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-model-registrygit 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-model-registry)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/agent-platform-model-registry"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/agent-platform-model-registry/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-model-registry"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/agent-platform-model-registry.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 | $0.00060 | $0.01054 |
| Opus 5 | $0.00030 | $0.00527 |
| Sonnet 5 | $0.00012 | $0.00211 |
| Haiku 4.5 | $0.00006 | $0.00105 |
Grade A, and why
agent-platform-model-registry 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 10d 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.
This is a copy
91% identical to agent-platform-model-registry — 70 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Platform Model Registry Management
Overview
This skill provides instructions for managing machine learning models in the Agent Platform Model Registry. It covers listing models, describing model details, uploading new models or versions, updating metadata, and deleting models.
Safety & Confirmation Tiers (CRITICAL)
Before executing any commands on behalf of the user, you MUST adhere to the following safety tiers based on the action requested:
- Tier R: Read-only (
list,describe,get)- No confirmation needed. Execute immediately to gather information.
- Tier M: Mutating & Reversible (
upload,update)- Requires interactive confirmation with 'Yes'/'No' options. The
confirmation prompt MUST contain the exact, literal command string
with all required flags (e.g.
--region=us-central1,--display-name="...") — natural-language paraphrases are NOT sufficient. - Same-turn restriction: NEVER execute the command in the same turn as presenting the confirmation prompt. Stop and wait for the user's reply; only execute after explicit 'Yes' / approval.
- Requires interactive confirmation with 'Yes'/'No' options. The
confirmation prompt MUST contain the exact, literal command string
with all required flags (e.g.
- Tier D: Destructive & Irreversible (
delete)- Requires explicit typed confirmation (e.g. "I confirm" or "Yes, delete it"). Ask for confirmation IMMEDIATELY — before any pre-flight checks (don't check if the model is deployed to endpoints first).
- Same-turn restriction: NEVER execute in the same turn as asking for typed confirmation. Wait for the user to reply in a new turn.
Phase 0: Environment Setup
CRITICAL: Before running any commands, 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:
gcloud auth login gcloud auth application-default login -
Set Project: Configure the active project for subsequent commands:
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.
- 10d ago First seen · 141 lines · 60 tokens per session scan A ba64c6688033
agent-platform-model-registry is a skill published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 1,054 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to agent-platform-model-registry, differing in 70 lines, and is treated as a copy.
Other skills, from other repositories
prompt-engineering
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…
fixing-prompt
Prompt: Prompt Refinement and Optimization.
feature-engineering
When building training datasets, designing feature pipelines, or debugging training-serving skew.
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
When designing Kafka consumers/producers or implementing real-time pipelines.