Mle Workflow
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
Specification
- Skill ID
- affaan-m/ecc/mle-workflow
- Publisher
- affaan-m
- Repository
- ecc
- Installs
- 0
- Files
- 2
- License
- MIT
- Synced
- Sep 23, 2026
How to use it
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
affaan-m/ecc/mle-workflowInstalls these files- SKILL.md
- agents/openai.yaml
What this skill tells the agent
Machine Learning Engineering Workflow
Use this skill to turn model work into a production ML system with clear data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.
When to Activate
- Planning or reviewing a production ML feature, model refresh, ranking system, recommender, classifier, embedding workflow, or forecasting pipeline
- Converting notebook code into a reusable training, evaluation, batch inference, or online inference pipeline
- Designing model promotion criteria, offline/online evals, experiment tracking, or rollback paths
- Debugging failures caused by data drift, label leakage, stale features, artifact mismatch, or inconsistent training and serving logic
- Adding model monitoring, canary rollout, shadow traffic, or post-deploy quality checks
Scope Calibration
Use only the lanes that fit the system in front of you. This skill is useful for ranking, search, recommendations, classifiers, forecasting, embeddings, LLM workflows, anomaly detection, and batch analytics, but it should not force one architecture onto all of them.
- Do not assume every model has supervised labels, online serving, a feature store, PyTorch, GPUs, human review, A/B tests, or real-time feedback.
- Do not add heavyweight MLOps machinery when a data contract, baseline, eval script, and rollback note would make the change reviewable.
- Do make assumptions explicit when the project lacks labels, delayed outcomes, slice definitions, production traffic, or monitoring ownership.
- Treat examples as interchangeable scaffolds. Replace metrics, serving mode, data stores, and rollout mechanics with the project-native equivalents.
Related Skills
python-patternsandpython-testingfor Python implementation and pytest coveragepytorch-patternsfor deep learning models, data loaders, device handling, and training loopseval-harnessandai-regression-testingfor promotion gates and agent-assisted regression checksdatabase-migrations,postgres-patterns, andclickhouse-iofor data storage and analytics surfacesdeployment-patterns,docker-patterns, andsecurity-reviewfor serving, secrets, containers, and production hardening
Reuse the SWE Surface
Do not treat MLE as separate from software engineering. Most ECC SWE workflows apply directly to ML systems, often with stricter failure modes:
The recommended minimal --with capability:machine-learning install keeps the core agent surface available alongside this skill. For skill-only or agent-limited harnesses, pair skill:mle-workflow with agent:mle-reviewer where the target supports agents.
| SWE surface | MLE use |
|---|---|
product-capability / architecture-decision-records | Turn model work into explicit product contracts and record irreversible data, model, and rollout choices |
repo-scan / codebase-onboarding / code-tour | Find existing training, feature, serving, eval, and monitoring paths before introducing a parallel ML stack |
plan / feature-dev | Scope model changes as product capabilities with data, eval, serving, and rollback phases |
tdd-workflow / python-testing | Test feature transforms, split logic, metric calculations, artifact loading, and inference schemas before implementation |
code-reviewer / mle-reviewer | Review code quality plus ML-specific leakage, reproducibility, promotion, and monitoring risks |
build-fix / pr-test-analyzer | Diagnose broken CI, flaky evals, missing fixtures, and environment-specific model or dependency failures |
quality-gate / test-coverage | Require automated evidence for transforms, metrics, inference contracts, promotion gates, and rollback behavior |
eval-harness / verification-loop | Turn offline metrics, slice checks, latency budgets, and rollback drills into repeatable gates |
ai-regression-testing | Preserve every production bug as a regression: missing feature, stale label, bad artifact, schema drift, or serving mismatch |
api-design / backend-patterns | Design prediction APIs, batch jobs, idempotent retraining endpoints, and response envelopes |
database-migrations / postgres-patterns / clickhouse-io | Version labels, feature snapshots, prediction logs, experiment metrics, and drift analytics |
deployment-patterns / docker-patterns | Package reproducible training and serving images with health checks, resource limits, and rollback |
canary-watch / dashboard-builder | Make rollout health visible with model-version, slice, drift, latency, cost, and delayed-label dashboards |
security-review / security-scan | Check model artifacts, notebooks, prompts, datasets, and logs for secrets, PII, unsafe deserialization, and supply-chain risk |
e2e-testing / browser-qa / accessibility | Test critical product flows that consume predictions, including explainability and fallback UI states |
benchmark / performance-optimizer | Measure throughput, p95 latency, memory, GPU utilization, and cost per prediction or retrain |
cost-aware-llm-pipeline / token-budget-advisor | Route LLM/embedding workloads by quality, latency, and budget instead of defaulting to the largest model |
documentation-lookup / search-first | Verify current library behavior for model serving, feature stores, vector DBs, and eval tooling before coding |
git-workflow / github-ops / opensource-pipeline | Package MLE changes for review with crisp scope, generated artifacts excluded, and reproducible test evidence |
strategic-compact / dmux-workflows | Split long ML work into parallel tracks: data contract, eval harness, serving path, monitoring, and docs |
Ten MLE Task Simulations
Use these simulations as coverage checks when planning or reviewing MLE work. A strong MLE workflow should reduce each task to explicit contracts, reusable SWE surfaces, automated evidence, and a reviewable artifact.
| ID | Common MLE task | Streamlined ECC path | Required output | Pipeline lanes covered |
|---|---|---|---|---|
| MLE-01 | Frame an ambiguous prediction, ranking, recommender, classifier, embedding, or forecast capability | product-capability, plan, architecture-decision-records, mle-workflow | Iteration Compact naming who cares, decision owner, success metric, unacceptable mistakes, assumptions, constraints, and first experiment | product contract, stakeholder loss, risk, rollout |
| MLE-02 | Define metric goals, labels, data sources, and the mistake budget | repo-scan, database-reviewer, database-migrations, postgres-patterns, clickhouse-io | Data and metric contract with entity grain, label timing, label confidence, feature timing, point-in-time joins, split policy, and dataset snapshot | data contract, metric design, leakage, reproducibility |
| MLE-03 | Build a baseline model and scoring path before adding complexity | tdd-workflow, python-testing, python-patterns, code-reviewer | Baseline scorer with confusion matrix, calibration notes, latency/cost estimate, known weaknesses, and tests for score shape and determinism | baseline, scoring, testing, serving parity |
| MLE-04 | Generate features from hypotheses about what separates outcomes | python-patterns, pytorch-patterns, docker-patterns, deployment-patterns | Feature plan and transform module covering signal source, missing values, outliers, correlations, leakage checks, and train/serve equivalence | feature pipeline, leakage, training, artifacts |
| MLE-05 | Tune thresholds, configs, and model complexity under tradeoffs | eval-harness, ai-regression-testing, quality-gate, test-coverage | Threshold/config report comparing precision, recall, F1, AUC, calibration, group slices, latency, cost, complexity, and acceptable error classes | evaluation, threshold, promotion, regression |
More from affaan-m
Use this skill in a project
Open any RiverX project, search the Skills panel for affaan-m/ecc/mle-workflow, and install it in one click.