Babysitter wandb-experiment-tracker
Weights & Biases integration skill for experiment tracking, hyperparameter sweeps, and artifact versioning.
install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/data-science-ml/skills/wandb-experiment-tracker" ~/.claude/skills/a5c-ai-babysitter-wandb-experiment-tracker && rm -rf "$T"
manifest:
library/specializations/data-science-ml/skills/wandb-experiment-tracker/SKILL.mdsource content
wandb-experiment-tracker
Overview
Weights & Biases integration skill for experiment tracking, hyperparameter sweeps, artifact versioning, and team collaboration.
Capabilities
- Experiment logging and visualization
- Hyperparameter sweep configuration and execution
- Artifact versioning and lineage tracking
- Table and media logging (images, audio, video)
- Team collaboration features
- Report generation and sharing
- Model registry integration
- Custom visualization dashboards
Target Processes
- Model Training Pipeline with Experiment Tracking
- Experiment Planning and Hypothesis Testing
- Model Evaluation and Validation Framework
Tools and Libraries
- Weights & Biases (wandb)
Input Schema
{ "type": "object", "required": ["action"], "properties": { "action": { "type": "string", "enum": ["init", "log", "sweep", "artifact", "alert", "report"], "description": "W&B action to perform" }, "project": { "type": "string", "description": "W&B project name" }, "runConfig": { "type": "object", "properties": { "name": { "type": "string" }, "tags": { "type": "array", "items": { "type": "string" } }, "notes": { "type": "string" }, "config": { "type": "object" } } }, "logData": { "type": "object", "properties": { "metrics": { "type": "object" }, "step": { "type": "integer" }, "commit": { "type": "boolean" } } }, "sweepConfig": { "type": "object", "properties": { "method": { "type": "string", "enum": ["grid", "random", "bayes"] }, "metric": { "type": "object" }, "parameters": { "type": "object" } } }, "artifactConfig": { "type": "object", "properties": { "name": { "type": "string" }, "type": { "type": "string" }, "path": { "type": "string" } } } } }
Output Schema
{ "type": "object", "required": ["status", "action"], "properties": { "status": { "type": "string", "enum": ["success", "error"] }, "action": { "type": "string" }, "runId": { "type": "string" }, "runUrl": { "type": "string" }, "sweepId": { "type": "string" }, "artifactId": { "type": "string" }, "artifactUrl": { "type": "string" } } }
Usage Example
{ kind: 'skill', title: 'Log training metrics to W&B', skill: { name: 'wandb-experiment-tracker', context: { action: 'log', project: 'ml-experiments', runConfig: { name: 'resnet-v1', tags: ['baseline', 'resnet'], config: { lr: 0.001, epochs: 100 } }, logData: { metrics: { loss: 0.5, accuracy: 0.85 }, step: 10 } } } }