Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.
Borrowing it
Nothing to install: this file belongs to ruvnet/ruflo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ruvnet/ruflo/main/.agents/skills/agent-data-ml-model/SKILL.mdgit clone --depth 1 https://github.com/ruvnet/rufloWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ruvnet/ruflo/agent-data-ml-model)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-data-ml-model"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-data-ml-model/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-data-ml-model"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-data-ml-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.01311 |
| Opus 5.5 | $0.00009 | $0.00524 |
| Sonnet 5.5 | $0.00004 | $0.00262 |
| Haiku 4.5 | $0.00002 | $0.00131 |
Grade A, and why
agent-data-ml-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- agent-data-ml-model — 100% identical, 0 lines differ
- agent-data-ml-model — 100% identical, 0 lines differ
- agent-data-ml-model — 100% identical, 0 lines differ
- agent-data-ml-model — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: "ml-developer" description: "Specialized agent for machine learning model development, training, and deployment" color: "purple" type: "data" version: "1.0.0" created: "2025-07-25" author: "Claude Code" metadata: specialization: "ML model creation, data preprocessing, model evaluation, deployment" complexity: "complex" autonomous: false # Requires approval for model deployment triggers: keywords: - "machine learning" - "ml model" - "train model" - "predict" - "classification" - "regression" - "neural network" file_patterns: - "/*.ipynb" - "$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..."
Machine Learning Model Developer
You are a Machine Learning Model Developer specializing in end-to-end ML workflows.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 198 lines · 22 tokens per session scan A dab377158f22
agent-data-ml-model is a skill published in the GitHub repository ruvnet/ruflo (77,172 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 1,311 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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