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Agent skill

Building Recommendation Systems

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

Execute this skill empowers AI assistant to construct recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches.

MITAuto-check passedSales & Support

Install Building Recommendation Systems

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill building-recommendation-systems -a claude-code

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

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace building-recommendation-systems --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/building-recommendation-systems .claude/skills/building-recommendation-systems && 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
building-recommendation-systems
GitHub stars
2.8k
Token cost
~978 tokens
SKILL.md length
412 words
Files
5 (incl. scripts, references, assets)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Execute this skill empowers AI assistant to construct recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches.

  • Works in 3 steps: Analyzing Requirements: Claude… → Generating Code: Claude generates Python… → Implementing Best Practices: The code…
  • Appropriate context detected
  • SKILL.md covers Overview, How It Works, When to Use This Skill and Examples, plus 7 more sections
  • With relevant phrases based on skill purpose

What it does

Building Recommendation Systems is an agent skill from jeremylongshore/tons-of-skills-marketplace. Execute this skill empowers AI assistant to construct recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches. it analyzes user preferences, item features, and interaction data to generate personalized recommendations... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

Its SKILL.md is about 980 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts, reference files and assets (for example `assets/README.md`, `assets/configuration_template.yaml` and `references/README.md`). Compatibility notes: Designed for Claude Code

It sits in Sales & Support. 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

  • Appropriate context detected
  • With relevant phrases based on skill purpose

Example prompts

  • “/building-recommendation-systems”

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

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

  1. Analyzing Requirements: Claude identifies the type of recommendation needed (collaborative, content-based, hybrid), data availability, and…
  2. Generating Code: Claude generates Python code using relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch) to build the…
  3. Implementing Best Practices: The code incorporates best practices for recommendation system development, such as handling cold starts…

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. 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/, 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

Building Recommendation Systems loads about 978 tokens when it runs, and up to ~994 if it reads all its reference files. Until then it costs about 97 tokens; SKILL.md has 412 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~97
When it runs · the whole SKILL.md, loaded when a task matches
~978
With references · SKILL.md plus every file in references/, read only if the agent opens them
~994

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 cfae287, republished under its MIT licence (© jeremylongshore). 412 words, ~978 tokens.

Download SKILL.mdSave it as .claude/skills/building-recommendation-systems/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
building-recommendation-systems
description
Execute this skill empowers AI assistant to construct recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches. it analyzes user preferences, item features, and interaction data to generate personalized recommendations... 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
1.21.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
ai, recommendation-systems

Recommendation Engine

Build recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches tailored to specific datasets and use cases.

Overview

design and implement recommendation systems tailored to specific datasets and use cases. It automates the process of selecting appropriate algorithms, preprocessing data, training models, and evaluating performance, ultimately providing users with a functional recommendation engine.

How It Works

  1. Analyzing Requirements: Claude identifies the type of recommendation needed (collaborative, content-based, hybrid), data availability, and performance goals.
  2. Generating Code: Claude generates Python code using relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch) to build the recommendation model. This includes data loading, preprocessing, model training, and evaluation.
  3. Implementing Best Practices: The code incorporates best practices for recommendation system development, such as handling cold starts, addressing scalability, and mitigating bias.

When to Use This Skill

This skill activates when you need to:

  • Build a personalized movie recommendation system.
  • Create a product recommendation engine for an e-commerce platform.
  • Implement a content recommendation system for a news website.

Examples

Example 1: Personalized Movie Recommendations

User request: "Build a movie recommendation system using collaborative filtering."

The skill will:

  1. Generate code to load and preprocess movie rating data.
  2. Implement a collaborative filtering algorithm (e.g., matrix factorization) to predict user preferences.
Example 2: E-commerce Product Recommendations

User request: "Create a product recommendation engine for an online store, using content-based filtering."

The skill will:

  1. Generate code to extract features from product descriptions and user purchase history.
  2. Implement a content-based filtering algorithm to recommend similar products.
Show full SKILL.md (161 more words)Show less

Best Practices

  • Data Preprocessing: Ensure data is properly cleaned and formatted before training the recommendation model.
  • Model Evaluation: Use appropriate metrics (e.g., precision, recall, NDCG) to evaluate the performance of the recommendation system.
  • Scalability: Design the recommendation system to handle large datasets and user bases efficiently.

Integration

This skill can be integrated with other Claude Code plugins to access data sources, deploy models, and monitor performance. For example, it can use data analysis plugins to extract features from raw data and deployment plugins to deploy the recommendation system to a production environment.

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 4 other files (scripts, references, assets) in skills/.curated/building-recommendation-systems of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • assets/README.md
  • assets/configuration_template.yaml
  • references/README.md
  • scripts/README.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

Building Recommendation Systems 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.

Building Recommendation Systems compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Building Recommendation Systems this skilljeremylongshore/tons-of-skills-marketplace2.8k—~978Automated safety check: PassMIT
Cold Outbound Optimizerericosiu/ai-marketing-skills3.6k1 repos~1.7kAutomated safety check: PassMIT
Doc Coauthoringaws-samples/sample-strands-agent-with-agentcore19540 repos~3.2kAutomated safety check: PassMIT
Amazon Buy Box Monitorbrowser-act/skills6.1k1 repos~1.6kAutomated safety check: PassMIT
Review Analysisliangdabiao/amazon-sorftime-research-MCP-skill9591 repos~2.5kAutomated safety check: PassNone
Deskcomm Extensaomelgarafael/DeskcommCRM4.5k—~2.7kAutomated safety check: PassMIT

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Categories

Questions about Building Recommendation Systems

What does Building Recommendation Systems do?

Execute this skill empowers AI assistant to construct recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches. Building Recommendation Systems is an agent skill from jeremylongshore/tons-of-skills-marketplace. Execute this skill empowers AI assistant to construct recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches.

When should I use Building Recommendation Systems?

Building Recommendation Systems fits situations like: appropriate context detected; with relevant phrases based on skill purpose.

How do I install Building Recommendation Systems in Claude Code?

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

How do I install Building Recommendation Systems in Codex?

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

Can I use Building Recommendation Systems 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 building-recommendation-systems -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/building-recommendation-systems, .gemini/skills/building-recommendation-systems, .github/skills/building-recommendation-systems and .opencode/skills/building-recommendation-systems in your project.

What does Building Recommendation Systems need to run?

SKILL.md names no scripts, command-line tools or credentials: Building Recommendation Systems is instructions for the agent only. 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 Building Recommendation Systems 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 Building Recommendation Systems 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 Building Recommendation Systems use?

Building Recommendation Systems 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 Building Recommendation Systems use?

About 978 tokens (SKILL.md is roughly 3.9k 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 16 tokens, read only when the agent opens those files.

What are the alternatives to Building Recommendation Systems?

Skills that share tags, products or a category with Building Recommendation Systems: Cold Outbound Optimizer (ericosiu/ai-marketing-skills, 3.6k stars), Doc Coauthoring (aws-samples/sample-strands-agent-with-agentcore, 195 stars), Amazon Buy Box Monitor (browser-act/skills, 6.1k stars) and Review Analysis (liangdabiao/amazon-sorftime-research-MCP-skill, 959 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Building Recommendation Systems?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 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.