القائمة
Use when "experiment tracking", "MLflow", "Weights & Biases", "wandb", "model registry", "hyperparameter logging", "ML experiments", "training metrics"
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | experiment-tracking |
| description | Use when "experiment tracking", "MLflow", "Weights & Biases", "wandb", "model registry", "hyperparameter logging", "ML experiments", "training metrics" |
| version | 1.0.0 |
Track ML experiments, metrics, and models.
| Platform | Best For | Self-hosted | Visualization |
|---|---|---|---|
| MLflow | Open-source, model registry | Yes | Basic |
| W&B | Collaboration, sweeps | Limited | Excellent |
| Neptune | Team collaboration | No | Good |
| ClearML | Full MLOps | Yes | Good |
Open-source platform from Databricks.
Core components:
Strengths: Self-hosted, open-source, model registry, framework integrations Limitations: Basic visualization, less collaborative features
Key concept: Autologging for major frameworks - automatic metric capture with one line.
Cloud-first experiment tracking with excellent visualization.
Core features:
Strengths: Beautiful visualizations, team collaboration, hyperparameter sweeps Limitations: Cloud-dependent, limited self-hosting
Key concept: wandb.init() + wandb.log() - simple API, powerful features.
| Category | Examples |
|---|---|
| Hyperparameters | Learning rate, batch size, architecture |
| Metrics | Loss, accuracy, F1, per-epoch values |
| Artifacts | Model checkpoints, configs, datasets |
| System | GPU usage, memory, runtime |
| Code | Git commit, diff, requirements |
| Stage | Purpose |
|---|---|
| None | Just logged, not registered |
| Staging | Testing, validation |
| Production | Serving live traffic |
| Archived | Deprecated, kept for reference |
| Scenario | Recommendation |
|---|---|
| Self-hosted requirement | MLflow |
| Team collaboration | W&B |
| Model registry focus | MLflow |
| Hyperparameter sweeps | W&B |
| Beautiful dashboards | W&B |
| Full MLOps pipeline | MLflow + deployment tools |
Use when evaluating agent performance, building test frameworks, measuring quality, or asking about "agent evaluation", "LLM-as-judge", "agent testing", "quality metrics", "evaluation rubrics", "agent benchmarks"
Use when "codex", "use gpt", "gpt-5", "openai codex", "let openai", "full-auto", "autonomous code generation"
Use when compressing agent context, implementing conversation summarization, reducing token usage in long sessions, or asking about "context compression", "conversation history", "token optimization", "context limits", "summarization strategies"
Use when diagnosing agent failures, debugging lost-in-middle issues, understanding context poisoning, or asking about "context degradation", "lost in middle", "context poisoning", "attention patterns", "context clash", "agent performance drops"
Use when optimizing agent context, reducing token costs, implementing KV-cache optimization, or asking about "context optimization", "token reduction", "context limits", "observation masking", "context budgeting", "context partitioning"
Use when "CrewAI", "multi-agent systems", "agent orchestration", "AI crews", or asking about "autonomous agents", "agent collaboration", "role-based agents", "agent workflows", "AI team coordination"
