launch_darkly-automation
Then configure the runtime dependency: add https://rube.app/mcp as an MCP server in your AI client. The skill expects Rube tools to be available, especially RUBE_SEARCH_TOOLS and RUBE_MANAGE_CONNECTIONS.
Before asking the agent to modify anything in LaunchDarkly:
- Confirm
RUBE_SEARCH_TOOLSresponds. - Use
RUBE_MANAGE_CONNECTIONSwith toolkitlaunch_darkly. - Complete the returned authentication flow if the connection is not
ACTIVE. - Confirm the connection is active before running write operations.
Inputs the skill needs to work well
For reliable launch_darkly-automation usage, give the agent operational context instead of a vague request. Include:
- LaunchDarkly project key or project name
- Environment key, such as
production,staging, ordev - Feature flag key or naming pattern
- Desired action: create, update, inspect, enable, disable, target, or roll out
- Rollout percentage, targeting rules, segment names, or user attributes
- Safety constraints, such as “do not change production” or “dry-run first”
- Expected output format, such as a change summary or checklist
Weak prompt: “Set up a rollout for the new checkout flag.”
Stronger prompt: “Use launch_darkly-automation for Workflow Automation. First discover current LaunchDarkly tool schemas. In project web-app, environment staging, inspect flag checkout-v2. If it exists, propose a 10% rollout to beta users in segment beta-testers; do not apply changes until you show the exact tool call plan and risks.”
Suggested workflow for safe operations
A good launch_darkly-automation guide should treat flag changes as release operations, not casual edits. Use this sequence:
- Ask the agent to call
RUBE_SEARCH_TOOLSfor LaunchDarkly feature flags, environments, segments, and rollout management. - Ask it to summarize available tool slugs, required fields, and any pitfalls returned by Rube.
- Run a read-only inspection first when touching existing flags or production environments.
- Review the proposed tool call plan before allowing writes.
- After execution, request a concise audit summary: changed object, environment, old value, new value, and follow-up checks.
For repository review, start with composio-skills/launch_darkly-automation/SKILL.md. This repo path has no separate scripts, references, or metadata files, so the core behavior is concentrated in that file.
launch_darkly-automation skill FAQ
Is launch_darkly-automation better than a normal prompt?
Yes, when the goal is to operate LaunchDarkly through tools. A normal prompt may describe the right conceptual steps but can hallucinate tool names, request fields, or API shapes. The launch_darkly-automation skill explicitly routes the agent through Rube MCP discovery so it can obtain current tool schemas before acting.
For brainstorming release strategy, a normal prompt may be enough. For authenticated changes to feature flags, use the skill.
What are the prerequisites?
You need an AI client that supports MCP, Rube MCP configured as a server, and an active Composio LaunchDarkly connection. The LaunchDarkly account behind that connection must also have permission to read or modify the target projects, environments, flags, and segments.
If RUBE_SEARCH_TOOLS is unavailable or the LaunchDarkly toolkit connection is not active, the skill cannot complete operational workflows.
Can beginners use this skill safely?
Beginners can use it, but they should start with read-only tasks: list available tools, inspect a flag, summarize environments, or explain a rollout plan. Avoid granting write approval until you understand the project key, environment key, and flag being changed.
For production changes, require the agent to show the discovered schema, planned tool calls, and a rollback or verification step before execution.
When should I not use it?
Do not use launch_darkly-automation as a substitute for release governance, approval workflows, or incident procedures. It is also a poor fit if your organization blocks MCP connections, does not use Composio/Rube, or requires all LaunchDarkly changes to go through Terraform or another GitOps pipeline.
In those cases, use the skill for planning or documentation only, then apply changes through your approved system.
How to Improve launch_darkly-automation skill
Improve launch_darkly-automation inputs
The fastest way to improve launch_darkly-automation results is to provide exact identifiers and boundaries. Replace “update the mobile flag” with “inspect flag mobile-home-redesign in project consumer-app, environment staging; prepare but do not execute a 25% rollout for users where country = US.”
Also state whether the task is exploratory, read-only, or approved for writes. This prevents the agent from moving too quickly from discovery to execution.
Avoid common failure modes
The main failure mode is skipping RUBE_SEARCH_TOOLS. If the agent proposes tool calls without first discovering current schemas, stop and ask it to search tools again. Another common issue is confusing project names, environment keys, and flag keys; LaunchDarkly operations often fail or hit the wrong target when these are ambiguous.
For sensitive environments, require a confirmation gate: “Do not call any write tool until I approve the exact plan.”
Iterate after the first output
After the first response, ask for a tighter execution plan rather than immediately approving changes. Useful follow-ups include:
- “Which fields are required by the discovered schema?”
- “What will change in production versus staging?”
- “Show a read-only verification step before the write.”
- “Summarize rollback options if the rollout causes errors.”
These prompts turn the skill from a one-shot automation helper into a safer operational assistant.
Extend the skill for team standards
If your team adopts this skill heavily, improve it locally with your own rules: protected environments, naming conventions, required approval language, default dry-run behavior, and post-change audit format. The upstream SKILL.md is intentionally compact, so adding organization-specific guardrails can materially improve output quality without changing the core Rube MCP workflow.