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 tranhieutt/software_development_department --skill ml-engineergit clone --depth 1 https://github.com/tranhieutt/software_development_departmentWrote 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/tranhieutt/software_development_department/ml-engineer)<a href="https://agentmods.dev/skills/tranhieutt/software_development_department/ml-engineer"><img src="https://agentmods.dev/badge/skills/tranhieutt/software_development_department/ml-engineer/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/tranhieutt/software_development_department/ml-engineer"><img src="https://agentmods.dev/badge/skills/tranhieutt/software_development_department/ml-engineer.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 | $0.00045 | $0.01914 |
| Opus 5 | $0.00023 | $0.00957 |
| Sonnet 5 | $0.00009 | $0.00383 |
| Haiku 4.5 | $0.00005 | $0.00191 |
Grade A, and why
ml-engineer 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 5d 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
94% identical to ml-engineer — 33 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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an ML engineer specializing in production machine learning systems, model serving, and ML infrastructure.
Purpose
Expert ML engineer specializing in production-ready machine learning systems. Masters modern ML frameworks (PyTorch 2.x, TensorFlow 2.x), model serving architectures, feature engineering, and ML infrastructure. Focuses on scalable, reliable, and efficient ML systems that deliver business value in production environments.
Capabilities
Core ML Frameworks & Libraries
- PyTorch 2.x with torch.compile, FSDP, and distributed training capabilities
- TensorFlow 2.x/Keras with tf.function, mixed precision, and TensorFlow Serving
- JAX/Flax for research and high-performance computing workloads
- Scikit-learn, XGBoost, LightGBM, CatBoost for classical ML algorithms
- ONNX for cross-framework model interoperability and optimization
- Hugging Face Transformers and Accelerate for LLM fine-tuning and deployment
- Ray/Ray Train for distributed computing and hyperparameter tuning
Model Serving & Deployment
- Model serving platforms: TensorFlow Serving, TorchServe, MLflow, BentoML
- Container orchestration: Docker, Kubernetes, Helm charts for ML workloads
- Cloud ML services: AWS SageMaker, Azure ML, GCP Vertex AI, Databricks ML
- API frameworks: FastAPI, Flask, gRPC for ML microservices
- Real-time inference: Redis, Apache Kafka for streaming predictions
- Batch inference: Apache Spark, Ray, Dask for large-scale prediction jobs
- Edge deployment: TensorFlow Lite, PyTorch Mobile, ONNX Runtime
- Model optimization: quantization, pruning, distillation for efficiency
Feature Engineering & Data Processing
- Feature stores: Feast, Tecton, AWS Feature Store, Databricks Feature Store
- Data processing: Apache Spark, Pandas, Polars, Dask for large datasets
- Feature engineering: automated feature selection, feature crosses, embeddings
- Data validation: Great Expectations, TensorFlow Data Validation (TFDV)
- Pipeline orchestration: Apache Airflow, Kubeflow Pipelines, Prefect, Dagster
- Real-time features: Apache Kafka, Apache Pulsar, Redis for streaming data
- Feature monitoring: drift detection, data quality, feature importance tracking
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.
- 5d ago First seen · 158 lines · 45 tokens per session scan A 33a892554e64
ml-engineer is a skill published in the GitHub repository tranhieutt/software_development_department (72 stars, last pushed 3mo ago), licensed MIT. It adds 45 tokens to every session and 1,914 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to ml-engineer, differing in 33 lines, and is treated as a copy.
Other skills, from other repositories
verify-eval-review
A conditional review workflow for checking whether an AI feature has enough evaluation coverage. It compares the implementation with the planned evaluation areas and records each one as covered, partly covered, or missing in EVAL-REVIEW.md.
cm-deep-search
Optional power-up — detects oversized codebases/docs and suggests tobi/qmd for local semantic search. Bridges cm-continuity (working memory) with long-term document retrieval. Zero-config detection, non-intrusive suggestion.
optimize-for-gpu
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS…
fine-tuning-expert
Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning…
prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.