ml-model-integration

ml-model-integration is a skill for Claude Code from itallstartedwithaidea/agent-skills. It costs 26 tokens per session (1,600 once invoked), scanned A, original, MIT.

A workflow for finding, testing, deploying, and adapting machine-learning models from Hugging Face Hub, a public registry of models for tasks such as text, images, speech, and translation.

In plain words
What is it for?
It helps search for models, test them on data, run them locally or through an API, deploy them with common serving tools, and fine-tune them for a specific domain.
Why use it?
It reduces the work of comparing models by task, license, hardware needs, speed, and quality before choosing one to use.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions Claude Code; built for cline.

Part of the all-skills plugin — 73 skills shipped together

Good fit It helps search for models, test them on data, run them locally or through an API, deploy them with common serving tools, and fine-tune them for a specific domain.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/itallstartedwithaidea/agent-skills/ml-model-integration
Install

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.

Any agent
npx skills add itallstartedwithaidea/agent-skills --skill ml-model-integration
Clone the repo
git clone --depth 1 https://github.com/itallstartedwithaidea/agent-skills

Made for: Claude Code.

Or install all-skills, the plugin that ships this one along with the rest of its 73 skills.

Wrote 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.

agentmods badge for ml-model-integration

README.md
[![agentmods](https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/ml-model-integration/github.svg)](https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/ml-model-integration)
Your own site
<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/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.

agentmods 80×15 button for ml-model-integration

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,600 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00026 $0.01600
Opus 5.5 $0.00010 $0.00640
Sonnet 5.5 $0.00005 $0.00320
Haiku 4.5 $0.00003 $0.00160

Measured 1mo ago against content hash 8030542f5237, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo 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.

skills/media-creative/ml-model-integration/SKILL.md · 177 lines

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]

Read the full file on GitHub · 177 lines

Changes

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.

  1. 1mo ago First seen · 177 lines · 26 tokens per session scan A 8030542f5237

Subscribe to this mod's changes

ml-model-integration is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 26 tokens to every session and 1,600 once invoked, about $0.0001 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

app-ai-binding

Build AI features in generated apps using the platform-provided Workers AI binding. Load this skill for chatbots, summarization, classification, embeddings, semantic search, image understanding, transcription, translation, or other model inference.

cloudflare/vibesdk · 48 tokens

ai-hive-simulate-aiera

A research and content-planning workflow that studies Aiera’s public work on AI, technology, products, engineering, and education. It turns those observations into original material and advisory analysis without impersonating the source.

wubin1836/ai-hive-agent-skills · 293 tokens

ai-hive-simulate-aigc-study-room

A research and content-planning workflow that studies AIGC Study Room’s public work on AI tools, agents, AI programming, visual content, products, and business. It turns those observations into original material and advisory analysis without impersonating the source.

wubin1836/ai-hive-agent-skills · 366 tokens

ai-hive-simulate-alex-hormozi

A research and content-planning workflow that studies Alex Hormozi’s public work on AI, technology startups, products, business, and teaching. It turns those observations into original material and advisory analysis without impersonating him.

wubin1836/ai-hive-agent-skills · 322 tokens

ai-hive-simulate-alexandr-wang

A research and content-planning workflow that studies Alexandr Wang’s public work on AI, technology startups, products, business, and teaching. It turns those observations into original material and advisory analysis without impersonating him.

wubin1836/ai-hive-agent-skills · 305 tokens

ai-hive-simulate-ali-abdaal

A research and content-planning workflow that studies Ali Abdaal’s public work on AI, technology startups, products, business, and teaching. It turns those observations into original material and advisory analysis without impersonating him.

wubin1836/ai-hive-agent-skills · 321 tokens