Senior Data Scientist
Raidriar7170/hermes-skilleval
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.
A skill your agent uses for MLAlgorithms (mla) educational machine-learning implementations: classical estimators, clustering/reduction, metrics, NeuralNet building blocks, and DQN examples.
Install ML Algorithms
$ npx skills add VectorSpaceLab/AREX-Skill --skill ml-algorithms -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill ml-algorithms --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 .claude/skills/ml-algorithms && 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 "ml-algorithms" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms into .claude/skills/ml-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-algorithms", 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-algorithmsType 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 ml-algorithms -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill ml-algorithms --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 .agents/skills/ml-algorithms && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ml-algorithms" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms into .agents/skills/ml-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-algorithms", 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 ml-algorithms -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill ml-algorithms --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 .cursor/skills/ml-algorithms && 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 "ml-algorithms" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms into .cursor/skills/ml-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-algorithms", 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--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 ml-algorithms -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill ml-algorithms --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 .gemini/skills/ml-algorithms && 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 "ml-algorithms" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms into .gemini/skills/ml-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-algorithms", 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 ml-algorithmsInstalls 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 ml-algorithms -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 .github/skills/ml-algorithms && 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 "ml-algorithms" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms into .github/skills/ml-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-algorithms", 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 ml-algorithms -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 ml-algorithms --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 .opencode/skills/ml-algorithms && 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 "ml-algorithms" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/ml-algorithms into .opencode/skills/ml-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-algorithms", 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
ml-algorithms- GitHub stars
- 328
- Token cost
- ~1.2k tokens
- SKILL.md length
- 388 words
- Files
- 8 (incl. scripts, references)
- Skills in repo
- 159
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses for MLAlgorithms (mla) educational machine-learning implementations: classical estimators, clustering/reduction, metrics, NeuralNet building blocks, and DQN examples.
- MLAlgorithms (mla) educational machine-learning implementations: classical estimators
- SKILL.md covers First checks, Route by user goal, Common decisions and Root references, plus 2 more sections
- Runs Python scripts from its folder; calls python
- Clustering/reduction
What it does
ML Algorithms is an agent skill from VectorSpaceLab/AREX-Skill. Use this skill for MLAlgorithms (mla) educational machine-learning implementations: classical estimators, clustering/reduction, metrics, NeuralNet building blocks, and DQN examples.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/api-reference.md`, `references/repo-provenance.md` and `references/repo-routing-metadata.json`).
It sits in Data & Analytics, covering Machine learning. It works with Python and NumPy. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is MIT.
When your agent uses it
- MLAlgorithms (mla) educational machine-learning implementations: classical estimators
- Clustering/reduction
- NeuralNet building blocks
Example prompts
- “/ml-algorithms”
Requirements
- Python 3
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.
Shell commands in SKILL.md call:
python
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
ML Algorithms loads about 1.2k tokens when it runs, and up to ~4.7k if it reads all its reference files. Until then it costs about 49 tokens; SKILL.md has 388 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). 388 words, ~1,179 tokens.
Download SKILL.mdSave it as
.claude/skills/ml-algorithms/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- ml-algorithms
- description
- Use this skill for MLAlgorithms (`mla`) educational machine-learning implementations: classical estimators, clustering/reduction, metrics, NeuralNet building blocks, and DQN examples.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- MIT
MLAlgorithms Repo Skill
Use this skill when a task asks about the rushter/MLAlgorithms package, the mla Python distribution, or its minimal NumPy/SciPy/autograd implementations of common machine-learning algorithms. The package is educational rather than production-optimized: favor small examples, explicit NumPy arrays, deterministic seeds, and direct metric checks.
First checks
Install the package and its scientific Python dependencies in an isolated environment. The public distribution name is
mla.Confirm the package imports:
bash python - <<'PY' import mla from mla.linear_models import LinearRegression from mla.kmeans import KMeans from mla.neuralnet import NeuralNet print("mla import ok") PYRun
scripts/run_import_smoke.py --jsonfrom this skill directory to inspect package imports, dependency versions, important signatures, and compatibility warnings without training, plotting, downloading, or reading original examples.Read
references/repo-provenance.mdbefore deciding whether this skill is current for a checkout or should be refreshed.
Route by user goal
- Classical supervised estimators: use
sub-skills/classical-estimators/SKILL.mdfor linear/logistic regression, KNN, Naive Bayes, SVM kernels, random forests, gradient boosting, and experimental factorization machines. - Unsupervised and reduction workflows: use
sub-skills/unsupervised-and-reduction/SKILL.mdfor KMeans, GaussianMixture, PCA, t-SNE, RBM, demo dataset loaders, distances, and no-display clustering/reduction checks. - Neural network building blocks: use
sub-skills/neural-network-building-blocks/SKILL.mdforNeuralNet, layers, activations, initializers, constraints, regularizers, optimizers, CNN/RNN/LSTM recipes, and DQN wiring.
Show full SKILL.md (212 more words)Show less
Common decisions
- Package name: install/query distribution
mla; import packagemla. - No CLIs: the project exposes Python APIs and example modules, not console entry points. Use bundled skill scripts for safe checks.
- Backend: selected workflows are CPU-only. A visible GPU is not required for this package.
- Compatibility: the current dataset text loader uses deprecated
np.bool; prefer NumPy<1.24or patch the loader before usingload_nietzsche()with modern NumPy. The DQN loop expects legacy Gym reset/step signatures, so do not assume Gymnasium compatibility. - Shapes: most estimators expect 2D feature arrays. Neural layers use explicit 2D dense, 3D sequence, or 4D image tensors.
- Plots and long examples: repo examples include plotting, MNIST ConvNet, RNN text generation, and CartPole DQN training. Treat those as reference workflows unless the user explicitly authorizes longer runs or display side effects.
Root references
references/api-reference.md: package-wide imports, metrics, datasets, dependency facts, and public API index.references/workflows.md: how to select and combine the sub-skills for common tasks.references/troubleshooting.md: cross-cutting install/import, dependency, data-loader, plotting, and runtime problems.references/repo-provenance.md: source snapshot and refresh triggers.references/repo-routing-metadata.json: structured metadata for the managed repo-skills router.
Bundled helpers
scripts/run_import_smoke.py: safe package/dependency/signature check for the active Python environment.sub-skills/classical-estimators/scripts/run_classical_smoke.py: small supervised estimator checks.sub-skills/unsupervised-and-reduction/scripts/run_unsupervised_smoke.py: small clustering/reduction/RBM checks.sub-skills/neural-network-building-blocks/scripts/run_neural_smoke.py: small dense/RBM/DQN-wiring checks.
Minimal examples
Classical estimator:
from mla.linear_models import LogisticRegression
model = LogisticRegression(lr=0.01, max_iters=300)
model.fit(X_train, y_train)
proba = model.predict(X_test)
labels = (proba >= 0.5).astype(int)Unsupervised estimator:
from mla.kmeans import KMeans
model = KMeans(K=3, init="++", max_iters=50)
model.fit(X)
labels = model.predict()Neural model:
from mla.neuralnet import NeuralNet
from mla.neuralnet.layers import Dense, Activation
from mla.neuralnet.optimizers import Adam
model = NeuralNet([Dense(16), Activation("relu"), Dense(1)], Adam(), loss="mse", max_epochs=5)© 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 7 other files (scripts, references) in skills/repositories/repo-skills/ml-algorithms of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/api-reference.md
- references/repo-provenance.md
- references/repo-routing-metadata.json
- references/troubleshooting.md
- references/workflows.md
- scripts/run_import_smoke.py
- sub-skills
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
ML Algorithms 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 |
|---|---|---|---|---|---|---|
| ML Algorithms this skillVectorSpaceLab/AREX-Skill | 328 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Senior Data ScientistRaidriar7170/hermes-skilleval | 125 | 6 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Machine Learning Trading StrategyHKUDS/Vibe-Trading | 35k | — | ~3.2k | Automated safety check: Pass | MIT | |
| Senior Data Scientistalirezarezvani/claude-skills | 28k | 2 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Optimize For GPUK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Scikit Learn Machine Learningjaechang-hits/SciAgent-Skills | 370 | 1 repos | ~4k | Automated safety check: Pass | BSD-3-Clause |
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Related
Categories
Questions about ML Algorithms
What does ML Algorithms do?
A skill your agent uses for MLAlgorithms (mla) educational machine-learning implementations: classical estimators, clustering/reduction, metrics, NeuralNet building blocks, and DQN examples. ML Algorithms is an agent skill from VectorSpaceLab/AREX-Skill. Use this skill for MLAlgorithms (mla) educational machine-learning implementations: classical estimators, clustering/reduction, metrics, NeuralNet building blocks, and DQN examples.
When should I use ML Algorithms?
ML Algorithms fits situations like: MLAlgorithms (mla) educational machine-learning implementations: classical estimators; clustering/reduction; neuralNet building blocks.
How do I install ML Algorithms in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill ml-algorithms -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/ml-algorithms in VectorSpaceLab/AREX-Skill) into .claude/skills/ml-algorithms in your project. Claude Code loads it when a task matches its description.
How do I install ML Algorithms in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill ml-algorithms -a codex`. Or copy the skill folder (skills/repositories/repo-skills/ml-algorithms in VectorSpaceLab/AREX-Skill) into .agents/skills/ml-algorithms in your project. Codex loads it when a task matches its description.
Can I use ML Algorithms 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 ml-algorithms -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ml-algorithms, .gemini/skills/ml-algorithms, .github/skills/ml-algorithms and .opencode/skills/ml-algorithms in your project.
What does ML Algorithms need to run?
Going by SKILL.md and its folder, ML Algorithms needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does ML Algorithms 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 ML Algorithms 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 ML Algorithms use?
ML Algorithms 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 ML Algorithms use?
About 1.2k tokens (SKILL.md is roughly 4.7k 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 3.5k tokens, read only when the agent opens those files.
What are the alternatives to ML Algorithms?
Skills that share tags, products or a category with ML Algorithms: Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Machine Learning Trading Strategy (HKUDS/Vibe-Trading, 35k stars), Senior Data Scientist (alirezarezvani/claude-skills, 28k stars) and Optimize For GPU (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains ML Algorithms?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 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.