Matplotlib
zLanqing/codex-claude-academic-skills
Low-level plotting library for full customization. An agent skill from zLanqing/codex-claude-academic-skills.
智能推荐系统分析工具，提供多种推荐算法实现、评估框架和可视化分析。使用时需要用户行为数据、商品信息或评分数据，支持协同过滤、矩阵分解等推荐算法，生成个性化推荐结果和评估报告。
Install Recommender System
$ npx skills add liangdabiao/claude-data-analysis-ultra-main --skill recommender-system -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install liangdabiao/claude-data-analysis-ultra-main recommender-system --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/liangdabiao/claude-data-analysis-ultra-main.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/recommender-system .claude/skills/recommender-system && rm -rf skills-srcUse ~/.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/
Install the "recommender-system" agent skill from https://github.com/liangdabiao/claude-data-analysis-ultra-main/tree/main/.claude/skills/recommender-system into .claude/skills/recommender-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recommender-system", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/liangdabiao/claude-data-analysis-ultra-main/tree/main/.claude/skills/recommender-systemType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add liangdabiao/claude-data-analysis-ultra-main --skill recommender-system -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install liangdabiao/claude-data-analysis-ultra-main recommender-system --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/liangdabiao/claude-data-analysis-ultra-main.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/recommender-system .agents/skills/recommender-system && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "recommender-system" agent skill from https://github.com/liangdabiao/claude-data-analysis-ultra-main/tree/main/.claude/skills/recommender-system into .agents/skills/recommender-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recommender-system", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add liangdabiao/claude-data-analysis-ultra-main --skill recommender-system -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install liangdabiao/claude-data-analysis-ultra-main recommender-system --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/liangdabiao/claude-data-analysis-ultra-main.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/recommender-system .cursor/skills/recommender-system && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "recommender-system" agent skill from https://github.com/liangdabiao/claude-data-analysis-ultra-main/tree/main/.claude/skills/recommender-system into .cursor/skills/recommender-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recommender-system", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/liangdabiao/claude-data-analysis-ultra-main.git --path .claude/skills/recommender-system--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add liangdabiao/claude-data-analysis-ultra-main --skill recommender-system -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install liangdabiao/claude-data-analysis-ultra-main recommender-system --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/liangdabiao/claude-data-analysis-ultra-main.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/recommender-system .gemini/skills/recommender-system && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "recommender-system" agent skill from https://github.com/liangdabiao/claude-data-analysis-ultra-main/tree/main/.claude/skills/recommender-system into .gemini/skills/recommender-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recommender-system", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install liangdabiao/claude-data-analysis-ultra-main recommender-systemInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add liangdabiao/claude-data-analysis-ultra-main --skill recommender-system -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/liangdabiao/claude-data-analysis-ultra-main.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/recommender-system .github/skills/recommender-system && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "recommender-system" agent skill from https://github.com/liangdabiao/claude-data-analysis-ultra-main/tree/main/.claude/skills/recommender-system into .github/skills/recommender-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recommender-system", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add liangdabiao/claude-data-analysis-ultra-main --skill recommender-system -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install liangdabiao/claude-data-analysis-ultra-main recommender-system --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/liangdabiao/claude-data-analysis-ultra-main.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/recommender-system .opencode/skills/recommender-system && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "recommender-system" agent skill from https://github.com/liangdabiao/claude-data-analysis-ultra-main/tree/main/.claude/skills/recommender-system into .opencode/skills/recommender-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recommender-system", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
recommender-system- GitHub stars
- 290
- Token cost
- ~1.1k tokens
- SKILL.md length
- 122 words
- Files
- 16 (incl. scripts)
- Skills in repo
- 19
- Repo updated
- First seen
- Licence
- None found
At a glance
智能推荐系统分析工具，提供多种推荐算法实现、评估框架和可视化分析。使用时需要用户行为数据、商品信息或评分数据，支持协同过滤、矩阵分解等推荐算法，生成个性化推荐结果和评估报告。
- Works in 4 steps: 推荐算法实现 (scripts/recommendation_engine.py) → 推荐系统评估器 (scripts/recommender_evaluator.py) → 数据分析器 (scripts/data_analyzer.py) → …
- Data & Analytics work in your project
- SKILL.md covers 🎯 技能概述, ✨ 核心特性, 🚀 主要功能模块 and 📋 支持的数据格式, plus 5 more sections
- Runs Python scripts from its folder
What it does
Recommender System is an agent skill from liangdabiao/claude-data-analysis-ultra-main. 智能推荐系统分析工具，提供多种推荐算法实现、评估框架和可视化分析。使用时需要用户行为数据、商品信息或评分数据，支持协同过滤、矩阵分解等推荐算法，生成个性化推荐结果和评估报告。
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 19 other files, including scripts (for example `README.md`, `examples/advanced_recommendation_example.py` and `examples/basic_recommendation_example.py`).
It sits in Data & Analytics. The repository describes itself as: 让小白都可以一键进行数据分析，搞互联网的，搞电商的，搞各种各样的，那么其实就会用到 互联网的数据分析， 例如互联网会关心 拉新，留存，促活，推荐，转化，A/B test, 用户分析 等等很多有用的数据分析。
When your agent uses it
- Data & Analytics work in your project
Example prompts
- “/recommender-system”
Requirements
- Python 3
- Pre-approved tools (allowed-tools): Read, Write, Edit, Glob, Grep, Bash, WebSearch, WebFetch
Workflow steps
4 steps, taken from the step headings in SKILL.md.
- 推荐算法实现 (scripts/recommendation_engine.py)
- 推荐系统评估器 (scripts/recommender_evaluator.py)
- 数据分析器 (scripts/data_analyzer.py)
- 可视化展示器 (scripts/recommender_visualizer.py)
What it can do on your machine
Read from SKILL.md and the folder at commit 6b52856. 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:
ReadWriteEditGlobGrepBashWebSearchWebFetch
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 5 files 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.
Context cost
Recommender System loads about 1.1k tokens when it runs. Until then it costs about 27 tokens; SKILL.md has 122 words of instructions outside code blocks.
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: notes
The automated check noted patterns worth knowing about, such as sudo or a known installer.
- NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
allowed-tools: Read, Write, Edit, Glob, Grep, Bash, WebSearch, WebFetch
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
Without a licence we can't republish the file, so here is its outline and opening line. It has 122 words (~1,138 tokens).
- name
- recommender-system
- allowed-tools
- Read, Write, Edit, Glob, Grep, Bash, WebSearch, WebFetch
Files
SKILL.md and 15 other files (scripts) in .claude/skills/recommender-system of liangdabiao/claude-data-analysis-ultra-main.
- SKILL.md
- README.md
- examples/advanced_recommendation_example.py
- examples/basic_recommendation_example.py
- examples/sample_data/sample_item_info.csv
- examples/sample_data/sample_user_behavior.csv
- examples/simple_output/model_info.csv
- examples/simple_output/recommendations_results.csv
- examples/simple_recommendation_example.py
- quick_test.py
- scripts/__init__.py
- scripts/data_analyzer.py
- scripts/recommendation_engine.py
- scripts/recommender_evaluator.py
- scripts/recommender_visualizer.py
- test_skill.py
Open the folder on GitHubat commit 6b52856
Compare with similar skills
Recommender System 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Recommender System this skillliangdabiao/claude-data-analysis-ultra-main | 290 | — | ~1.1k | Automated safety check: Notes | None | |
| MatplotlibzLanqing/codex-claude-academic-skills | 4.7k | 17 repos | ~2.9k | Automated safety check: Pass | MIT | |
| Exploratory Data Analysisspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.7k | 16 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Chart Visualizationbytedance/deer-flow | 84k | 2 repos | ~840 | Automated safety check: Pass | MIT | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 |
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Exploratory Data Analysis
spacering-net/codeg
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.
Scikit Learn
zLanqing/codex-claude-academic-skills
Machine learning in Python with scikit-learn. An agent skill from zLanqing/codex-claude-academic-skills.
Chart Visualization
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Picks a suitable chart type from 26 options for your data, maps the data to that chart's parameters and generates a chart image through a JavaScript script.
TimesFM Forecasting
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Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
- Official
Sandbox Bench
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Benchmark React or Next.js changes on Vercel Sandbox VMs with paired A/B statistics: react PR/commit vs base, or Next.js PR/commit vs base, measured end-to-end through the bench/render-pipeline app…
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Categories
Questions about Recommender System
What does Recommender System do?
智能推荐系统分析工具，提供多种推荐算法实现、评估框架和可视化分析。使用时需要用户行为数据、商品信息或评分数据，支持协同过滤、矩阵分解等推荐算法，生成个性化推荐结果和评估报告。. Recommender System is an agent skill from liangdabiao/claude-data-analysis-ultra-main.
When should I use Recommender System?
Recommender System fits situations like: data & Analytics work in your project.
How do I install Recommender System in Claude Code?
Run `npx skills add liangdabiao/claude-data-analysis-ultra-main --skill recommender-system -a claude-code`. Or copy the skill folder (.claude/skills/recommender-system in liangdabiao/claude-data-analysis-ultra-main) into .claude/skills/recommender-system in your project. Claude Code loads it when a task matches its description.
How do I install Recommender System in Codex?
Run `npx skills add liangdabiao/claude-data-analysis-ultra-main --skill recommender-system -a codex`. Or copy the skill folder (.claude/skills/recommender-system in liangdabiao/claude-data-analysis-ultra-main) into .agents/skills/recommender-system in your project. Codex loads it when a task matches its description.
Can I use Recommender System 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 liangdabiao/claude-data-analysis-ultra-main --skill recommender-system -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/recommender-system, .gemini/skills/recommender-system, .github/skills/recommender-system and .opencode/skills/recommender-system in your project.
What does Recommender System need to run?
Going by SKILL.md and its folder, Recommender System needs Python for the scripts in its folder. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Glob, Grep, Bash, WebSearch, WebFetch.
Does Recommender System 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 Recommender System safe to install?
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. 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 Recommender System use?
No licence was found for Recommender System or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does Recommender System use?
About 1.1k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Recommender System?
Skills that share tags, products or a category with Recommender System: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars) and Chart Visualization (bytedance/deer-flow, 84k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Recommender System?
liangdabiao (a GitHub user) maintains it in liangdabiao/claude-data-analysis-ultra-main, which has 290 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on May 10, 2026.
Source: liangdabiao/claude-data-analysis-ultra-main on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.