Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/hamzabellouch/agent-skillsnpx agentmods add skills/hamzabellouch/agent-skills/agent-platform-tuningWrote 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)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/agent-platform-tuning"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/agent-platform-tuning/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"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/agent-platform-tuning.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 | $0.00064 | $0.04066 |
| Opus 5 | $0.00032 | $0.02033 |
| Sonnet 5 | $0.00013 | $0.00813 |
| Haiku 4.5 | $0.00006 | $0.00407 |
Grade A, and why
agent-platform-tuning 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 12d 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
83% identical to agent-platform-tuning — 360 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 — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Platform Model Tuning
Overview
This skill provides procedural knowledge for fine-tuning Large Language Models (both Open Models and Gemini Models) using Agent Platform's tuning service. It covers the entire lifecycle from environment setup and data preparation to job configuration, monitoring, and deployment.
Workflow Decision Tree
-
Model Category Identification: Has the user explicitly stated whether they want to tune an Open Model or a Gemini Model?
- No → STOP. Ask the user if they want to tune an Open Model or a Gemini Model. CRITICAL EXCEPTION for Environment Setup Requests: If the user is specifically asking for environment setup instructions (e.g. "What environment setup is needed?"), you MUST provide the full Phase 0 environment setup instructions in your initial response, simultaneously with asking clarifying questions about the model category.
- If the user provides a specific tuning purpose, you should
recommend three models: one Open Model, one Gemini Model, and a third
generally recommended choice. Briefly list the pros and cons of each
(e.g., Gemini models might be more expensive, etc.). CRITICAL: You
must read
references/models.mdduring this step and only recommend models explicitly listed in that catalog. Do not recommend unsupported models like Mistral. Do not proceed with model configuration until the category is confirmed. - Yes → Proceed.
-
Environment Check: Has the environment (Auth, APIs, IAM, Venv) been initialized?
- No → Go to Phase 0: Environment & IAM Setup.
- Yes → Proceed.
-
Dataset Status: Is the dataset ready in JSONL format, is its structure valid for tuning, and is it uploaded to Google Cloud Storage?
- **No** → Go to [Phase 1: Dataset Preparation & Upload](#phase-1). - **Yes** → Proceed.
What ships with it
10 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.
- references/data_prep.md 1.6 KB
- references/hf_datasets.md 4.9 KB
- references/models.md 2.7 KB
- references/requirements.txt 137 B
- references/tuning_guide.md 2.2 KB
- scripts/calculate_cost.py 4.8 KB runs code
- scripts/cancel_tuning_job.py 999 B runs code
- scripts/monitor_tuning_job.py 2.1 KB runs code
- scripts/prepare_dataset.py 8.0 KB runs code
- scripts/tune_open_model.py 3.0 KB 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.
- 12d ago First seen · 393 lines · 64 tokens per session scan A 7363015fcdfe
agent-platform-tuning is a skill published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 4,066 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to agent-platform-tuning, differing in 360 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.
streaming-patterns
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feature-engineering
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inference-serving
When deploying a model to an API endpoint or optimizing inference latency.
model-evaluation
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