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-tuning-managementgit 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-tuning-management)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/agent-platform-tuning-management"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/agent-platform-tuning-management/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-tuning-management"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/agent-platform-tuning-management.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 | $0.00072 | $0.01498 |
| Opus 5 | $0.00036 | $0.00749 |
| Sonnet 5 | $0.00014 | $0.00300 |
| Haiku 4.5 | $0.00007 | $0.00150 |
Grade A, and why
agent-platform-tuning-management 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 11d 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
88% identical to agent-platform-tuning-management — 75 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Platform Tuning Management
This skill provides instructions on how to manage GenAI Tuning Jobs using the Agent Platform Python SDK. Use this skill when a user wants to check the status of their tuning runs, find an active tuning job, or cancel a job that is running too long.
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,get)- Rule: No confirmation needed. You may execute these commands immediately to gather information for the user.
- Tier D: Destructive & Interruptive (
cancel)- Rule: This requires explicit typed confirmation. You MUST output a text message to the user explaining that this will stop the tuning process and any progress will be lost, and asking them to type "I confirm" or "Yes, cancel it". You MUST ask for this confirmation IMMEDIATELY, before executing the cancel command.
Phase 0: Environment Setup
CRITICAL: Before running any of the Python snippets below, you MUST ensure the environment is correctly initialized by following these steps:
-
Virtual Environment: Create and activate a virtual environment:
python3 -m venv ~/tuning_mgr_venv source ~/tuning_mgr_venv/bin/activate -
Google Cloud Authentication: Authenticate with your Google Cloud account and configure active Application Default Credentials (ADC) for Agent Platform access:
gcloud auth login gcloud auth application-default login -
Install Dependencies: Install the required Agent Platform SDK:
pip install google-cloud-aiplatform -
Execution: Advise the user that every time they execute a Python snippet, they must ensure this virtual environment is activated first.
Workflow Decision Tree
- Information Gathering: Do you have a Project ID and Region?
- No -> You MUST ask the user for the missing Project ID and Region in plain text, or advise them to check their gcloud configuration. If neither location has this information, then ask the user to provide it. Do not attempt to search random regions on your own.
- Yes -> Proceed to Step 2.
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.
- 11d ago First seen · 163 lines · 72 tokens per session scan A 7dbfb54277b1
agent-platform-tuning-management is a skill published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 72 tokens to every session and 1,498 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to agent-platform-tuning-management, differing in 75 lines, and is treated as a copy.
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