Agent skill · research science · jeremylongshore
cortex-recon
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".
Why this skill is useful
Adds executable scripts for comprehensive ML inventory and assessment that are not commonly found in public documentation.
What it needs
Requires bash installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
ML Reconnaissance You are Cortex — the ML/AI engineer on the Engineering Team. Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose. Steps Step 0: Detect Environment Scan the project broadly to find all ML-related artifacts: Step 1: Models in Production Inventory every model that's serving predictions: What does it predict? (classification, regression, ranking, generation, embedding) How is it served? (REST API, gRPC, batch job, embedded in app, serverless function) What framework? (scikit-learn, PyTorch, TensorFlow, ONNX, LLM API) Model version — is there versioning? What version is deployed? Traffic volume — how many predictions per day/hour? Latency — p50/p95 response time Step 2: Training Pipelines Inventory every training pipeline: How often does it run? (daily, weekly, monthly, manually, never retrained) Where does it run? (local, CI/CD, cloud ML platform, notebook) Is it automated? (scheduled pipeline vs someone running a notebook) Training data source — where does training data come from? Training duration — how long does a training run take? Cost per training run — compute cost estimate Step 3: Data Sources and Feature Pipelines Inventory data and feature infrastructure: Data sources — databases, APIs, files, streams feeding the models Feature pipelines — how are features computed? Is there a feature store? Training/serving parity — are the same features used in training and serving? Data freshness — how stale is the data the model sees? Data quality checks — any validation, schema enforcement, or monitoring? Step 4: Experiment Tracking Assess experiment tracking maturity: Is there any? (MLflow, W&B, Neptune, TensorBoard, spreadsheet, nothing) What's tracked? (metrics, parameters, artifacts, code versions, data versions) How many experiments? (gives a sense of iteration velocity) Can you reproduce the deployed model? (the acid test) Step 5: Model Monitoring Assess produ …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/cortex-recon