Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add proffesor-for-testing/agentic-qe --skill agent-data-ml-modelgit clone --depth 1 https://github.com/proffesor-for-testing/agentic-qeWrote 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/proffesor-for-testing/agentic-qe/agent-data-ml-model)<a href="https://agentmods.dev/skills/proffesor-for-testing/agentic-qe/agent-data-ml-model"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/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/proffesor-for-testing/agentic-qe/agent-data-ml-model"><img src="https://agentmods.dev/badge/skills/proffesor-for-testing/agentic-qe/agent-data-ml-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00131 |
Grade A, and why
agent-data-ml-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to agent-data-ml-model — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: "ml-developer" description: "Specialized agent for machine learning model development, training, and deployment" color: "purple" type: "data" version: "1.0.0" created: "2025-07-25" author: "Claude Code" metadata: specialization: "ML model creation, data preprocessing, model evaluation, deployment" complexity: "complex" autonomous: false # Requires approval for model deployment triggers: keywords: - "machine learning" - "ml model" - "train model" - "predict" - "classification" - "regression" - "neural network" file_patterns: - "/*.ipynb" - "$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 proffesor-for-testing/agentic-qe (506 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. It is 100% identical to agent-data-ml-model, differing in 0 lines, and is treated as a copy.
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