huggingface-trackio

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

Quality

100%

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Securityby

Passed

No findings from the security scan

Quality

Content

100%Weight 40%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Well-structured skill body: an overview table routes each task to a real, one-level-deep reference, code is executable across the Python API and CLI, and the autonomous workflow is a numbered sequence with a genuine feedback loop. It avoids generic filler and stays tight.

Suggestions

Minor conciseness gain: the print-statement analogy and the opening definition sentence could be trimmed slightly, though both currently earn their place.

Dimension	Reasoning	Score
Conciseness	Lean and action-oriented with no padding about concepts Claude already knows; prose (key concepts, the agent-workflow note) is purposeful and non-generic. A few framing analogies could be trimmed but every section earns its place.	3 / 3
Actionability	Provides fully executable, copy-paste-ready code (trackio.init/log/finish, the alert loop with AlertLevel, the polling CLI commands) and concrete commands across both Python API and CLI.	3 / 3
Workflow Clarity	The autonomous workflow is a clearly numbered 5-step sequence with an explicit feedback loop in step 5 ('based on alerts and metrics, stop the run, adjust hyperparameters, and launch a new run'). The operation is monitoring/iteration rather than destructive, so a hard validation checkpoint is not required.	3 / 3
Progressive Disclosure	SKILL.md is a concise overview with a task→interface→reference table, and three well-signaled one-level-deep references (logging_metrics.md, alerts.md, retrieving_metrics.md) that all exist as real files. Content is appropriately split with clear navigation.	3 / 3
	Total	12 / 12 Passed

Description

100%Weight 40%Scale 1-3

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A high-quality description that concisely states what the skill does and gives an explicit 'Use when' trigger covering all three interfaces. Third-person voice and concrete actions make it distinctive and unambiguous.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Track and visualize ML training experiments', 'logging metrics during training', 'firing alerts for training diagnostics', 'retrieving/analyzing logged metrics', plus 'real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output'.	3 / 3
Completeness	Explicitly states what ('Track and visualize ML training experiments with Trackio') and when ('Use when logging metrics during training... firing alerts... or retrieving/analyzing logged metrics'), with a clear 'Use when' trigger clause.	3 / 3
Trigger Term Quality	Covers natural terms a user would say — 'ML training experiments', 'logging metrics during training', 'training diagnostics', 'alerts', 'retrieving/analyzing logged metrics' — with good variation across logging, alerting, and retrieval.	3 / 3
Distinctiveness Conflict Risk	A clearly defined Trackio-specific niche with distinct triggers (logging/alerts/retrieving) that is unlikely to overlap with unrelated skills; uses correct third-person voice throughout.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: openai/plugins
Path: plugins/hugging-face/skills/trackio/SKILL.md
Commit: 11c74d6

Reviewed: 17 days ago

Table of Contents

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