codacy-automation
by ComposioHQcodacy-automation helps Claude run Codacy workflows through Composio Rube MCP by discovering live tool schemas, checking the Codacy connection, and executing safer automation steps.
Score: 68/100. This is an acceptable but limited listing candidate: directory users can understand that it automates Codacy through Composio/Rube MCP and how to initialize tool discovery and authentication, but the repository evidence is thin and mostly contained in a single SKILL.md with limited concrete Codacy workflow detail.
- Frontmatter is valid and declares the required MCP dependency (`rube`), making the basic activation context clear.
- Prerequisites and setup steps identify the needed Rube MCP server, Codacy connection, and connection-status check before running workflows.
- The skill gives an explicit operational rule to call `RUBE_SEARCH_TOOLS` first for current Codacy schemas, reducing stale-tool-schema risk.
- No support files, scripts, references, or README are present beyond SKILL.md, so users get little validation or runnable scaffolding.
- The workflow relies on dynamic Rube tool discovery and the excerpt shows generic patterns more than concrete Codacy task examples, which may leave agents guessing for specific operations.
Overview of codacy-automation skill
What codacy-automation is for
codacy-automation is a Claude skill for running Codacy workflows through Composio’s Rube MCP server. Instead of hard-coding Codacy API calls or guessing tool names, the skill instructs the agent to discover the current Codacy toolkit schema first, verify the Codacy connection, and then execute the appropriate Rube MCP tools.
The main job-to-be-done is practical Workflow Automation: ask an agent to inspect or act on Codacy data while respecting the live tool schemas returned by Rube. This matters because MCP tool names, inputs, and execution plans can change; the skill’s strongest differentiator is its “search tools first” pattern.
Best-fit users and workflows
The codacy-automation skill is best for developers, DevOps engineers, engineering managers, and AI workflow builders who already use Codacy and want Claude to help operate it from an MCP-enabled environment. Typical goals include discovering available Codacy actions, checking project quality information, automating review-related tasks, or building repeatable agent workflows around Codacy data.
It is not a standalone Codacy CLI, and it does not replace Codacy configuration inside your repository. It is most useful when you want an assistant to call Rube MCP tools safely and adapt to the current Codacy toolkit interface.
Key adoption requirements
Before installing or invoking the skill, confirm that your AI client supports MCP and can connect to Rube. The upstream skill declares requires: mcp: [rube], so adoption depends on Rube MCP availability, not just the markdown file.
You also need an active Codacy connection through RUBE_MANAGE_CONNECTIONS with toolkit codacy. If the connection is not active, the agent must follow the returned authorization flow before attempting Codacy operations.
How to Use codacy-automation skill
codacy-automation install context
Install the skill from the Composio skills repository:
npx skills add ComposioHQ/awesome-claude-skills --skill codacy-automation
Then add Rube MCP as an MCP server in your client configuration using:
https://rube.app/mcp
The skill itself does not include helper scripts, local config templates, or extra reference files; the practical source to read first is composio-skills/codacy-automation/SKILL.md. Also review Composio’s Codacy toolkit documentation at https://composio.dev/toolkits/codacy if you need to understand the available Codacy-side capabilities.
Inputs the skill needs
A strong codacy-automation usage prompt should include the specific Codacy outcome, target scope, and any constraints. Avoid asking “check Codacy” without naming the repository, organization, project, branch, pull request, time window, or report format you need.
A weak prompt:
Use Codacy to review my project.
A stronger prompt:
Use codacy-automation. First discover the current Rube Codacy tools and schemas. Confirm the Codacy connection is ACTIVE. Then find what Codacy information is available for the
my-org/my-repoproject and summarize actionable quality issues for the default branch. Do not make changes; produce a concise report with tool names used and any missing permissions.
This helps the agent select a use case for RUBE_SEARCH_TOOLS, preserve session context, and avoid inventing unavailable Codacy actions.
Recommended workflow
Use this sequence for reliable results:
- Ask the agent to call
RUBE_SEARCH_TOOLSfor the exact Codacy task. - Reuse the returned session ID when checking connections and executing tools.
- Ask it to call
RUBE_MANAGE_CONNECTIONSfor toolkitcodacy. - Proceed only after the Codacy connection status is
ACTIVE. - Have the agent restate the selected tool slug, required input fields, and execution plan before running higher-impact actions.
- Request a final summary that separates Codacy findings from MCP/tooling limitations.
The repository’s core guidance is important: always search tools first for current schemas. Do not rely on examples from an old chat or copied input shapes unless Rube returns the same schema in the current session.
Practical prompt pattern
Use prompts shaped like this:
Use the codacy-automation skill for
[task]. Start withRUBE_SEARCH_TOOLSusing the use case[specific Codacy operation]. Check thecodacyconnection withRUBE_MANAGE_CONNECTIONSand stop if it is not ACTIVE. If tools are available, show me the required schema before execution. Then run the safest read-only workflow first and summarize results with any missing fields, permissions, or ambiguous project identifiers.
This pattern is especially useful when you are unsure whether the Codacy toolkit exposes the exact operation you want.
codacy-automation skill FAQ
Is codacy-automation only for Codacy users?
Yes. The codacy-automation skill is only useful if your team uses Codacy and you can authorize a Codacy connection through Rube MCP. Without an active Codacy connection, the skill can help discover setup steps, but it cannot complete Codacy operations.
How is this better than an ordinary prompt?
An ordinary prompt may cause the agent to guess tool names or assume stale schemas. This skill adds an operational discipline: discover available tools with RUBE_SEARCH_TOOLS, check the codacy connection, and execute against the current schema returned by Rube. That makes it safer for Workflow Automation than asking the model to improvise.
Can beginners use this skill?
Beginners can use it if their client already supports MCP, but setup may be the main hurdle. You need to know where your client stores MCP server configuration, how to add https://rube.app/mcp, and how to complete the Rube-managed Codacy authorization flow. If you are new to MCP, test with a read-only Codacy query first.
When should I not use codacy-automation?
Do not use it for tasks that belong in Codacy’s own UI, repository configuration, or CI pipeline unless you specifically want agent-mediated MCP automation. Also avoid using it for destructive or broad organization-level actions until the agent has displayed the live tool schema, target scope, and permission assumptions.
How to Improve codacy-automation skill
Improve codacy-automation results with clearer scope
The most common failure mode is underspecified scope. Give the agent identifiers that reduce ambiguity: organization name, project or repository name, branch, commit, pull request, severity threshold, and desired output format. If you do not know the exact Codacy project identifier, ask the agent to first discover available projects and confirm the target before continuing.
Ask for schema confirmation before execution
Because the skill depends on live Rube MCP tool discovery, make schema confirmation part of your workflow. A good instruction is:
Before executing any Codacy tool, list the selected tool slug, required fields, optional fields you plan to omit, and why the tool matches my request.
This reduces bad calls, exposes missing permissions early, and makes the agent’s reasoning auditable.
Iterate after the first output
For the first run, prefer read-only discovery and reporting. After the agent returns results, refine the request with concrete follow-ups such as “filter to high-severity issues,” “group by file path,” “compare default branch and pull request results if supported,” or “turn this into a weekly engineering report format.”
Iteration works better than one large prompt because the agent can adapt to the actual tool capabilities returned by RUBE_SEARCH_TOOLS.
Strengthen the skill itself
If you maintain a local copy, the most useful improvements would be small examples for common Codacy workflows, a troubleshooting section for inactive connections, and prompt templates for read-only versus write-capable operations. Keep the central rule intact: codacy-automation should always discover current Rube Codacy schemas before attempting execution.