Agent Builder
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
Use this sub-skill for MLAlgorithms clustering, Gaussian mixtures, PCA, t-SNE, RBM feature learning, metrics/distances, and packaged demo dataset loaders.
Install Unsupervised And Reduction
$ npx skills add VectorSpaceLab/AREX-Skill --skill unsupervised-and-reduction -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill unsupervised-and-reduction --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/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction .claude/skills/unsupervised-and-reduction && 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 "unsupervised-and-reduction" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction into .claude/skills/unsupervised-and-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unsupervised-and-reduction", 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/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reductionType 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 VectorSpaceLab/AREX-Skill --skill unsupervised-and-reduction -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill unsupervised-and-reduction --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction .agents/skills/unsupervised-and-reduction && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "unsupervised-and-reduction" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction into .agents/skills/unsupervised-and-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unsupervised-and-reduction", 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 VectorSpaceLab/AREX-Skill --skill unsupervised-and-reduction -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill unsupervised-and-reduction --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction .cursor/skills/unsupervised-and-reduction && 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 "unsupervised-and-reduction" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction into .cursor/skills/unsupervised-and-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unsupervised-and-reduction", 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/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction--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 VectorSpaceLab/AREX-Skill --skill unsupervised-and-reduction -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill unsupervised-and-reduction --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction .gemini/skills/unsupervised-and-reduction && 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 "unsupervised-and-reduction" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction into .gemini/skills/unsupervised-and-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unsupervised-and-reduction", 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 VectorSpaceLab/AREX-Skill unsupervised-and-reductionInstalls 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 VectorSpaceLab/AREX-Skill --skill unsupervised-and-reduction -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction .github/skills/unsupervised-and-reduction && 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 "unsupervised-and-reduction" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction into .github/skills/unsupervised-and-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unsupervised-and-reduction", 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 VectorSpaceLab/AREX-Skill --skill unsupervised-and-reduction -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill unsupervised-and-reduction --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction .opencode/skills/unsupervised-and-reduction && 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 "unsupervised-and-reduction" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction into .opencode/skills/unsupervised-and-reduction/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "unsupervised-and-reduction", 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
unsupervised-and-reduction- GitHub stars
- 330
- Token cost
- ~1.2k tokens
- SKILL.md length
- 367 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 159
- Repo updated
- First seen
- Licence
- MIT
At a glance
Use this sub-skill for MLAlgorithms clustering, Gaussian mixtures, PCA, t-SNE, RBM feature learning, metrics/distances, and packaged demo dataset loaders.
- Works in 4 steps: Confirm the public imports you need → Choose the workflow → Avoid plotting in headless or automated… → …
- AI & LLM Engineering work in your project
- SKILL.md covers Route elsewhere, Start here, Common workflows and Bundled references and helpers
- Runs Python scripts from its folder
What it does
Unsupervised And Reduction is an agent skill from VectorSpaceLab/AREX-Skill. Use this sub-skill for MLAlgorithms clustering, Gaussian mixtures, PCA, t-SNE, RBM feature learning, metrics/distances, and packaged demo dataset loaders.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api-reference.md`, `references/troubleshooting.md` and `references/workflows.md`).
It sits in AI & LLM Engineering. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is MIT.
When your agent uses it
- AI & LLM Engineering work in your project
Example prompts
- “/unsupervised-and-reduction”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Confirm the public imports you need
- Choose the workflow
- Avoid plotting in headless or automated runs; inspect returned labels, component shapes, likelihoods, embeddings, or errors instead.
- Run scripts/run_unsupervised_smoke.py --workflow all for a safe no-display check.
What it can do on your machine
Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
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.
Context cost
Unsupervised And Reduction loads about 1.2k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 45 tokens; SKILL.md has 367 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 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 VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its MIT licence (© VectorSpaceLab). 367 words, ~1,207 tokens.
Download SKILL.mdSave it as
.claude/skills/unsupervised-and-reduction/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- unsupervised-and-reduction
- description
- Use this sub-skill for MLAlgorithms clustering, Gaussian mixtures, PCA, t-SNE, RBM feature learning, metrics/distances, and packaged demo dataset loaders.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- MIT
Unsupervised and Reduction
Use this sub-skill when a task asks for MLAlgorithms' unsupervised estimators, dimensionality reduction, small visualization-oriented workflows, RBM feature learning, or demo data loaders. These APIs are CPU-only educational implementations; keep workloads small and validate output shapes or assignments before scaling.
Route elsewhere
- Supervised regression/classification, SVM kernels, random forests, gradient boosting, KNN supervised modes, and factorization machines belong in
../classical-estimators/SKILL.md. - The custom neural-network container, layer stack, optimizers, CNN/RNN/LSTM recipes, and DQN belong in
../neural-network-building-blocks/SKILL.md. - Cross-cutting installation, provenance, and route selection are in the root skill.
Start here
Confirm the public imports you need:
python from mla.kmeans import KMeans from mla.gaussian_mixture import GaussianMixture from mla.pca import PCA from mla.tsne import TSNE from mla.rbm import RBM from mla.datasets import load_mnist, load_nietzscheChoose the workflow:
- Cluster assignments:
KMeans(K=..., init="random"|"++"). - Soft density-style clusters:
GaussianMixture(K=..., init="random"|"kmeans"). - Linear reduction for downstream models:
PCA(n_components, solver="svd"|"eigen"). - 2D nonlinear visualization:
TSNE(n_components=2, perplexity=..., max_iter=...). - Binary/continuous unsupervised features:
RBM(n_hidden=..., max_epochs=...).
- Cluster assignments:
Avoid plotting in headless or automated runs; inspect returned labels, component shapes, likelihoods, embeddings, or errors instead.
Run
scripts/run_unsupervised_smoke.py --workflow allfor a safe no-display check.
Common workflows
KMeans clustering
from sklearn.datasets import make_blobs
from mla.kmeans import KMeans
X, _ = make_blobs(n_samples=120, centers=3, n_features=2, random_state=42)
model = KMeans(K=3, max_iters=50, init="++")
model.fit(X)
labels = model.predict() # labels for the fitted training data
centroids = model.centroidsCall fit(X) before predict(). KMeans.predict() without an argument returns assignments for the stored training data because the class overrides _predict around self.X.
Gaussian mixture model
from mla.gaussian_mixture import GaussianMixture
model = GaussianMixture(K=3, init="kmeans", max_iters=50, tolerance=1e-3)
model.fit(X)
assignments = model.predict(X)GMM uses full covariance matrices and SciPy's multivariate_normal.pdf. Tiny or duplicate clusters can produce singular-covariance failures; increase samples, reduce K, or add slight jitter.
Show full SKILL.md (159 more words)Show less
PCA reduction
from mla.pca import PCA
pca = PCA(n_components=2, solver="svd")
pca.fit(X_train)
X_train_2d = pca.transform(X_train)
X_test_2d = pca.transform(X_test)Fit PCA on training data only, then transform held-out data with the learned mean and components.
t-SNE embedding
from mla.tsne import TSNE
embedding = TSNE(n_components=2, perplexity=10.0, max_iter=250, learning_rate=200).fit_transform(X)Use t-SNE for visualization-style embeddings rather than predictive features. Keep sample counts small; this implementation uses dense pairwise distances and a Python loop.
RBM feature learning
import numpy as np
from mla.rbm import RBM
X = np.random.RandomState(0).uniform(0, 1, (100, 8))
rbm = RBM(n_hidden=4, learning_rate=0.05, batch_size=10, max_epochs=5)
rbm.fit(X)
features = rbm.predict(X)RBM stores per-epoch reconstruction errors in rbm.errors.
Demo dataset loaders
load_mnist() returns (X_train, X_test, y_train, y_test) with image arrays shaped for the ConvNet example. load_nietzsche() creates one-hot text sequences for recurrent examples. Both read package data shipped with the distribution; do not copy those datasets into generated skills. With modern NumPy, load_nietzsche() may need numpy<1.24 or a source patch because it uses deprecated np.bool.
Bundled references and helpers
- Read
references/api-reference.mdfor import paths, signatures, state attributes, and outputs. - Read
references/workflows.mdfor cluster/reduction/RBM recipes and validation ideas. - Read
references/troubleshooting.mdfor singular covariance, plotting, data-loader, t-SNE runtime, and NumPy compatibility issues. - Run
scripts/run_unsupervised_smoke.py --workflow allto exercise small no-display checks against an installedmlapackage.
© VectorSpaceLab, 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) in skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/api-reference.md
- references/troubleshooting.md
- references/workflows.md
- scripts/run_unsupervised_smoke.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Unsupervised And Reduction 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 |
|---|---|---|---|---|---|---|
| Unsupervised And Reduction this skillVectorSpaceLab/AREX-Skill | 330 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 5 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3.3k | Automated safety check: Pass | MIT | |
| 1passwordtrpc-group/trpc-agent-go | 1.9k | 14 repos | ~656 | Automated safety check: Pass | Apache-2.0 |
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Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage.
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Related
Categories
Questions about Unsupervised And Reduction
What does Unsupervised And Reduction do?
Use this sub-skill for MLAlgorithms clustering, Gaussian mixtures, PCA, t-SNE, RBM feature learning, metrics/distances, and packaged demo dataset loaders. Unsupervised And Reduction is an agent skill from VectorSpaceLab/AREX-Skill. Use this sub-skill for MLAlgorithms clustering, Gaussian mixtures, PCA, t-SNE, RBM feature learning, metrics/distances, and packaged demo dataset loaders.
When should I use Unsupervised And Reduction?
Unsupervised And Reduction fits situations like: AI & LLM Engineering work in your project.
How do I install Unsupervised And Reduction in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill unsupervised-and-reduction -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction in VectorSpaceLab/AREX-Skill) into .claude/skills/unsupervised-and-reduction in your project. Claude Code loads it when a task matches its description.
How do I install Unsupervised And Reduction in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill unsupervised-and-reduction -a codex`. Or copy the skill folder (skills/repositories/repo-skills/ml-algorithms/sub-skills/unsupervised-and-reduction in VectorSpaceLab/AREX-Skill) into .agents/skills/unsupervised-and-reduction in your project. Codex loads it when a task matches its description.
Can I use Unsupervised And Reduction 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 VectorSpaceLab/AREX-Skill --skill unsupervised-and-reduction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/unsupervised-and-reduction, .gemini/skills/unsupervised-and-reduction, .github/skills/unsupervised-and-reduction and .opencode/skills/unsupervised-and-reduction in your project.
What does Unsupervised And Reduction need to run?
Going by SKILL.md and its folder, Unsupervised And Reduction needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Unsupervised And Reduction 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 Unsupervised And Reduction 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 Unsupervised And Reduction use?
Unsupervised And Reduction 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 Unsupervised And Reduction use?
About 1.2k tokens (SKILL.md is roughly 4.8k 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 2.8k tokens, read only when the agent opens those files.
What are the alternatives to Unsupervised And Reduction?
Skills that share tags, products or a category with Unsupervised And Reduction: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Unsupervised And Reduction?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 330 GitHub stars. The repository holds 159 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.