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 skills add hamzabellouch/agent-skills --skill agent-platform-inferencegit clone --depth 1 https://github.com/hamzabellouch/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hamzabellouch/agent-skills/agent-platform-inference)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/agent-platform-inference"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/agent-platform-inference/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/agent-platform-inference"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/agent-platform-inference.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00125 | $0.04148 |
| Opus 5 | $0.00063 | $0.02074 |
| Sonnet 5 | $0.00025 | $0.00830 |
| Haiku 4.5 | $0.00013 | $0.00415 |
Grade A, and why
agent-platform-inference scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
> Garden page to get the exact `curl` or Python code for that specific model How it starts
The opening of the file, as written. The whole thing — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Platform GenAI Inference Skill
This skill provides instructions for authenticating and connecting to Google Cloud Agent Platform to use Generative AI models. It covers both First-Party (Gemini) and Third-Party (OpenMaaS) models.
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. (The skill is read-only; other safety tiers are omitted):
- Tier R: Read-only / Inference (
client.models.generate_content,client.chat.completions.create,client.completions.create,client.embeddings.create)- Requires interactive confirmation with 'Yes'/ 'No' options before executing model inference on behalf of the user, to prevent unexpected cost or quota consumption. The confirmation prompt must clearly explain the proposed inference execution and its key parameters (e.g., target model ID, SDK choice, input prompt). Natural-language paraphrases without specifying the parameters are NOT sufficient.
- Same-turn restriction: Do not execute the inference scripts or commands 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:
I will perform model inference with the following parameters. Please confirm this information before I proceed:
- Model ID:
deepseek-ai/deepseek-v3.2-maas - SDK: OpenAI SDK (via Vertex AI Endpoint)
- Input Prompt: "Explain the concept of quantum computing..." Do you confirm? [Yes/No]
- Model ID:
Phase 0: Environment Setup
CRITICAL: Before running any of the Python sample scripts in the scripts/
directory (e.g., scripts/openmaas_openai_sdk.py), you MUST ensure the
environment is correctly initialized by following these steps:
- Google Cloud Authentication: Authenticate with your Google Cloud credentials and configure active Application Default Credentials (ADC) for Agent Platform access:
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- scripts/gemini_genai_sdk.py 505 B runs code
- scripts/gemini_openai_sdk.py 875 B runs code
- scripts/gemini_vertexai_sdk.py 462 B runs code
- scripts/openmaas_genai_sdk.py 607 B runs code
- scripts/openmaas_openai_sdk.py 917 B runs code
- scripts/openmaas_vertexai_sdk.py 564 B runs code
- scripts/requirements.txt 56 B
- scripts/verify_all.sh 919 B runs code
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.
- 9d ago First seen · 372 lines · 125 tokens per session scan A d42d697cd961
agent-platform-inference is a skill published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 125 tokens to every session and 4,148 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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Universal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic…
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Prompt: Prompt Refinement and Optimization.
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model-evaluation
When evaluating a trained model, comparing versions, or performing fairness analysis.
orchestration
When designing DAGs, debugging pipeline failures, or configuring retries.
streaming-patterns
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