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 skills/itallstartedwithaidea/agent-skills/ml-model-integrationnpx skills add itallstartedwithaidea/agent-skills --skill ml-model-integrationgit clone --depth 1 https://github.com/itallstartedwithaidea/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/itallstartedwithaidea/agent-skills/ml-model-integration)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/ml-model-integration"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/ml-model-integration.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.01600 |
| Opus 5 | $0.00013 | $0.00800 |
| Sonnet 5 | $0.00005 | $0.00320 |
| Haiku 4.5 | $0.00003 | $0.00160 |
Grade A, and why
ml-model-integration 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Model Integration
Part of Agent Skills™ by googleadsagent.ai™
Description
ML Model Integration provides workflows for discovering, evaluating, and deploying machine learning models from HuggingFace Hub. The agent searches the model registry by task type, evaluates candidates on benchmark datasets, configures inference pipelines for local or API-based execution, and orchestrates fine-tuning workflows for domain adaptation.
HuggingFace Hub hosts 500,000+ models across hundreds of task types: text generation, image classification, object detection, speech recognition, translation, summarization, and more. Navigating this landscape requires understanding model architectures, license compatibility, hardware requirements, and benchmark performance. This skill encodes that knowledge, helping the agent select the right model for the right task at the right cost.
The skill covers the complete model lifecycle: discovery (searching by task, filtering by license and size), evaluation (running inference on test data, measuring latency and quality), deployment (local Transformers pipeline, HuggingFace Inference API, or self-hosted with TGI/vLLM), and fine-tuning (LoRA adapters for domain-specific customization with minimal training data).
Use When
- Selecting a model for a specific ML task (classification, generation, detection)
- Setting up inference pipelines locally or via API
- Fine-tuning a pre-trained model on domain-specific data
- Evaluating model quality against custom benchmarks
- Deploying models to production with optimized serving
- The user asks about HuggingFace, Transformers, or model selection
How It Works
graph TD
A[Task Definition] --> B[Search HuggingFace Hub]
B --> C[Filter: License, Size, Downloads]
C --> D[Shortlist Top-3 Candidates]
D --> E[Evaluate on Benchmark Data]
E --> F{Quality Sufficient?}
F -->|Yes| G[Deploy as Inference Pipeline]
F -->|No| H[Fine-tune with LoRA]
H --> I[Evaluate Fine-tuned Model]
I --> G
G --> J{Deployment Target}
J -->|Local| K[Transformers Pipeline]
J -->|API| L[HuggingFace Inference API]
J -->|Self-Hosted| M[TGI / vLLM Server]
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.
- 6d ago First seen · 177 lines · 26 tokens per session scan A 8030542f5237
ml-model-integration is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (36 stars, last pushed 4mo ago), licensed MIT. It adds 26 tokens to every session and 1,600 once invoked, about $0.0001 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.
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