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 agentmods add skills/proffesor-for-testing/agentic-qe/agent-data-ml-modelnpx 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.svg" alt="Measured on agentmods" 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 | $0.00022 | $0.01311 |
| Opus 5 | $0.00011 | $0.00656 |
| Sonnet 5 | $0.00004 | $0.00262 |
| Haiku 4.5 | $0.00002 | $0.00131 |
Grade A, and why
agent-data-ml-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to agent-data-ml-model — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: "ml-developer" description: "Specialized agent for machine learning model development, training, and deployment" color: "purple" type: "data" version: "1.0.0" created: "2025-07-25" author: "Claude Code" metadata: specialization: "ML model creation, data preprocessing, model evaluation, deployment" complexity: "complex" autonomous: false # Requires approval for model deployment triggers: keywords: - "machine learning" - "ml model" - "train model" - "predict" - "classification" - "regression" - "neural network" file_patterns: - "/*.ipynb" - "$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.
- 4d 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 (473 stars, last pushed 2d ago), licensed MIT. It adds 22 tokens to every session and 1,311 once invoked, about $0.0001 per session on Opus 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.
Other skills, from other repositories
iso-9001-internal-audit
Conduct an internal audit by clause, answer ISO 9001 internal audit questions, or prepare evidence for §4 §5 §6 §7 §8 §9 §10. Provides key audit questions by clause, finding classification (Major NC / Minor NC / OFI), and audit report writing. Use when planning or conducting an ISO 9001:2015 internal audit or…
dfmea-design
Build a design risk analysis, DFMEA worksheet, or interface analysis using the AIAG-VDA FMEA Handbook 2019. Covers design intent, interface failures, boundary diagram, and design robustness before manufacturing. Use during new product development, design changes, or when a field failure reveals a design weakness.…
pfmea-process
Build a PFMEA worksheet, process risk analysis, or AP table using the AIAG-VDA FMEA Handbook 2019 7-step approach. Covers Structure Analysis, Function Analysis, Failure Analysis, Risk Analysis (Action Priority H/M/L), Optimization, and Documentation. Required by IATF 16949 and OEM customer-specific requirements for…
audit-guide
Interactive internal audit guide for ISO 9001:2015 and IATF 16949:2016 — walks through key clauses interactively, scores findings as Major NC / Minor NC / OFI, and generates a structured audit report. Use when conducting an internal audit and needing real-time finding documentation and a structured clause-by-clause…
fmea-reviewer
PFMEA and DFMEA gap audit against AIAG-VDA FMEA Handbook 2019 — reviews an existing FMEA for missing failure modes, incorrect AP ratings, unaddressed H-AP items, missing special characteristics, and PFMEA-to-Control Plan linkage gaps. Returns a structured gap report with specific findings and required actions before…
ncr-writer
Write a non-conformance report or NCR fast — converts bullet-point inputs into professional objective-evidence language, suggests Critical/Major/Minor severity, recommends disposition, and flags missing required information. Use when writing an NCR quickly or when informal defect observations need to become formal…