raisely-automation
by ComposioHQraisely-automation helps agents automate Raisely workflows through Composio Rube MCP by discovering current tool schemas, checking the raisely connection, and executing safer tool calls.
Score: 68/100. This is an acceptable but limited directory listing: it gives agents enough trigger and setup guidance to use Composio/Rube for Raisely automation with less guesswork than a generic prompt, but directory users should understand that most operational detail is delegated to live tool discovery rather than documented workflows in the repository.
- Valid frontmatter clearly names the skill, describes Raisely automation, and declares the required `rube` MCP dependency.
- Prerequisites and setup steps tell agents to verify `RUBE_SEARCH_TOOLS`, manage a Raisely connection with `RUBE_MANAGE_CONNECTIONS`, and confirm ACTIVE status before running workflows.
- The skill gives a repeatable execution pattern: search current tool schemas first, check the connection, then use discovered Raisely tools rather than relying on stale hardcoded schemas.
- No support files, README, install command, scripts, or local references are provided beyond the single SKILL.md, so adoption depends on already knowing how to add the Rube MCP endpoint.
- The guidance is intentionally generic and schema-discovery-driven; it does not include many concrete Raisely task examples or edge-case handling beyond connection/tool discovery.
Overview of raisely-automation skill
What raisely-automation does
raisely-automation is a Claude skill for running Raisely-related workflow automation through Composio’s Rube MCP server. Instead of assuming fixed API shapes, the skill instructs the agent to discover the current Raisely tool schemas first, check the active connection, and then execute the correct Rube tool calls for the requested Raisely operation.
Best fit for Raisely operators and automation builders
This skill is most useful if you already use Raisely for fundraising, campaigns, donations, supporters, or nonprofit operations and want an AI agent to help perform actions through connected tools rather than only draft instructions. It fits users who need guided automation for Raisely tasks but do not want to manually inspect Composio toolkit schemas every time.
Key differentiator: tool discovery before execution
The important design choice in raisely-automation is the “search tools first” workflow. The skill tells the agent to call RUBE_SEARCH_TOOLS before taking action, so it can retrieve current tool slugs, input schemas, execution plans, and pitfalls. This reduces failures caused by stale assumptions about Raisely fields or Composio tool names.
Adoption requirements to check first
This is not a standalone Raisely API wrapper. It requires Rube MCP to be available in your client, with the raisely toolkit connected and active through RUBE_MANAGE_CONNECTIONS. If your environment cannot use MCP tools, or you only need written Raisely advice with no tool execution, a normal prompt may be enough.
How to Use raisely-automation skill
raisely-automation install and setup path
Install the skill from the Composio skill collection, then connect Rube MCP in your AI client:
npx skills add ComposioHQ/awesome-claude-skills --skill raisely-automation
Add https://rube.app/mcp as an MCP server in the client where you run the skill. Then verify that RUBE_SEARCH_TOOLS is available. Use RUBE_MANAGE_CONNECTIONS with toolkit raisely; if the connection is not ACTIVE, follow the returned authentication link and confirm the connection before asking the agent to run live workflows.
Inputs the skill needs for reliable usage
For strong raisely-automation usage, give the agent the operational goal, the Raisely object type, any known identifiers, the desired output, and safety constraints. A weak request is: “Update my Raisely campaign.” A stronger prompt is: “Use raisely-automation to find the current Raisely tools, confirm the raisely connection is active, then update the campaign with ID camp_123 by changing the public title to Spring Appeal 2026. Do not modify donation settings. Show the tool schema you plan to use before executing.”
This improves results because the agent can map your intent to the discovered schema and avoid broad, risky changes.
Practical workflow for live Raisely automation
A good raisely-automation guide follows four steps. First, ask the agent to run RUBE_SEARCH_TOOLS for the exact use case, such as “Raisely supporter lookup” or “create campaign donation report.” Second, have it verify the Raisely connection with RUBE_MANAGE_CONNECTIONS. Third, ask it to summarize the selected tool, required fields, optional fields, and risks before execution. Fourth, run the action and review the returned result for IDs, status messages, and any partial failures.
For sensitive operations, add a confirmation gate: “Prepare the tool call but wait for my approval before executing.”
Repository file to read first
The repository path is composio-skills/raisely-automation, and the main file to inspect is SKILL.md. There are no visible helper scripts, rules, resources, or README files in the preview, so the install decision mostly depends on whether the core MCP workflow matches your environment. Read the prerequisites, setup, tool discovery, and core workflow sections before installing.
raisely-automation skill FAQ
Is raisely-automation for Workflow Automation or general advice?
raisely-automation for Workflow Automation is the right framing. The skill is intended to help an agent discover and call Raisely tools through Rube MCP. It can still explain steps, but its main value is operational execution through connected tools, not fundraising strategy or generic Raisely documentation.
How is this better than an ordinary prompt?
An ordinary prompt may invent fields, assume outdated API shapes, or skip connection checks. The raisely-automation skill encodes a safer sequence: discover tools, confirm the Raisely connection, inspect schemas, then execute. That pattern is especially useful when Composio tool schemas change or when the exact Raisely action requires structured inputs.
Can beginners use it?
Yes, if they are comfortable connecting an MCP server and authenticating the Raisely toolkit. Beginners should start with read-only or low-risk tasks such as listing campaigns, checking supporter records, or retrieving available tool schemas. Avoid bulk updates or financial configuration changes until you understand the confirmation and review flow.
When should I not install it?
Do not install raisely-automation if your AI client cannot use MCP tools, your organization does not allow third-party tool connections to Raisely, or you only need copywriting, campaign planning, or documentation help. Also avoid it for unsupervised high-impact changes unless you add explicit approval checkpoints.
How to Improve raisely-automation skill
Improve raisely-automation prompts with exact task context
The best results come from prompts that include the Raisely entity, identifiers, intended change, allowed scope, and success criteria. For example: “Search for the current Raisely tools for supporter lookup, confirm the active connection, then retrieve supporter details for email name@example.org. Do not create or update records. Return the matching supporter ID, campaign associations, and any ambiguity.”
This gives the agent enough context to choose the right schema and avoid accidental writes.
Add guardrails for risky Raisely operations
For updates, deletions, imports, exports, and donation-related changes, require a plan-before-execute step. Ask the agent to show the discovered tool slug, required parameters, missing information, and expected side effects. If a tool can affect multiple records, require a dry-run-style summary where possible, or ask for a filtered preview before executing.
Common failure modes to watch for
The most common blockers are inactive Raisely connection status, missing MCP access, vague task descriptions, and stale assumptions about field names. If execution fails, do not immediately retry the same call. Ask the agent to run RUBE_SEARCH_TOOLS again with the specific failed use case and compare the returned schema with the attempted parameters.
Iterate after the first output
After the first result, verify whether the returned data matches the intended Raisely account, campaign, or supporter record. Then refine the prompt with concrete follow-up instructions such as “limit this to campaign ID X,” “include only active supporters,” or “prepare the update call but wait for approval.” This keeps raisely-automation precise while still benefiting from agent-driven workflow automation.