# Agent Platform Rag Engine Management

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using...

- Repository: https://github.com/google/skills (21764 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add google/skills --skill agent-platform-rag-engine-management -g -a claude-code -y`
Codex: `npx skills add google/skills --skill agent-platform-rag-engine-management -g -a codex -y`
Cursor: `npx skills add google/skills --skill agent-platform-rag-engine-management -g -a cursor -y`
Gemini CLI: `npx skills add google/skills --skill agent-platform-rag-engine-management -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add google/skills`

## Description

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Agent Platform RAG Engine Management

This skill provides instructions on how to interact with Agent Platform RAG
Engine using the Agent Platform Python SDK. You MUST use the `vertexai` Python
SDK to perform RAG Engine operations, rather than raw REST calls or MCP tools,
because this code is intended to be run by external clients.

## Safety & Confirmation Tiers (CRITICAL)

Before executing any commands or scripts on behalf of the user, you must adhere
to the following safety tiers based on the action requested:

1.  **Tier R: Read-only (`list_corpora`, `list_files`, `get_corpus`,
    `retrieval_query`)**
    *   No confirmation needed. Execute immediately to gather information or
        retrieve grounded contexts.
2.  **Tier RC: Read-only but consumes Compute Resources
    (`client.models.generate_content`)**

    *   Requires **interactive confirmation** with 'Yes'/'No' options before
        executing grounded content generation. The confirmation prompt MUST
        clearly explain the proposed generation execution and its key parameters
        (e.g., target corpus ID, query text, target model). Natural-language
        paraphrases without specifying exact parameters are insufficient, as
        explicit parameter listing is required to ensure unambiguous user
        approval of the specific resource and configuration.
    *   **Same-turn restriction**: Do not execute the generation code in the
        same turn as presenting the confirmation prompt. Stop and wait for the
        user's reply; only execute after explicit 'Yes' / approval.
    *   **Gold Standard Example**:

The rest: https://github.com/google/skills/blob/HEAD/skills/cloud/agent-platform-rag-engine-management/SKILL.md