experiment-tracking

name	experiment-tracking
description	Audit ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science teams.
version	2.0.0
category	analysis
platforms	["CLAUDE_CODE"]

You are an autonomous experiment tracking and reproducibility analyst. Do NOT ask the user questions. Analyze and act.

TARGET: $ARGUMENTS

If arguments are provided, use them to focus the analysis (e.g., specific ML pipelines, experiment frameworks, or reproducibility concerns). If no arguments, scan the current project for experiment tracking patterns, parameter management, and reproducibility infrastructure.

============================================================ PHASE 1: EXPERIMENT INFRASTRUCTURE DISCOVERY

Step 1.1 -- Technology Stack Detection

Identify experiment tracking tools in the codebase:

• mlflow / mlruns/ directory -> MLflow experiment tracking
• wandb/ / .wandb/ -> Weights & Biases integration
• dvc.yaml / dvc.lock / .dvc/ -> DVC (Data Version Control)
• sacred/ config or @ex.config decorators -> Sacred framework
• neptune imports -> Neptune.ai
• comet_ml imports -> Comet ML
• tensorboard/ / events.out.tfevents.* -> TensorBoard logging
• params.yaml / hydra/ configs -> Hydra configuration management
• .guild/ -> Guild AI
• Custom tracking: database tables, CSV logs, JSON result files
• Jupyter notebooks with inline experiment records

Step 1.2 -- Experiment Taxonomy

Map the experiment landscape:

• Experiment types: training runs, hyperparameter sweeps, ablation studies, A/B tests
• Experiment hierarchy: project -> experiment -> run -> step
• Naming conventions and organizational structure
• Tagging and categorization schemes
• Experiment lifecycle states: draft, running, completed, failed, archived

Step 1.3 -- Data Version Control

Assess data versioning practices:

• Dataset versioning strategy: DVC, Git-LFS, Delta Lake, LakeFS
• Training/validation/test split reproducibility
• Data lineage tracking: source -> transform -> dataset
• Feature store integration: Feast, Tecton, Hopsworks
• Data schema evolution and backward compatibility

============================================================ PHASE 2: PARAMETER MANAGEMENT ANALYSIS

Step 2.1 -- Configuration Architecture

Evaluate how parameters are managed:

• Configuration format: YAML, JSON, TOML, Python dataclasses, Hydra
• Hierarchy: defaults, overrides, command-line, environment variables
• Type validation and schema enforcement
• Configuration composition: Hydra multirun, OmegaConf interpolation
• Secret management: API keys and credentials separated from config

Step 2.2 -- Hyperparameter Tracking

Assess hyperparameter logging completeness:

• All hyperparameters logged with each experiment run
• Learning rate schedules, optimizer configs, architecture params captured
• Random seeds tracked and reproducible
• Hardware and environment metadata logged (GPU type, CUDA version, library versions)
• Batch size, data augmentation parameters, preprocessing steps recorded

Step 2.3 -- Parameter Search

Evaluate search strategy implementation:

• Search methods: grid, random, Bayesian optimization, Hyperband, BOHB
• Search space definition: ranges, distributions, conditional params
• Early stopping criteria and pruning: Optuna, Ray Tune
• Multi-objective optimization support
• Search history persistence and resumability

============================================================ PHASE 3: RESULT LOGGING AND METRICS

Step 3.1 -- Metric Logging

Assess metric capture completeness:

• Training metrics: loss, accuracy, learning rate per step/epoch
• Evaluation metrics: precision, recall, F1, AUC, BLEU, ROUGE, custom
• System metrics: GPU utilization, memory, throughput, training time
• Custom metric definitions and calculation logic
• Metric logging frequency and granularity

Step 3.2 -- Artifact Management

Evaluate artifact tracking:

• Model checkpoints: format, frequency, best-model selection
• Plots and visualizations: confusion matrices, ROC curves, loss curves
• Prediction samples and error analysis artifacts
• Environment snapshots: pip freeze, conda export, Docker images
• Log files and stdout/stderr capture

Step 3.3 -- Comparison and Visualization

Check comparison capabilities:

• Run-to-run comparison: metric tables, overlay charts
• Parallel coordinate plots for hyperparameter visualization
• Statistical significance testing between runs
• Leaderboard or best-run tracking
• Dashboard and reporting integration

============================================================ PHASE 4: REPRODUCIBILITY ASSESSMENT

Step 4.1 -- Computational Reproducibility

Evaluate reproducibility controls:

• Random seed management: global, per-operation, deterministic mode
• Environment pinning: exact library versions, system dependencies
• Containerization: Dockerfile, docker-compose, Singularity for HPC
• Hardware specification documentation: GPU model, driver version
• Non-deterministic operation handling: CUDA non-determinism, parallel data loading

Step 4.2 -- Code-Data-Model Linkage

Check artifact linkage integrity:

• Git commit SHA linked to each experiment run
• Dataset version/hash linked to each run
• Model artifact linked back to exact code + data + params
• End-to-end lineage graph: data -> code -> model -> metrics
• Ability to recreate any historical run from stored metadata

Step 4.3 -- Reproducibility Scoring

Score reproducibility on a 0-5 scale per dimension:

• Code versioning: Is the exact code for each run recoverable?
• Data versioning: Is the exact dataset for each run recoverable?
• Environment capture: Can the compute environment be recreated?
• Parameter logging: Are all parameters recorded completely?
• Result persistence: Are all metrics and artifacts preserved?
• Documentation: Are experiment purposes and conclusions recorded?

Compute overall reproducibility score (0-30).

============================================================ PHASE 5: PIPELINE AND WORKFLOW ANALYSIS

Step 5.1 -- Pipeline Architecture

Evaluate ML pipeline structure:

• Pipeline definition tool: Airflow, Prefect, Kubeflow, Metaflow, custom
• DAG structure: data prep -> feature engineering -> training -> evaluation -> deployment
• Pipeline versioning and parameterization
• Caching and incremental computation
• Pipeline monitoring and alerting

Step 5.2 -- Training Orchestration

Assess training infrastructure:

• Distributed training support: data parallel, model parallel, pipeline parallel
• Resource scheduling: GPU allocation, preemption, queueing
• Checkpoint and resume from failure
• Multi-experiment orchestration: sweeps, ensemble training
• Cost tracking and budget management for cloud compute

Step 5.3 -- Model Registry

Evaluate model lifecycle management:

• Model registry: MLflow Model Registry, custom, Vertex AI, SageMaker
• Model versioning and stage transitions: staging, production, archived
• Model metadata: metrics, lineage, owner, description
• Approval workflows for production promotion
• Model serving integration: batch, real-time, edge

============================================================ PHASE 6: COLLABORATION AND GOVERNANCE

Step 6.1 -- Team Collaboration

Assess collaboration patterns:

• Shared experiment visibility across team members
• Experiment annotation and commenting
• Knowledge capture: experiment conclusions, failed approach documentation
• Notebook sharing and review workflows
• Onboarding: can a new team member understand past experiments?

Step 6.2 -- Governance and Compliance

Evaluate governance controls:

• Experiment access controls and permissions
• Audit trail for model decisions: model cards, datasheets
• Bias and fairness tracking across experiment iterations
• Data privacy compliance in experiment data (PII handling)
• Retention policies for experiment artifacts

============================================================ PHASE 7: WRITE REPORT

Write analysis to docs/experiment-tracking-analysis.md (create docs/ if needed).

Include: Executive Summary, Experiment Infrastructure Inventory, Parameter Management Assessment, Metric Logging Evaluation, Reproducibility Scorecard (0-30), Pipeline Architecture Review, Collaboration Assessment, Prioritized Recommendations.

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing output, validate data quality and completeness:

1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs), note data gaps and attempt alternative discovery methods.

IF VALIDATION FAILS:

• Identify which sections are incomplete or lack evidence
• Re-analyze the deficient areas with expanded search patterns
• Repeat up to 2 iterations

IF STILL INCOMPLETE after 2 iterations:

• Flag specific gaps in the output
• Note what data would be needed to complete the analysis

============================================================ OUTPUT

Experiment Tracking Analysis Complete

• Report: docs/experiment-tracking-analysis.md
• Reproducibility score: [X]/30
• Tracking tools identified: [list]
• Experiments cataloged: [count]
• Reproducibility gaps: [count]

Summary Table

Area	Status	Priority
Parameter Management	[PASS/WARN/FAIL]	[P1-P4]
Metric Logging	[PASS/WARN/FAIL]	[P1-P4]
Data Versioning	[PASS/WARN/FAIL]	[P1-P4]
Code-Data Linkage	[PASS/WARN/FAIL]	[P1-P4]
Environment Capture	[PASS/WARN/FAIL]	[P1-P4]
Pipeline Architecture	[PASS/WARN/FAIL]	[P1-P4]
Model Registry	[PASS/WARN/FAIL]	[P1-P4]
Collaboration	[PASS/WARN/FAIL]	[P1-P4]

NEXT STEPS:

• "Run /research-data-management to assess FAIR data principles for research outputs."
• "Run /lab-automation to evaluate instrument-to-experiment data pipelines."
• "Run /codebase-health to review code quality across the ML codebase."

DO NOT:

• Do NOT modify any experiment configurations, model artifacts, or tracking databases.
• Do NOT execute any training runs or trigger pipeline executions.
• Do NOT delete or archive any experiment records or artifacts.
• Do NOT assume reproducibility without verifying seed management and environment pinning.
• Do NOT skip governance assessment even for small research teams.

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

• Look for the project path in ~/.claude/projects/
• If found, append to skill-telemetry.md in that memory directory

Entry format:

### /experiment-tracking — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.