Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/gemini-cli-extensions/vertex/gemini-mdgit clone --depth 1 https://github.com/gemini-cli-extensions/vertexWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.05598 | $0.05598 |
| Opus 5 | $0.02799 | $0.02799 |
| Sonnet 5 | $0.01120 | $0.01120 |
| Haiku 4.5 | $0.00560 | $0.00560 |
Grade A, and why
vertex GEMINI.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- vertex-ai-mcp-bridge GEMINI.md — 97% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 450 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vertex AI Gemini CLI Extension
This extension provides tools to manage prompts and use prompt optimization in Vertex AI directly from the Gemini CLI.
Available Tools
Prompt Management Tools
create_prompt: To save or create new prompts.read_prompt: To retrieve existing prompts by ID or display name.update_prompt: To modify existing prompts.delete_prompt: To remove prompts.list_prompts: To search and list prompts, useful for finding IDs.
Prompt Optimization Tools
run_few_shot_optimization: optimize user prompt based on examples provided in the CSV file stored in the GCS bucket.run_data_driven_optimize: Starts a data-driven prompt optimization job on Vertex AI using a configuration file stored in GCS.analyze_data_driven_optimize_results: Analyzes the output of a Data-Driven Optimize job to identify trends and best-performing candidates.generate_html_report: Generates a comprehensive HTML report with visualizations to help you understand optimization performance.write_data_driven_optimize_config: Constructs and uploads a new JSON configuration for optimization jobs, incorporating suggested tuning parameters.
Detailed Instructions for create_prompt Parameters
When using tools.create_prompt, pay special attention to how the following
arguments are sourced:
-
content(string, required):- Scenario 1: Saving the Last User Prompt: When the user issues a command
like "save last prompt", "save this prompt", or similar, indicating they
want to store their previous input:
- Examine the conversation history.
- Identify the most recent message with a
roleof "user". - Extract the
textcontent from this latest "user" message.
- Scenario 2: Creating from Explicitly Provided Content: If the user
provides the prompt content directly within the command (e.g., "Create a
prompt... with content '...'")
- Use the explicitly provided content.
- If no content can be determined from either scenario, pass an empty string.
- Scenario 1: Saving the Last User Prompt: When the user issues a command
like "save last prompt", "save this prompt", or similar, indicating they
want to store their previous input:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 450 lines · 5,598 tokens per session scan A 6b5da03c1ab4
vertex GEMINI.md is an instructions file published in the GitHub repository gemini-cli-extensions/vertex (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 5,598 tokens to every session, about $0.0280 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
prompty copilot-instructions.md
Instructions for microsoft/prompty, covering prompty v2 — complete rebuild plan, what prompty uses from the typra-generated model, key design decisions, python coding rules and environment & tooling.
AgentX ai.instructions.md
AI and ML specific coding instructions for building AI agents, LLM integrations, and intelligent workflows.
ai-prompts AGENTS.md
Instructions for liatrio-labs/ai-prompts, covering ai agent instructions, context marker, local skill scripts, standard workflow and initialize.
vscode-copilot-chat model-prompts.instructions.md
Model-specific prompt authoring and registry guidelines.
comfy-prompt-studio AGENTS.md
Instructions for yxhpy/comfy-prompt-studio, covering agents.md - ai 代理配置文档, ai 提供商, 1. ollama (默认), 2. gemini and 提示词生成服务.
ZipAI CLAUDE.md
Claude Code instructions for nickdesi/ZipAI, covering claude.md — zipai: ultra-dense token optimizer, rules, 1. zero filler, 2. ambiguity and 3. prompt caching.