ml-engineering
Use when "deploying ML models", "MLOps", "model serving", "feature stores", "model monitoring", or asking about "PyTorch deployment", "TensorFlow production", "RAG systems", "LLM integration", "ML infrastructure"
What this skill does
<!-- Adapted from: claude-skills/engineering-team/senior-ml-engineer -->
# ML Engineering Guide
Production-grade ML/AI systems, MLOps, and model deployment.
## When to Use
- Deploying ML models to production
- Building ML platforms and infrastructure
- Implementing MLOps pipelines
- Integrating LLMs into production systems
- Setting up model monitoring and drift detection
## Tech Stack
| Category | Tools |
|----------|-------|
| ML Frameworks | PyTorch, TensorFlow, Scikit-learn, XGBoost |
| LLM Frameworks | LangChain, LlamaIndex, DSPy |
| Data Tools | Spark, Airflow, dbt, Kafka, Databricks |
| Deployment | Docker, Kubernetes, AWS/GCP/Azure |
| Monitoring | MLflow, Weights & Biases, Prometheus |
| Databases | PostgreSQL, BigQuery, Snowflake, Pinecone |
## Production Patterns
### Model Deployment Pipeline
```python
# Model serving with FastAPI
from fastapi import FastAPI
import torch
app = FastAPI()
model = torch.load("model.pth")
@app.post("/predict")
async def predict(data: dict):
tensor = preprocess(data)
with torch.no_grad():
prediction = model(tensor)
return {"prediction": prediction.tolist()}
```
### Feature Store Integration
```python
# Feast feature store
from feast import FeatureStore
store = FeatureStore(repo_path=".")
features = store.get_online_features(
features=["user_features:age", "user_features:location"],
entity_rows=[{"user_id": 123}]
).to_dict()
```
### Model Monitoring
```python
# Drift detection
from evidently import ColumnMapping
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset
report = Report(metrics=[DataDriftPreset()])
report.run(reference_data=ref_df, current_data=curr_df)
```
## MLOps Best Practices
### Development
- Test-driven development for ML pipelines
- Version control models and data
- Reproducible experiments with MLflow
### Production
- A/B testing infrastructure
- Canary deployments for models
- Automated retraining pipelines
- Model monitoring and drift detection
### Performance Targets
| Metric | Target |
|--------|--------|
| P50 Latency | < 50ms |
| P95 Latency | < 100ms |
| P99 Latency | < 200ms |
| Throughput | > 1000 RPS |
| Availability | 99.9% |
## LLM Integration Patterns
### RAG System
```python
# Basic RAG with LangChain
from langchain.vectorstores import Pinecone
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
vectorstore = Pinecone.from_existing_index(
index_name="docs",
embedding=OpenAIEmbeddings()
)
qa = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever()
)
```
### Prompt Management
```python
# Structured prompts with DSPy
import dspy
class QA(dspy.Signature):
"""Answer questions based on context."""
context = dspy.InputField()
question = dspy.InputField()
answer = dspy.OutputField()
qa = dspy.Predict(QA)
```
## Common Commands
```bash
# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/
# Training
python scripts/train.py --config prod.yaml
mlflow run . -P epochs=10
# Deployment
docker build -t model:v1 .
kubectl apply -f k8s/model-serving.yaml
# Monitoring
mlflow ui --port 5000
```
## Security & Compliance
- Authentication for model endpoints
- Data encryption (at rest & in transit)
- PII handling and anonymization
- GDPR/CCPA compliance
- Model access audit logging
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.