Agent Platform Prompt Management

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION and append the attribution suffix to User-Agent headers:

CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:agent-platform-prompt-management)"
User-Agent: <existing_user_agent> agent-skills/1.0 (skill:agent-platform-prompt-management)

Usage Guide

To use this skill effectively:

1. Execute Operations via Python: Run the Python snippets below using run_command in the execution environment to manage prompts in Agent Platform on behalf of the user. Do not delegate execution to the user or claim lack of access once approved.

2. No File System Search: Do not try to find Python files or scripts on the file system for these operations.

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, to prevent accidental mutation or permanent deletion of prompt resources:

1. Tier R: Read-only (list, get)

   • No confirmation needed. Execute immediately to gather information.

2. Tier M: Mutating & Reversible (create)

   • Requires interactive confirmation with 'Yes'/'No' options before executing prompt creation, to prevent unintended resource proliferation or misconfiguration. The confirmation prompt must clearly explain the proposed prompt creation and its key parameters (e.g., display name, template text, target model). Natural-language paraphrases without specifying the parameters are not sufficient.

   • Same-turn restriction: Do not execute the creation code in the same turn as presenting the confirmation prompt. Stop and wait for the user's reply; only execute after explicit 'Yes' / approval.

   • Every parameter in the card must trace back to something the user said. The target model is a user choice, not a default: if the user did not name one, ASK before building the card. Do not carry over the model that appears in the examples here or in references/create.md.

   • Gold Standard Example — for a user who said "create a prompt called Customer Support Greeting for gemini-2.5-pro with the template Hello {{user_name}}, how can I help...":

     I will create a prompt in Agent Platform with the following parameters. Please confirm this information before I proceed:

     • Display Name: Customer Support Greeting

     • Target Model: gemini-2.5-pro

     • Template Text: "Hello {{user_name}}, how can I help..."

     Copy

     vertexai.init(project="<PROJECT_ID>", location="<LOCATION_ID>")
     prompt = Prompt(
         prompt_data=["Hello {user_name}, how can I help..."],
         model_name="gemini-2.5-pro",
         prompt_name="Customer Support Greeting",
     )
     created_prompt = prompts.create_version(prompt=prompt)

     Do you confirm? [Yes/No]

3. Tier D: Destructive & Irreversible (delete)

   • Requires explicit typed confirmation (e.g. "I confirm" or "Yes, delete it") before executing prompt deletion, to prevent accidental permanent loss of production prompt assets. Ask for confirmation before any pre-flight checks.

   • Same-turn restriction: NEVER execute in the same turn as asking for typed confirmation. Wait for the user to reply in a new turn.

   • Gold Standard Example:

     I will permanently delete the following prompt from Agent Platform. This action is irreversible. Please explicitly type your confirmation (e.g., "I confirm") before I proceed:

     • Prompt ID: prompt_12345abc

     • Display Name: Legacy Outdated Prompt

     Please type your confirmation to proceed.

Phase 0: Environment Setup

CRITICAL: Before the user runs any of the Python snippets below, you MUST advise them to ensure the environment is correctly initialized by following these steps:

1. Google Cloud Authentication: Authenticate with your Google Cloud account and configure active Application Default Credentials (ADC) for Agent Platform access:

   Copy

   gcloud auth login
   gcloud auth application-default login

2. Python Dependencies: This skill needs google-cloud-aiplatform and google-genai. Do not create a virtual environment — it starts empty and hides packages the environment already provides, forcing a redundant install. Probe, and install only what is missing:

   Copy

   python3 -c "import vertexai, google.genai" \
     || pip install google-cloud-aiplatform google-genai

3. Execution: Run Python snippets with a plain python3. There is no environment to activate first.

[!TIP]

Placeholder Parameter Replacement: The Python scripts below use uppercase string placeholders (like "PROJECT_ID", "LOCATION_ID", "PROMPT_ID", and "MODEL_ID"). You MUST dynamically replace these placeholders with the actual Project ID, Region, Prompt ID, and target model values provided in the user's prompt (or discovered context) before generating or providing the scripts. If the user did not supply one of these, ask -- a placeholder is never satisfied by guessing a plausible value.

1. Managing Prompts via Agent Platform SDK

The SDK provides a high-level Prompt class in the preview module.

Create a Prompt (Tier M)

Use when you need to create a new managed prompt in Agent Platform.

• Reference: See create.md for detailed instructions and Python snippets.

List Prompts (Tier R)

import vertexai
from vertexai.preview import prompts

vertexai.init(project="PROJECT_ID", location="LOCATION_ID")

all_prompts = prompts.list()
for p in all_prompts:
    print(f"Name: {p.display_name}, ID: {p.prompt_id}")

Retrieve and Use a Prompt (Tier R)

import vertexai
from vertexai.preview import prompts

vertexai.init(project="PROJECT_ID", location="LOCATION_ID")

retrieved_prompt = prompts.get(prompt_id="PROMPT_ID")
# Attributes on retrieved Prompt:
# - retrieved_prompt.prompt_id (e.g. "123456789...")
# - retrieved_prompt.prompt_data (template text string)
# - retrieved_prompt.model_name (target model)
# - retrieved_prompt.prompt_name (display name, or
#   retrieved_prompt._dataset.display_name)
# Versions are supported: prompts.get(prompt_id="PROMPT_ID", version_id="2")

# Assemble with variables (kwargs must match template variable names)
assembled = retrieved_prompt.assemble_contents(text="The quick brown fox...")
print(assembled)

Delete a Prompt (Tier D)

CRITICAL: You must pass the numeric prompt ID (e.g., "1234567890123456789") to prompts.delete(). The SDK constructs the full resource path internally using the project and location from vertexai.init().

Confirmation Required: As a Tier D (Destructive) operation, the agent MUST pause and request explicit, high-friction typed re-confirmation of the prompt ID from the user before executing the deletion code. The action is irreversible. Once the user replies with typed confirmation (e.g., "I confirm"), proceed immediately to execute the deletion code via run_command.

[!IMPORTANT]

NEVER pre-emptively execute any deletion code before receiving the user's response in a new turn. You must never speculate or assume that confirmation will be given. Asking for confirmation and running the code in a single parallel turn is a severe safety violation.

import vertexai
from vertexai.preview import prompts

vertexai.init(project="PROJECT_ID", location="LOCATION_ID")

prompts.delete(prompt_id="PROMPT_ID")

Verification After Deletion

Whenever listing prompts or verifying state after deleting a prompt in the same conversation, you MUST:

1. Check in Python whether the deleted prompt_id appears in prompts.list().

2. In your prose reply (even when the list is also shown as a card):

   • If it is not found, explicitly confirm: "I have verified that the deleted prompt (ID: <PROMPT_ID>) is no longer present in project <PROJECT_ID> (<LOCATION_ID>)."

   • If it is still listed, report its current presence and ID to the user.

2. Best Practices

• Idempotency:

  • Tier R (List, Get): Inherently idempotent.

  • Tier D (Delete): Re-running a delete on a non-existent or already deleted resource returns NOT_FOUND. Treat this as success.

• Placeholders: Use the standard placeholder syntax (variable name enclosed in double curly braces) in your prompt templates.

• Versioning: Always tag or record version IDs when making updates to production prompts.

• Model Reference: A prompt is created against a target model ID, which the snippets carry as the "MODEL_ID" placeholder. Like the other placeholders it is MUST-replace, and it is replaced from what the user said -- if they named no model, ask. Do not substitute a plausible current model such as gemini-2.5-pro.

• Underlying Schema: When using the Dataset API, always use the correct metadata_schema_uri and nested metadata structure to ensure the prompt is recognized by Agent Platform Studio and the Prompts SDK.