How do I install the Hugging Face Trackio skill?

Run npx skillmds@latest add patchy631/hugging-face-trackio-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Hugging Face Trackio safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Hugging Face Trackio?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Hugging Face Trackio free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Hugging Face Trackio?

patchy631 (@patchy631) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Hugging Face Trackio by patchy631 — Agent Skill

Name: Hugging Face Trackio
Rating: 4.2 (5 reviews)
Author: patchy631

Hugging Face Trackio

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

patchy631 Updated Sep 9, 2026

File contents

Trackio - Experiment Tracking for ML Training

Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards.

Two Interfaces

Task	Interface	Reference
Logging metrics during training	Python API	references/logging_metrics.md
Retrieving metrics after/during training	CLI	references/retrieving_metrics.md

When to Use Each

Python API → Logging

Use import trackio in your training scripts to log metrics:

Initialize tracking with trackio.init()
Log metrics with trackio.log() or use TRL's report_to="trackio"
Finalize with trackio.finish()

Key concept: For remote/cloud training, pass space_id — metrics sync to a Space dashboard so they persist after the instance terminates.

→ See references/logging_metrics.md for setup, TRL integration, and configuration options.

CLI → Retrieving

Use the trackio command to query logged metrics:

trackio list projects/runs/metrics — discover what's available
trackio get project/run/metric — retrieve summaries and values
trackio show — launch the dashboard
trackio sync — sync to HF Space

Key concept: Add --json for programmatic output suitable for automation and LLM agents.

→ See references/retrieving_metrics.md for all commands, workflows, and JSON output formats.

Minimal Logging Setup

import trackio

trackio.init(project="my-project", space_id="username/trackio")
trackio.log({"loss": 0.1, "accuracy": 0.9})
trackio.log({"loss": 0.09, "accuracy": 0.91})
trackio.finish()

Minimal Retrieval

trackio list projects --json
trackio get metric --project my-project --run my-run --metric loss --json

Source: patchy631/ai-engineering-hub → hugging-face-skills/skills/hugging-face-trackio/SKILL.md

---name: hugging-face-trackio-2description: Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.--- # Trackio - Experiment Tracking for ML Training Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards. ## Two Interfaces | Task | Interface | Reference | |------|-----------|-----------| | **Logging metrics** during training | Python API | [references/logging_metrics.md](references/logging_metrics.md) | | **Retrieving metrics** after/during training | CLI | [references/retrieving_metrics.md](references/retrieving_metrics.md) | ## When to Use Each ### Python API → Logging Use `import trackio` in your training scripts to log metrics: - Initialize tracking with `trackio.init()` - Log metrics with `trackio.log()` or use TRL's `report_to="trackio"` - Finalize with `trackio.finish()` **Key concept**: For remote/cloud training, pass `space_id` — metrics sync to a Space dashboard so they persist after the instance terminates. → See [references/logging_metrics.md](references/logging_metrics.md) for setup, TRL integration, and configuration options. ### CLI → Retrieving Use the `trackio` command to query logged metrics: - `trackio list projects/runs/metrics` — discover what's available - `trackio get project/run/metric` — retrieve summaries and values - `trackio show` — launch the dashboard - `trackio sync` — sync to HF Space **Key concept**: Add `--json` for programmatic output suitable for automation and LLM agents. → See [references/retrieving_metrics.md](references/retrieving_metrics.md) for all commands, workflows, and JSON output formats. ## Minimal Logging Setup ```python import trackio trackio.init(project="my-project", space_id="username/trackio") trackio.log({"loss": 0.1, "accuracy": 0.9}) trackio.log({"loss": 0.09, "accuracy": 0.91}) trackio.finish() ``` ### Minimal Retrieval ```bash trackio list projects --json trackio get metric --project my-project --run my-run --metric loss --json ``` --- **Source:** [`patchy631/ai-engineering-hub`](https://github.com/patchy631/ai-engineering-hub) → `hugging-face-skills/skills/hugging-face-trackio/SKILL.md`

Frequently asked questions