Claude Automation Recommender

Claude Automation Recommender is an official skill from Anthropic's claude-plugins-official repository. According to the provider, it analyzes a codebase and recommends Claude Code extensions that fit the observed project. The extension types covered are hooks, subagents, skills, plugins, and MCP servers. The skill is a read-only analysis workflow: it examines project signals and produces recommendations, but it does not independently create or modify files. That boundary matters for teams that want to review proposals, control permissions, and deliberately place approved automation under version control.

A project profile instead of generic advice

The workflow first gathers context about the runtime, languages, frameworks, libraries, and repository structure. According to the provider, it checks common manifests and directories so that suggestions can reflect a specific codebase. Dependencies may indicate documentation needs, browser testing, databases, cloud services, or external work systems. Existing Claude Code configuration and context files also reveal which instructions are already available. The analysis is not a replacement for a team-owned architecture review. It is a structured starting point that must be checked against current code, operational policy, and the project's real security boundary.

Distinguishing five extension types

A core value of the skill is explaining which automation shape fits a need. According to the provider, hooks respond automatically to tool events and can support formatting, linting, or protective checks. Subagents package specialized reviewers or analyzers that may work separately or in parallel. Skills package reusable expertise and workflows. Plugins collect related skills into an installable bundle. MCP servers connect Claude Code to external tools, APIs, databases, or documentation sources. This vocabulary makes it easier to choose a small maintainable intervention rather than adding a broad collection without a clear owner.

Prioritizing and limiting recommendations

The provider instructs the workflow not to overwhelm people with possibilities. For each relevant category, it generally surfaces one or two high-value recommendations. When a user requests one specific type, the report can focus there and provide more alternatives. Selection should connect repository evidence with a real team objective. An existing test suite may justify a focused validation automation, while sensitive environment files may justify a protection hook. The skill can go beyond reference lists and use web search for recommendations tailored to the codebase's tools. Every external suggestion still needs independent verification of its operator, permissions, maintenance, and data flow.

Boundaries, security, and responsibility

The skill does not guarantee quality, security, or currency. Recommendations can reflect stale dependencies, partial repository views, or incorrect assumptions about how a team works. Suggestions involving hooks, shell execution, network access, deployment, databases, or external state require particular care. Credentials, tokens, private keys, and personal production data should never be placed in prompts, context files, or example configuration. An MCP server may be connected locally while inputs or results are still sent to the selected model provider. Before implementation, responsible maintainers should constrain permissions, inspect diffs, run tests, and define a rollback path. The skill analyzes and recommends; the team owns implementation, approval, and ongoing review.

Source and catalog context

The authoritative source is the official path plugins/claude-code-setup/skills/claude-automation-recommender. According to the provider, the surrounding repository is released under Apache-2.0. Anthropic's official Claude Code documentation provides complementary context about Claude Code and its extension model. This entry states no concrete prices or quotas because current conditions depend on the chosen Claude Code setup and the provider terms in force. The skill suits teams that want to derive automation from observable project signals and then expand their workflow under deliberate human control.