| name | tracking-model-versions |
| description | Build this skill enables AI assistant to track and manage ai/ml model versions using the model-versioning-tracker plugin. it should be used when the user asks to manage model versions, track model lineage, log model performance, or implement version control f... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
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| allowed-tools | Read, Write, Edit, Grep, Glob, Bash(cmd:*) |
| version | 1.24.0 |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| license | MIT |
| tags | ["ai","performance","tracking-model"] |
| compatibility | Designed for Claude Code, also compatible with Codex and OpenClaw |
Model Versioning Tracker
Overview
Track and manage AI/ML model versions using MLflow, DVC, or Weights & Biases. Log model metadata (hyperparameters, training data hash, framework version), record evaluation metrics (accuracy, F1, latency), manage model registry transitions (Staging, Production, Archived), and generate model cards documenting lineage and performance.
Prerequisites
- MLflow tracking server running locally or remotely (
mlflow server or managed MLflow)
- Python 3.9+ with
mlflow, pandas, and the relevant ML framework installed
- Model artifacts accessible on the local filesystem or cloud storage (S3, GCS)
- Write access to the MLflow tracking URI and artifact store
Instructions
- Connect to the MLflow tracking server by setting
MLFLOW_TRACKING_URI and verify connectivity with mlflow experiments list.
- Create or select an MLflow experiment for the model project using
mlflow experiments create --experiment-name <name>.
- Log a new model version: start an MLflow run, log parameters (learning rate, epochs, batch size), log metrics (accuracy, loss, F1 score), and log the model artifact with
mlflow.<flavor>.log_model().
- Register the model in the MLflow Model Registry using
mlflow.register_model() with the run URI and a descriptive model name.
- Transition the model version through stages:
None -> Staging -> Production using client.transition_model_version_stage(). Archive previous production versions.
- Compare model versions by querying metrics across runs with
mlflow.search_runs() and generating comparison tables showing metric improvements between versions.
- Generate a model card from the registered model metadata, including training data description, evaluation metrics, intended use, limitations, and ethical considerations. See
${CLAUDE_SKILL_DIR}/assets/model_card_template.md.
- Set up automated alerts for model performance degradation by comparing production metrics against baseline thresholds stored in the model registry.
See ${CLAUDE_SKILL_DIR}/assets/example_mlflow_workflow.yaml for a complete workflow configuration.
Examples
Tracking a new image classification model version: Log a ResNet-50 fine-tuned on a custom dataset. Record hyperparameters (lr=0.001, epochs=50, optimizer=Adam), metrics (val_accuracy=0.94, val_loss=0.18, inference_latency_ms=12), and the serialized model artifact. Register as version 3 in the model registry and transition to Staging for validation.
