honeyhive-automation
by ComposioHQhoneyhive-automation helps Claude automate Honeyhive workflows through Composio Rube MCP, with setup checks, active connection verification, and schema-first tool discovery before actions.
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 complete Honeyhive automation package. Directory users get enough evidence to understand when to install it—automating Honeyhive through Rube MCP with live schema discovery—but should expect to rely on Rube-discovered tools for task-specific execution details.
- Valid frontmatter declares the required MCP dependency (`rube`) and a concise trigger description for Honeyhive automation.
- Prerequisites and setup steps tell agents to verify `RUBE_SEARCH_TOOLS`, manage the Honeyhive connection, and confirm ACTIVE status before acting.
- The skill repeatedly instructs agents to call `RUBE_SEARCH_TOOLS` first, reducing schema guesswork for current Composio/Honeyhive tools.
- No support files, scripts, references, or README beyond SKILL.md, so users get only inline guidance rather than reusable automation assets.
- Operational detail appears mostly centered on generic Rube tool discovery and connection setup; concrete Honeyhive task examples are limited in the provided evidence.
Overview of honeyhive-automation skill
What honeyhive-automation does
honeyhive-automation is a Claude skill for automating Honeyhive operations through Composio’s Rube MCP server. Instead of hard-coding Honeyhive tool names or outdated schemas, the skill instructs the agent to discover the current Honeyhive tools first, verify the Honeyhive connection, then execute the requested workflow with the schema returned by Rube.
Best-fit users and jobs
This skill is most useful if you already use Honeyhive for LLM evaluation, observability, prompt/version tracking, or experiment workflows and want an AI agent to perform operational tasks without manually navigating APIs. Good fits include AI engineers, eval owners, prompt ops teams, and developers who want Claude to inspect available Honeyhive actions, create or update resources, retrieve evaluation data, or coordinate repetitive Honeyhive administration.
Key differentiator: schema-first automation
The main value of the honeyhive-automation skill is its “search tools first” pattern. Rube MCP tool schemas can change, and Honeyhive capabilities may vary by connection or account. The skill reduces brittle automation by requiring RUBE_SEARCH_TOOLS before execution, then using the returned tool slugs, required fields, execution plan, and pitfalls.
What to check before installing
Install this skill only if your Claude client supports MCP and you can add the Rube MCP endpoint. The repository path contains a single SKILL.md, so the operating logic is compact: there are no helper scripts, examples directory, or local resources to inspect. That makes adoption simple, but also means your prompt must provide the missing business context, such as the Honeyhive project, target object, desired action, and success criteria.
How to Use honeyhive-automation skill
honeyhive-automation install and setup
Install from the skill directory with:
npx skills add ComposioHQ/awesome-claude-skills --skill honeyhive-automation
Then add Rube MCP to your Claude-compatible client using:
https://rube.app/mcp
After installation, confirm that RUBE_SEARCH_TOOLS is available. Use RUBE_MANAGE_CONNECTIONS or the connection-management tool exposed by Rube to connect the honeyhive toolkit. If the connection is not ACTIVE, follow the authentication link returned by Rube before asking the agent to run Honeyhive actions.
Inputs the skill needs from you
For reliable honeyhive-automation usage, give the agent more than a broad request like “update Honeyhive.” Include:
- the Honeyhive object type, such as project, dataset, evaluation, run, prompt, trace, or experiment
- the operation, such as list, create, update, compare, export, or summarize
- identifiers or filters, such as project name, environment, date range, run ID, dataset name, or metric
- safety limits, such as “read-only first,” “do not delete,” or “ask before writes”
- output format, such as a table, execution summary, JSON payload, or change log
A stronger prompt is: “Use honeyhive-automation for Workflow Automation. First discover the current Honeyhive tools via Rube. Check that my Honeyhive connection is active. Then list recent evaluation runs for project support-agent-prod from the last 7 days, summarize pass rate and main failure categories, and do not modify anything.”
Recommended execution workflow
A good honeyhive-automation guide follows four steps:
- Ask Claude to invoke the skill and call
RUBE_SEARCH_TOOLSfor your exact Honeyhive task. - Have it inspect the returned tool schemas before choosing a tool.
- Confirm the Honeyhive connection is active.
- Execute the smallest safe action first, then expand.
For write operations, require a preview step: “Show the tool, schema fields, and proposed payload before executing.” This prevents accidental updates caused by missing project IDs, ambiguous names, or stale assumptions about Honeyhive’s API shape.
Repository files to read first
Start with composio-skills/honeyhive-automation/SKILL.md. It contains the core prerequisites, setup pattern, and discovery-first workflow. There is no README.md, metadata.json, scripts/, references/, or resources/ folder in this skill path, so do not expect bundled examples. If you need Honeyhive-specific object semantics, pair the skill with the toolkit documentation linked from the source: https://composio.dev/toolkits/honeyhive.
honeyhive-automation skill FAQ
Is honeyhive-automation only for advanced users?
Not necessarily, but beginners need MCP setup working first. If you are comfortable adding an MCP server and completing an OAuth-style connection flow, the skill can guide tool discovery. If you have never used Honeyhive or do not know which project, run, or dataset you need, start with read-only listing and summarization tasks.
How is this better than a normal prompt?
A normal prompt may guess Honeyhive API fields or invent tool names. The honeyhive-automation skill explicitly tells the agent to query Rube for current Honeyhive tool schemas before acting. That makes it better for live workflow automation where valid inputs, auth state, and available actions matter more than generic Honeyhive knowledge.
When should I not use this skill?
Do not use it if your client cannot connect to Rube MCP, if your Honeyhive account cannot be connected through Composio, or if you need offline-only repository scripts. Also avoid using it for destructive changes unless you add a confirmation checkpoint and require the agent to show the proposed tool call before execution.
Does it require Honeyhive API keys?
The source indicates that Rube MCP is added through https://rube.app/mcp and that the Honeyhive connection is managed through Rube connection tooling. You should not paste secrets into prompts. Complete the connection flow returned by Rube and verify the toolkit status is ACTIVE.
How to Improve honeyhive-automation skill
Improve prompts for honeyhive-automation
The biggest output-quality gain comes from making the task operationally precise. Replace “analyze my evals” with: “Discover Honeyhive tools, then retrieve evaluation runs for project checkout-agent, environment staging, last 14 days. Group failures by metric and include run links if available. Read-only only.” This gives the agent the target, filters, constraints, and output shape.
Add guardrails for write workflows
For create, update, archive, or delete workflows, ask for a two-phase plan: discovery and payload preview first, execution second. A strong instruction is: “Before any Honeyhive write action, show the selected Rube tool slug, required schema fields, inferred values, missing values, and risks. Wait for approval.” This compensates for the skill’s compact source and prevents overconfident automation.
Common failure modes to watch
Most failures come from inactive connections, vague Honeyhive identifiers, or skipping tool discovery. If the agent returns generic advice instead of tool calls, restate that it must use RUBE_SEARCH_TOOLS first. If it cannot find the right object, provide exact names, IDs, date ranges, or screenshots copied into text. If a schema error appears, ask it to re-run discovery and rebuild the payload from the latest schema.
Iterate after the first result
Treat the first run as calibration. Ask the agent to report what tools were discovered, which one it used, what fields were required, and what assumptions it made. Then refine the next honeyhive-automation request with those facts. Over time, keep a small team prompt template with your common Honeyhive project names, naming conventions, safe default filters, and required approval rules.