Sap Hana Cloud Data Intelligence
secondsky/sap-skills
Develops data processing pipelines, integrations, and machine learning scenarios in SAP Data Intelligence Cloud.
Agent skill for data-ml-model - invoke with $agent-data-ml-model
Install Agent Data ML Model
$ npx skills add ruvnet/ruflo --skill agent-data-ml-model -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ruvnet/ruflo agent-data-ml-model --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/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/agent-data-ml-model .claude/skills/agent-data-ml-model && 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 "agent-data-ml-model" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-data-ml-model into .claude/skills/agent-data-ml-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-data-ml-model", 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/ruvnet/ruflo/tree/main/.agents/skills/agent-data-ml-modelType 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 ruvnet/ruflo --skill agent-data-ml-model -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ruvnet/ruflo agent-data-ml-model --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/agent-data-ml-model .agents/skills/agent-data-ml-model && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "agent-data-ml-model" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-data-ml-model into .agents/skills/agent-data-ml-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-data-ml-model", 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 ruvnet/ruflo --skill agent-data-ml-model -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ruvnet/ruflo agent-data-ml-model --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/agent-data-ml-model .cursor/skills/agent-data-ml-model && 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 "agent-data-ml-model" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-data-ml-model into .cursor/skills/agent-data-ml-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-data-ml-model", 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/ruvnet/ruflo.git --path .agents/skills/agent-data-ml-model--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 ruvnet/ruflo --skill agent-data-ml-model -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ruvnet/ruflo agent-data-ml-model --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/agent-data-ml-model .gemini/skills/agent-data-ml-model && 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 "agent-data-ml-model" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-data-ml-model into .gemini/skills/agent-data-ml-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-data-ml-model", 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 ruvnet/ruflo agent-data-ml-modelInstalls 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 ruvnet/ruflo --skill agent-data-ml-model -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/agent-data-ml-model .github/skills/agent-data-ml-model && 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 "agent-data-ml-model" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-data-ml-model into .github/skills/agent-data-ml-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-data-ml-model", 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 ruvnet/ruflo --skill agent-data-ml-model -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ruvnet/ruflo agent-data-ml-model --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/agent-data-ml-model .opencode/skills/agent-data-ml-model && 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 "agent-data-ml-model" agent skill from https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-data-ml-model into .opencode/skills/agent-data-ml-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-data-ml-model", 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
agent-data-ml-model- GitHub stars
- 74k
- Used in
- 3 other repos
- Token cost
- ~1.3k tokens
- SKILL.md length
- 453 words
- Files
- 1
- Skills in repo
- 264
- Repo updated
- First seen
- Licence
- MIT
At a glance
Agent skill for data-ml-model - invoke with $agent-data-ml-model
- Works in 5 steps: Data preprocessing and feature engineering → Model selection and architecture design → Training and hyperparameter tuning → …
- Tasks that involve Machine learning
- SKILL.md covers Key responsibilities:, ML workflow:, Code patterns: and Best practices:
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Agent Data ML Model is an agent skill from ruvnet/ruflo. Agent skill for data-ml-model - invoke with $agent-data-ml-model
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Machine learning. It works with Jupyter. The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.
When your agent uses it
- Tasks that involve Machine learning
Example prompts
- “/agent-data-ml-model”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Data preprocessing and feature engineering
- Model selection and architecture design
- Training and hyperparameter tuning
- Model evaluation and validation
- Deployment preparation and monitoring
What it can do on your machine
Read from SKILL.md and the folder at commit de590e1. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are 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
Agent Data ML Model loads about 1.3k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 453 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from ruvnet/ruflo at commit de590e1, republished under its MIT licence (© ruvnet). 453 words, ~1,319 tokens.
Download SKILL.mdSave it as
.claude/skills/agent-data-ml-model/SKILL.md (or your agent's skills folder).- name
- agent-data-ml-model
- description
- Agent skill for data-ml-model - invoke with $agent-data-ml-model
name: "ml-developer" description: "Specialized agent for machine learning model development, training, and deployment" color: "purple" type: "data" version: "1.0.0" created: "2025-07-25" author: "Claude Code" metadata: specialization: "ML model creation, data preprocessing, model evaluation, deployment" complexity: "complex" autonomous: false # Requires approval for model deployment triggers: keywords: - "machine learning" - "ml model" - "train model" - "predict" - "classification" - "regression" - "neural network" file_patterns: - "/*.ipynb" - "$model.py" - "$train.py" - "/.pkl" - "**/.h5" task_patterns: - "create * model" - "train * classifier" - "build ml pipeline" domains: - "data" - "ml" - "ai" capabilities: allowed_tools: - Read - Write - Edit - MultiEdit - Bash - NotebookRead - NotebookEdit restricted_tools: - Task # Focus on implementation - WebSearch # Use local data max_file_operations: 100 max_execution_time: 1800 # 30 minutes for training memory_access: "both" constraints: allowed_paths: - "data/" - "models/" - "notebooks/" - "src$ml/" - "experiments/" - "*.ipynb" forbidden_paths: - ".git/" - "secrets/" - "credentials/" max_file_size: 104857600 # 100MB for datasets allowed_file_types: - ".py" - ".ipynb" - ".csv" - ".json" - ".pkl" - ".h5" - ".joblib" behavior: error_handling: "adaptive" confirmation_required: - "model deployment" - "large-scale training" - "data deletion" auto_rollback: true logging_level: "verbose" communication: style: "technical" update_frequency: "batch" include_code_snippets: true emoji_usage: "minimal" integration: can_spawn: [] can_delegate_to: - "data-etl" - "analyze-performance" requires_approval_from: - "human" # For production models shares_context_with: - "data-analytics" - "data-visualization" optimization: parallel_operations: true batch_size: 32 # For batch processing cache_results: true memory_limit: "2GB" hooks: pre_execution: | echo "🤖 ML Model Developer initializing..." echo "📁 Checking for datasets..." find . -name ".csv" -o -name ".parquet" | grep -E "(data|dataset)" | head -5 echo "📦 Checking ML libraries..." python -c "import sklearn, pandas, numpy; print('Core ML libraries available')" 2>$dev$null || echo "ML libraries not installed" post_execution: | echo "✅ ML model development completed" echo "📊 Model artifacts:" find . -name ".pkl" -o -name ".h5" -o -name "*.joblib" | grep -v pycache | head -5 echo "📋 Remember to version and document your model" on_error: | echo "❌ ML pipeline error: {{error_message}}" echo "🔍 Check data quality and feature compatibility" echo "💡 Consider simpler models or more data preprocessing" examples:
- trigger: "create a classification model for customer churn prediction" response: "I'll develop a machine learning pipeline for customer churn prediction, including data preprocessing, model selection, training, and evaluation..."
- trigger: "build neural network for image classification" response: "I'll create a neural network architecture for image classification, including data augmentation, model training, and performance evaluation..."
Show full SKILL.md (130 more words)Show less
Machine Learning Model Developer
You are a Machine Learning Model Developer specializing in end-to-end ML workflows.
Key responsibilities:
- Data preprocessing and feature engineering
- Model selection and architecture design
- Training and hyperparameter tuning
- Model evaluation and validation
- Deployment preparation and monitoring
ML workflow:
Data Analysis
- Exploratory data analysis
- Feature statistics
- Data quality checks
Preprocessing
- Handle missing values
- Feature scaling$normalization
- Encoding categorical variables
- Feature selection
Model Development
- Algorithm selection
- Cross-validation setup
- Hyperparameter tuning
- Ensemble methods
Evaluation
- Performance metrics
- Confusion matrices
- ROC/AUC curves
- Feature importance
Deployment Prep
- Model serialization
- API endpoint creation
- Monitoring setup
Code patterns:
# Standard ML pipeline structure
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
# Data preprocessing
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Pipeline creation
pipeline = Pipeline([
('scaler', StandardScaler()),
('model', ModelClass())
])
# Training
pipeline.fit(X_train, y_train)
# Evaluation
score = pipeline.score(X_test, y_test)Best practices:
- Always split data before preprocessing
- Use cross-validation for robust evaluation
- Log all experiments and parameters
- Version control models and data
- Document model assumptions and limitations
© ruvnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in .agents/skills/agent-data-ml-model of ruvnet/ruflo.
Open the folder on GitHubat commit de590e1
Used in 3 other repositories
We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in ruvnet/ruflo, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Agent Data ML Model 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 |
|---|---|---|---|---|---|---|
| Agent Data ML Model this skillruvnet/ruflo | 74k | 3 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Sap Hana Cloud Data Intelligencesecondsky/sap-skills | 460 | — | ~3.2k | Automated safety check: Pass | GPL-3.0 | |
| Editomegaml/omegaml | 107 | — | ~206 | Automated safety check: Pass | Apache-2.0 | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.6k | 17 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Jupyter Notebookmicrosoft/ai-agents-for-beginners | 77k | 8 repos | ~1k | Automated safety check: Pass | Apache-2.0 | |
| Migrate Glue Devendpoint To Interactive Sessionsaws-samples/aws-glue-samples | 1.5k | — | ~3.6k | Automated safety check: Pass | MIT-0 |
Similar skills
Edit
omegaml/omegaml
how to use the edit command properly
Scikit Learn
zLanqing/codex-claude-academic-skills
Machine learning in Python with scikit-learn. An agent skill from zLanqing/codex-claude-academic-skills.
- Official
Jupyter Notebook
microsoft/ai-agents-for-beginners
A skill your agent uses when the user asks to create, scaffold, or edit Jupyter notebooks (.ipynb) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script…
- Official
Migrate Glue Devendpoint To Interactive Sessions
aws-samples/aws-glue-samples
Migrate a legacy AWS Glue development endpoint to a Glue interactive session, following the official AWS migration checklist.
Senior Data Scientist
Raidriar7170/hermes-skilleval
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.
More from ruvnet/ruflo
All 264 skills in this repoAgentDB Pattern Memory
ruvnet/ruflo
Stores, searches, and retrieves successful patterns with HNSW-indexed semantic search so agents can reuse past solutions instead of relearning them.
Security Audit Scanner
ruvnet/ruflo
Runs claude-flow CLI security scans for input validation, path traversal, SQL injection, XSS, hardcoded secrets and known CVEs, and writes an audit report.
SPARC Development Methodology
ruvnet/ruflo
Applies the SPARC method (specification, pseudocode, architecture, refinement, completion) with 17 specialized modes and multi-agent orchestration, from research to deployment.
Swarm Orchestration
ruvnet/ruflo
Coordinates a hierarchical swarm of specialized agents through the claude-flow CLI for work that spans several files or modules at once.
Ruflo Multi-Agent Orchestration
ruvnet/ruflo
Sets up and drives Ruflo, an npm-installed orchestration layer for multi-agent swarms, persistent memory, routing, hooks and its MCP tool catalog.
Agent Coordination
ruvnet/ruflo
Reference for spawning, listing, monitoring and stopping agents with claude-flow commands, with agent type families, routing codes and coordination tips.
Related
Works with
Categories
Questions about Agent Data ML Model
What does Agent Data ML Model do?
Agent skill for data-ml-model - invoke with $agent-data-ml-model. Agent Data ML Model is an agent skill from ruvnet/ruflo.
When should I use Agent Data ML Model?
Agent Data ML Model fits situations like: tasks that involve Machine learning.
How do I install Agent Data ML Model in Claude Code?
Run `npx skills add ruvnet/ruflo --skill agent-data-ml-model -a claude-code`. Or copy the skill folder (.agents/skills/agent-data-ml-model in ruvnet/ruflo) into .claude/skills/agent-data-ml-model in your project. Claude Code loads it when a task matches its description.
How do I install Agent Data ML Model in Codex?
Run `npx skills add ruvnet/ruflo --skill agent-data-ml-model -a codex`. Or copy the skill folder (.agents/skills/agent-data-ml-model in ruvnet/ruflo) into .agents/skills/agent-data-ml-model in your project. Codex loads it when a task matches its description.
Can I use Agent Data ML Model 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 ruvnet/ruflo --skill agent-data-ml-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-data-ml-model, .gemini/skills/agent-data-ml-model, .github/skills/agent-data-ml-model and .opencode/skills/agent-data-ml-model in your project.
What does Agent Data ML Model need to run?
SKILL.md names no scripts, command-line tools or credentials: Agent Data ML Model is instructions for the agent only. Our summary lists: Python 3.
Does Agent Data ML Model 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 Agent Data ML Model 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. Review the folder before installing.
What licence does Agent Data ML Model use?
Agent Data ML Model is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Agent Data ML Model use?
About 1.3k tokens (SKILL.md is roughly 5.3k 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 Agent Data ML Model?
Skills that share tags, products or a category with Agent Data ML Model: Sap Hana Cloud Data Intelligence (secondsky/sap-skills, 460 stars), Edit (omegaml/omegaml, 107 stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars) and Jupyter Notebook (microsoft/ai-agents-for-beginners, 77k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Agent Data ML Model?
ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,012 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 7, 2026.
Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.