givebutter-automation
by ComposioHQgivebutter-automation helps agents automate Givebutter workflows through Composio Rube MCP. Learn setup, connection checks, tool discovery, and safe usage patterns.
This skill scores 68/100, which means it is acceptable for directory listing but should be presented as a lightweight connector-oriented skill rather than a complete Givebutter playbook. Directory users get enough information to understand when to use it and how an agent should start via Rube MCP, but should expect limited task-specific guidance and reliance on live tool discovery.
- Clear trigger and scope: the frontmatter and title identify Givebutter automation via Rube MCP, with an explicit instruction to search tools first for current schemas.
- Operational prerequisites are stated, including Rube MCP availability, an active Givebutter connection via RUBE_MANAGE_CONNECTIONS, and confirming ACTIVE status before workflows.
- Provides a repeatable tool-discovery pattern using RUBE_SEARCH_TOOLS, which should reduce schema guesswork compared with a generic prompt.
- No support files, scripts, references, or README are present beyond SKILL.md, so adoption depends on the brief in-skill instructions and external Composio/Rube behavior.
- Workflow guidance is mostly a generic Rube discovery/execution pattern rather than detailed Givebutter-specific recipes, so agents may still need to infer exact campaign, donor, or transaction operations after tool discovery.
Overview of givebutter-automation skill
What givebutter-automation is for
The givebutter-automation skill helps an AI agent automate Givebutter tasks through Composio’s Rube MCP interface. It is designed for workflows where the agent must discover the current Givebutter tool schema, confirm an authenticated connection, and then run operations using the right Rube tool calls instead of guessing API fields from memory.
Best-fit users and workflows
This skill is a good fit for nonprofit operators, campaign teams, fundraising admins, and automation builders who already use Givebutter and want Claude or another MCP-capable agent to help with operational tasks. Typical use cases include donor, campaign, transaction, contact, event, or reporting workflows where the exact available actions should be discovered at runtime through RUBE_SEARCH_TOOLS.
What makes this different from a generic prompt
A normal prompt may ask an agent to “update Givebutter” or “pull donor data,” but it does not force schema discovery or connection validation. The givebutter-automation skill adds a safer execution pattern: search tools first, check the Givebutter connection, use the returned schemas, execute through Rube MCP, and verify results. That matters because Composio tool names, fields, and supported operations can change.
Main adoption constraint
The skill depends on Rube MCP and an active Givebutter connection. It is not a standalone Givebutter API wrapper, and it does not include helper scripts, local resources, or a separate README. Before installing, confirm your AI client supports MCP servers and can access https://rube.app/mcp.
How to Use givebutter-automation skill
givebutter-automation install context
Install the skill from the Composio skills repository if your client supports skill installation:
npx skills add ComposioHQ/awesome-claude-skills --skill givebutter-automation
Then add Rube MCP as an MCP server in your client configuration:
https://rube.app/mcp
The repository’s SKILL.md does not provide a package-specific runtime or scripts. The essential setup is MCP availability plus a Givebutter connection managed through Rube.
Required setup before running tasks
Start by confirming RUBE_SEARCH_TOOLS is available. Then use RUBE_MANAGE_CONNECTIONS with toolkit givebutter to check whether the connection is active. If Rube returns an auth link, complete the authorization flow before asking the agent to modify or retrieve Givebutter data.
A practical first prompt is:
Use the givebutter-automation skill. First call
RUBE_SEARCH_TOOLSfor my Givebutter task, then check thegivebutterconnection withRUBE_MANAGE_CONNECTIONS. Do not execute changes until you show me the discovered tool, required fields, and planned action.
Turning a rough goal into a strong prompt
Weak prompt:
Update my Givebutter donors.
Better prompt:
Use givebutter-automation for Workflow Automation. I need to find Givebutter supporters who donated to campaign
[campaign name or ID]between[date range], identify records missing phone numbers, and prepare an update plan. First discover current Givebutter tools withRUBE_SEARCH_TOOLS; then confirm the Givebutter connection is active. If a write operation is needed, show the exact fields you will send before executing.
Strong inputs improve output because the skill relies on discovered schemas. Include the object type, campaign or event identifiers, date range, desired read/write action, matching rules, and whether the agent should pause before making changes.
Files and source path to inspect first
The key source file is:
composio-skills/givebutter-automation/SKILL.md
Read it for the required workflow: prerequisites, setup, tool discovery, connection check, execution, and result verification. There are no bundled scripts, references, or rule folders, so most implementation detail will come from Rube’s live tool discovery and Composio’s Givebutter toolkit documentation.
givebutter-automation skill FAQ
Is givebutter-automation beginner-friendly?
It is beginner-friendly if you already have an MCP-capable AI client and can authorize Givebutter through Rube. It is less friendly if you expect a point-and-click integration, because the agent must call MCP tools and interpret returned schemas. Beginners should start with read-only discovery or reporting tasks before allowing updates.
Can this replace the Givebutter dashboard?
No. The givebutter-automation skill is best used for repeatable operational workflows, bulk assistance, reporting preparation, and guided updates. The Givebutter dashboard is still better for manual review, visual campaign management, and sensitive one-off changes where a human should inspect records directly.
When should I not use this skill?
Do not use it when you cannot connect Rube MCP, when the Givebutter account has not been authorized, or when you need guaranteed support for a specific Givebutter action before tool discovery. Also avoid unsupervised write operations for donor, payment, campaign, or event data unless your prompt includes approval gates and verification steps.
Why must the agent search tools first?
The upstream skill explicitly requires RUBE_SEARCH_TOOLS before workflows. This is important because the current tool slugs, input schemas, recommended execution plans, and pitfalls are returned dynamically. Skipping discovery increases the risk of invalid fields, wrong assumptions, or failed automation.
How to Improve givebutter-automation skill
Improve prompts for givebutter-automation results
For better givebutter-automation usage, give the agent operational boundaries instead of only the end goal. Specify whether the task is read-only or can write data, what counts as a match, how to handle duplicates, whether to batch changes, and when to stop for approval.
Example:
Find supporters from campaign
Spring Gala 2025with donations over$250. Return a table with name, email, donation total, and missing fields. Do not update records. If an update tool exists, only describe the schema and ask before using it.
Add safeguards for write workflows
For updates, ask the agent to separate discovery, planning, execution, and verification. A strong workflow is: discover tools, check connection, fetch target records, summarize proposed changes, wait for approval, execute in small batches, then report successes and failures. This reduces the risk of accidental edits in donor or campaign data.
Common failure modes to watch
The most common problems are inactive Givebutter authorization, skipped tool discovery, vague object identifiers, and prompts that request unsupported operations. If the first result fails, ask the agent to show the exact Rube tool schema it used, the required fields it lacked, and the next safest alternative.
Iterating after the first output
After the first run, refine the task with the returned schema names and field requirements. For example, replace “get recent donors” with “use the discovered donor or transaction search tool for donations after 2025-01-01, filtered by campaign ID.” Iterating this way turns the givebutter-automation skill from a broad assistant into a reliable Givebutter workflow operator.