ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization.
SkillScore70Maintained
Automatic
◆AI Engineer pack
Last verified 8/20/2026
What it changes
Passed 16 of 23 test cases with the skill, and 9 of 23 without it.
Without skill39%
With skill70%
Checks it passed that the model alone missed
Uses tenacity for retries.
Sets the p95 latency threshold to under 100ms.
Uses Feast for online feature vector retrieval.
and 5 more in the full report
7 fixed9 passed either way7 still failing0 broke
Pass-rate lift
+30 pp
Tokens
+70%
Agent turns
0%
Response time
+10%
Model gemini-3.6-flashMeasured 8/20/2026v116 of 23 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 7
Prompt
Just draft it: Show the CLI command to generate deployment artifacts using the deployment script tool with verbose JSON output logging.
What was checked, without and with the skill
Checked
Without skill
With skill
Includes the --verbose flag in the CLI command.
Passed
Passed
Includes the --input parameter in the CLI command.
Missed
Passed
Includes the --output parameter in the CLI command.
Passed
Passed
case-07 · gemini-3.6-flash · 8/20/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md79 lines9 filesincludes scripts
Senior ML Engineer
Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration.
Core Capabilities
Model deployment — export to ONNX/TorchScript/SavedModel, containerize, canary rollout, and serve via FastAPI, Triton, TF Serving, TorchServe, or Ray Serve with p95<100ms / error<0.1% gates.
MLOps pipelines — feature stores (Feast/Tecton), experiment tracking (MLflow/W&B), model registry, A/B testing, and drift-triggered retraining.
LLM integration — provider abstraction, retry/fallback with exponential backoff, token counting, response caching, cost tracking, and Pydantic output validation.
RAG systems — vector database selection, chunking strategies, ingestion, retrieval, and reranking.
Model monitoring — latency/error tracking, input drift detection (KS test, PSI), prediction-shift alerts, and automated retraining triggers.
When to Use
Deploying a trained model to production with canary rollout and monitoring.
Standing up MLOps infrastructure (feature store, registry, retraining).
Integrating LLM APIs with provider abstraction and cost control.
Building a RAG pipeline (vector DB + chunking + retrieval + reranking).
Setting up drift detection and model-health alerting.
Clarify First
Before generating artifacts, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Task — model deployment / RAG pipeline build / monitoring setup (selects the script and workflow)
Serving target & rollout — container vs K8s and canary vs direct (drives the generated Dockerfile/manifests and health gates)
Model or data interface — the input/output contract, and for RAG the corpus + vector store (shapes the scaffold)
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Tools
Tool
Purpose
Command
model_deployment_pipeline.py
Generate deployment artifacts (Dockerfile, K8s manifests, health checks)
All tools support --verbose/-v and emit JSON (status, start_time, end_time, processed_items) to stdout. See references/tool-reference.md for full flag detail.
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