Agent skill

Hugging Face Trackio

by sickn33 in sickn33/agentic-awesome-skills

Track and visualize ML training experiments with Trackio. An agent skill from sickn33/agentic-awesome-skills.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Hugging Face Trackio

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills hugging-face-trackio --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/hugging-face-trackio .claude/skills/hugging-face-trackio && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
hugging-face-trackio
GitHub stars
47k
Used in
2 other repos
Token cost
~1.4k tokens
SKILL.md length
463 words
Files
5 (incl. references)
Skills in repo
1,497
Repo updated
First seen
Licence
Apache-2.0

At a glance

Track and visualize ML training experiments with Trackio. An agent skill from sickn33/agentic-awesome-skills.

  • Works in 5 steps: Set up training with alerts — insert… → Launch training — run the script in the… → Poll for alerts — use trackio list… → …
  • Logging metrics during training (Python API)
  • SKILL.md covers Three Interfaces, When to Use Each, Minimal Logging Setup and Autonomous ML Experiment…, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Hugging Face Trackio is an agent skill from sickn33/agentic-awesome-skills. 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).

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `.claude-plugin/plugin.json`, `references/alerts.md` and `references/logging_metrics.md`).

It sits in AI & LLM Engineering, covering Model hubs and datasets. It works with Python and Hugging Face. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is Apache-2.0.

When your agent uses it

  • Logging metrics during training (Python API)
  • Firing alerts for training diagnostics
  • Retrieving/analyzing logged metrics (CLI)

Example prompts

  • “/hugging-face-trackio”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Set up training with alerts — insert trackio.alert() calls for diagnostic conditions
  2. Launch training — run the script in the background
  3. Poll for alerts — use trackio list alerts --project --json --since to check for new alerts
  4. Read metrics — use trackio get metric ... to inspect specific values
  5. Iterate — based on alerts and metrics, stop the run, adjust hyperparameters, and launch a new run

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hugging Face Trackio loads about 1.4k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 55 tokens; SKILL.md has 463 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit b84d35a, republished under its Apache-2.0 licence (© sickn33). 463 words, ~1,351 tokens.

Download SKILL.mdSave it as .claude/skills/hugging-face-trackio/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
hugging-face-trackio
description
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).
risk
critical
source
https://github.com/huggingface/skills/tree/main/skills/huggingface-trackio
source_repo
huggingface/skills
source_type
official
date_added
2026-07-01
license
Apache-2.0
license_source
https://github.com/huggingface/skills/blob/main/LICENSE

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.

Three Interfaces

TaskInterfaceReference
Logging metrics during trainingPython APIreferences/logging_metrics.md
Firing alerts for training diagnosticsPython APIreferences/alerts.md
Retrieving metrics & alerts after/during trainingCLIreferences/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.

Python API → Alerts

Insert trackio.alert() calls in training code to flag important events — like inserting print statements for debugging, but structured and queryable:

  • trackio.alert(title="...", level=trackio.AlertLevel.WARN) — fire an alert
  • Three severity levels: INFO, WARN, ERROR
  • Alerts are printed to terminal, stored in the database, shown in the dashboard, and optionally sent to webhooks (Slack/Discord)

Key concept for LLM agents: Alerts are the primary mechanism for autonomous experiment iteration. An agent should insert alerts into training code for diagnostic conditions (loss spikes, NaN gradients, low accuracy, training stalls). Since alerts are printed to the terminal, an agent that is watching the training script's output will see them automatically. For background or detached runs, the agent can poll via CLI instead.

→ See references/alerts.md for the full alerts API, webhook setup, and autonomous agent workflows.

Show full SKILL.md (215 more words)Show less
CLI → Retrieving

Use the trackio command to query logged metrics and alerts:

  • trackio list projects/runs/metrics — discover what's available
  • trackio get project/run/metric — retrieve summaries and values
  • trackio list alerts --project <name> --json — retrieve alerts
  • 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

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

Autonomous ML Experiment Workflow

When running experiments autonomously as an LLM agent, the recommended workflow is:

  1. Set up training with alerts — insert trackio.alert() calls for diagnostic conditions
  2. Launch training — run the script in the background
  3. Poll for alerts — use trackio list alerts --project <name> --json --since <timestamp> to check for new alerts
  4. Read metrics — use trackio get metric ... to inspect specific values
  5. Iterate — based on alerts and metrics, stop the run, adjust hyperparameters, and launch a new run
python
import trackio

trackio.init(project="my-project", config={"lr": 1e-4})

for step in range(num_steps):
    loss = train_step()
    trackio.log({"loss": loss, "step": step})

    if step > 100 and loss > 5.0:
        trackio.alert(
            title="Loss divergence",
            text=f"Loss {loss:.4f} still high after {step} steps",
            level=trackio.AlertLevel.ERROR,
        )
    if step > 0 and abs(loss) < 1e-8:
        trackio.alert(
            title="Vanishing loss",
            text="Loss near zero — possible gradient collapse",
            level=trackio.AlertLevel.WARN,
        )

trackio.finish()

Then poll from a separate terminal/process:

bash
trackio list alerts --project my-project --json --since "2025-01-01T00:00:00"

Limitations

  • Use this skill only when the task clearly matches its upstream product or API scope.
  • Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.
  • Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

© sickn33, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in skills/hugging-face-trackio of sickn33/agentic-awesome-skills.

  • SKILL.md
  • .claude-plugin/plugin.json
  • references/alerts.md
  • references/logging_metrics.md
  • references/retrieving_metrics.md

Open the folder on GitHubat commit b84d35a

Used in 2 other repositories

We found 11 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Hugging Face Trackio next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Hugging Face TokenizersOrchestra-Research/AI-Research-SKILLs13k6 repos~3.4kAutomated safety check: PassMIT
Hugging Face Vision Trainerhuggingface/skills11k1 repos~7.5kAutomated safety check: PassApache-2.0
Edge Bringupexeex/edge-cores110—~1.7kAutomated safety check: NotesApache-2.0
Publish Tracelab Huggingfaceuw-syfi/TraceLab142—~1.4kAutomated safety check: PassApache-2.0

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Questions about Hugging Face Trackio

What does Hugging Face Trackio do?

Track and visualize ML training experiments with Trackio. An agent skill from sickn33/agentic-awesome-skills. Hugging Face Trackio is an agent skill from sickn33/agentic-awesome-skills. Track and visualize ML training experiments with Trackio.

When should I use Hugging Face Trackio?

Hugging Face Trackio fits situations like: logging metrics during training (Python API); firing alerts for training diagnostics; retrieving/analyzing logged metrics (CLI).

How do I install Hugging Face Trackio in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a claude-code`. Or copy the skill folder (skills/hugging-face-trackio in sickn33/agentic-awesome-skills) into .claude/skills/hugging-face-trackio in your project. Claude Code loads it when a task matches its description.

How do I install Hugging Face Trackio in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a codex`. Or copy the skill folder (skills/hugging-face-trackio in sickn33/agentic-awesome-skills) into .agents/skills/hugging-face-trackio in your project. Codex loads it when a task matches its description.

Can I use Hugging Face Trackio in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-trackio -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hugging-face-trackio, .gemini/skills/hugging-face-trackio, .github/skills/hugging-face-trackio and .opencode/skills/hugging-face-trackio in your project.

What does Hugging Face Trackio need to run?

SKILL.md names no scripts, command-line tools or credentials: Hugging Face Trackio is instructions for the agent only. Our summary lists: Python 3.

Does Hugging Face Trackio access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Hugging Face Trackio safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Hugging Face Trackio use?

Hugging Face Trackio is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hugging Face Trackio use?

About 1.4k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.9k tokens, read only when the agent opens those files.

What are the alternatives to Hugging Face Trackio?

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Who maintains Hugging Face Trackio?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.