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Agent skill

Adapting Transfer Learning Models

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques.

MITAuto-check passedAI & LLM Engineering

Install Adapting Transfer Learning Models

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill adapting-transfer-learning-models -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace adapting-transfer-learning-models --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/adapting-transfer-learning-models .claude/skills/adapting-transfer-learning-models && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
adapting-transfer-learning-models
GitHub stars
2.8k
Token cost
~1.1k tokens
SKILL.md length
502 words
Files
7 (incl. scripts, references, assets)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques.

  • Works in 5 steps: Analyze Requirements: Examines the… → Generate Adaptation Code: Creates Python… → Implement Validation and Error Handling:… → …
  • Requests assistance with fine-tuning a model
  • SKILL.md covers Overview, How It Works, When to Use This Skill and Examples, plus 7 more sections
  • Runs Python scripts from its folder

What it does

Adapting Transfer Learning Models is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. it is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts, reference files and assets (for example `assets/README.md`, `assets/data_preprocessing_example.py` and `assets/example_config.json`). Compatibility notes: Designed for Claude Code

It sits in AI & LLM Engineering, covering Fine-tuning, Machine learning and Computer vision. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Requests assistance with fine-tuning a model
  • Adapting a pre-trained model to a new dataset
  • Appropriate context detected
  • With relevant phrases based on skill purpose

Example prompts

  • “/adapting-transfer-learning-models”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(cmd:*)

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Analyze Requirements: Examines the user's request to understand the target task, dataset characteristics, and desired performance metrics.
  2. Generate Adaptation Code: Creates Python code using appropriate ML frameworks (e.g., TensorFlow, PyTorch) to fine-tune the pre-trained…
  3. Implement Validation and Error Handling: Adds code to validate the data, monitor the training process, and handle potential errors…
  4. Provide Performance Metrics: Calculates and reports key performance indicators (KPIs) such as accuracy, precision, recall, and F1-score to…
  5. Save Artifacts and Documentation: Saves the adapted model, training logs, performance metrics, and automatically generates documentation…

What it can do on your machine

Read from SKILL.md and the folder at commit d57fcd5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash(cmd:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Adapting Transfer Learning Models loads about 1.1k tokens when it runs, and up to ~1.1k if it reads all its reference files. Until then it costs about 96 tokens; SKILL.md has 502 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~96
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit d57fcd5, republished under its MIT licence (© jeremylongshore). 502 words, ~1,094 tokens.

Download SKILL.mdSave it as .claude/skills/adapting-transfer-learning-models/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
adapting-transfer-learning-models
description
Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. it is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(cmd:*)
compatibility
Designed for Claude Code
version
1.22.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
ai, ml, adapting-transfer

Transfer Learning Adapter

Adapt pre-trained models (ResNet, BERT, GPT) to new tasks and datasets through fine-tuning, layer freezing, and domain-specific optimization.

Overview

This skill streamlines the process of adapting pre-trained machine learning models via transfer learning. It enables you to quickly fine-tune models for specific tasks, saving time and resources compared to training from scratch. It handles the complexities of model adaptation, data validation, and performance optimization.

How It Works

  1. Analyze Requirements: Examines the user's request to understand the target task, dataset characteristics, and desired performance metrics.
  2. Generate Adaptation Code: Creates Python code using appropriate ML frameworks (e.g., TensorFlow, PyTorch) to fine-tune the pre-trained model on the new dataset. This includes data preprocessing steps and model architecture modifications if needed.
  3. Implement Validation and Error Handling: Adds code to validate the data, monitor the training process, and handle potential errors gracefully.
  4. Provide Performance Metrics: Calculates and reports key performance indicators (KPIs) such as accuracy, precision, recall, and F1-score to assess the model's effectiveness.
  5. Save Artifacts and Documentation: Saves the adapted model, training logs, performance metrics, and automatically generates documentation outlining the adaptation process and results.

When to Use This Skill

This skill activates when you need to:

  • Fine-tune a pre-trained model for a specific task.
  • Adapt a pre-trained model to a new dataset.
  • Perform transfer learning to improve model performance.
  • Optimize an existing model for a particular application.

Examples

Example 1: Adapting a Vision Model for Image Classification

User request: "Fine-tune a ResNet50 model to classify images of different types of flowers."

The skill will:

  1. Download the ResNet50 model and load a flower image dataset.
  2. Generate code to fine-tune the model on the flower dataset, including data augmentation and optimization techniques.
Show full SKILL.md (215 more words)Show less
Example 2: Adapting a Language Model for Sentiment Analysis

User request: "Adapt a BERT model to perform sentiment analysis on customer reviews."

The skill will:

  1. Download the BERT model and load a dataset of customer reviews with sentiment labels.
  2. Generate code to fine-tune the model on the review dataset, including tokenization, padding, and attention mechanisms.

Best Practices

  • Data Preprocessing: Ensure data is properly preprocessed and formatted to match the input requirements of the pre-trained model.
  • Hyperparameter Tuning: Experiment with different hyperparameters (e.g., learning rate, batch size) to optimize model performance.
  • Regularization: Apply regularization techniques (e.g., dropout, weight decay) to prevent overfitting.

Integration

This skill can be integrated with other plugins for data loading, model evaluation, and deployment. For example, it can work with a data loading plugin to fetch datasets and a model deployment plugin to deploy the adapted model to a serving infrastructure.

Prerequisites

  • Appropriate file access permissions
  • Required dependencies installed

Instructions

  1. Invoke this skill when the trigger conditions are met
  2. Provide necessary context and parameters
  3. Review the generated output
  4. Apply modifications as needed

Output

The skill produces structured output relevant to the task.

Error Handling

  • Invalid input: Prompts for correction
  • Missing dependencies: Lists required components
  • Permission errors: Suggests remediation steps

Resources

  • Project documentation
  • Related skills and commands

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references, assets) in skills/.curated/adapting-transfer-learning-models of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • assets/README.md
  • assets/data_preprocessing_example.py
  • assets/example_config.json
  • assets/model-architecture-brief.md
  • references/README.md
  • scripts/README.md

Open the folder on GitHubat commit d57fcd5

Compare with similar skills

Adapting Transfer Learning Models next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Adapting Transfer Learning Models compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Adapting Transfer Learning Models this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1.1kAutomated safety check: PassMIT
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Vision Sftwshobson/agents40k—~2kAutomated safety check: PassMIT
Transformersynulihao/AgentSkillOS618—~2.9kAutomated safety check: PassNone
AI ML Skillswentorai/research-plugins2981 repos~993Automated safety check: PassMIT
ML Cv Specialistalirezarezvani/claude-cto-team117—~3.1kAutomated safety check: PassMIT

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Categories

Questions about Adapting Transfer Learning Models

What does Adapting Transfer Learning Models do?

Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. Adapting Transfer Learning Models is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques.

When should I use Adapting Transfer Learning Models?

Adapting Transfer Learning Models fits situations like: requests assistance with fine-tuning a model; adapting a pre-trained model to a new dataset; appropriate context detected; with relevant phrases based on skill purpose.

How do I install Adapting Transfer Learning Models in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill adapting-transfer-learning-models -a claude-code`. Or copy the skill folder (skills/.curated/adapting-transfer-learning-models in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/adapting-transfer-learning-models in your project. Claude Code loads it when a task matches its description.

How do I install Adapting Transfer Learning Models in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill adapting-transfer-learning-models -a codex`. Or copy the skill folder (skills/.curated/adapting-transfer-learning-models in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/adapting-transfer-learning-models in your project. Codex loads it when a task matches its description.

Can I use Adapting Transfer Learning Models in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill adapting-transfer-learning-models -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/adapting-transfer-learning-models, .gemini/skills/adapting-transfer-learning-models, .github/skills/adapting-transfer-learning-models and .opencode/skills/adapting-transfer-learning-models in your project.

What does Adapting Transfer Learning Models need to run?

Going by SKILL.md and its folder, Adapting Transfer Learning Models needs Python for the scripts in its folder. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Adapting Transfer Learning Models access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Adapting Transfer Learning Models safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Adapting Transfer Learning Models use?

Adapting Transfer Learning Models is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Adapting Transfer Learning Models use?

About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 17 tokens, read only when the agent opens those files.

What are the alternatives to Adapting Transfer Learning Models?

Skills that share tags, products or a category with Adapting Transfer Learning Models: RuView Model Training (ruvnet/RuView, 97k stars), Vision Sft (wshobson/agents, 40k stars), Transformers (ynulihao/AgentSkillOS, 618 stars) and AI ML Skills (wentorai/research-plugins, 298 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Adapting Transfer Learning Models?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,831 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 11, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.